
==== Front
Brief Bioinform
Brief Bioinform
bib
Briefings in Bioinformatics
1467-5463
1477-4054
Oxford University Press

10.1093/bib/bbae444
bbae444
Problem Solving Protocol
AcademicSubjects/SCI01060
Reinforcement learning-driven exploration of peptide space: accelerating generation of drug-like peptides
https://orcid.org/0009-0001-1310-4656
Wang Qian College of Computer Science and Technology, Ocean University of China, 238 Songling Rd, 266100 Shandong, China

Hu Xiaotong College of Computer Science and Technology, Ocean University of China, 238 Songling Rd, 266100 Shandong, China

Wei Zhiqiang College of Computer Science and Technology, Ocean University of China, 238 Songling Rd, 266100 Shandong, China

https://orcid.org/0009-0009-6553-6205
Lu Hao College of Computer Science and Technology, Ocean University of China, 238 Songling Rd, 266100 Shandong, China

Liu Hao College of Computer Science and Technology, Ocean University of China, 238 Songling Rd, 266100 Shandong, China

Corresponding author. E-mail: liu.hao@ouc.edu.cn
Equal contribution.

9 2024
10 9 2024
10 9 2024
25 5 bbae44417 3 2024
05 8 2024
27 8 2024
© The Author(s) 2024. Published by Oxford University Press.
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

Abstract

Using amino acid residues in peptide generation has solved several key problems, including precise control of amino acid sequence order, customized peptides for property modification, and large-scale peptide synthesis. Proteins contain unknown amino acid residues. Extracting them for the synthesis of drug-like peptides can create novel structures with unique properties, driving drug development. Computer-aided design of novel peptide drug molecules can solve the high-cost and low-efficiency problems in the traditional drug discovery process. Previous studies faced limitations in enhancing the bioactivity and drug-likeness of polypeptide drugs due to less emphasis on the connection relationships in amino acid structures. Thus, we proposed a reinforcement learning-driven generation model based on graph attention mechanisms for peptide generation. By harnessing the advantages of graph attention mechanisms, this model effectively captured the connectivity structures between amino acid residues in peptides. Simultaneously, leveraging reinforcement learning’s strength in guiding optimal sequence searches provided a novel approach to peptide design and optimization. This model introduces an actor-critic framework with real-time feedback loops to achieve dynamic balance between attributes, which can customize the generation of multiple peptides for specific targets and enhance the affinity between peptides and targets. Experimental results demonstrate that the generated drug-like peptides meet specified absorption, distribution, metabolism, excretion, and toxicity properties and bioactivity with a success rate of over 90\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\%$\end{document}, thereby significantly accelerating the process of drug-like peptide generation.

amino acid residues
graph attention mechanism
reinforcement learning
drug-like peptide generation
==== Body
pmcIntroduction

Over the past few decades, the role of peptides in various biological processes has become increasingly prominent, often acting as crucial signaling molecules and regulators within organisms. Peptide-based drugs exhibit unique properties. Due to their resemblance to proteins yet smaller size, peptides interact more precisely with specific receptors, significantly reducing potential side effects [1–3]. Traditional methods for synthesizing drug-like peptides, such as rational design and combinatorial chemistry, are time-consuming and laborious [4–7]. The development of computer-aided drug design techniques has provided more efficient means for peptide design [8–9]. However, existing peptide design methods still face significant challenges in terms of generation efficiency and quality. The fundamental difficulty comes from the need for systematic construction of correlation mechanism between complex structures, connection relationships in amino acid residues and a variety of drug-like properties.

The complex structure and connection relationships in amino acid residues determine the pharmacological properties and functions of peptide compounds. Therefore, the characterization of amino acid residues is a fundamental issue in peptide drugs computing. In terms of intuitiveness, the 3D structural representation of amino acid residues can provide spatial configuration information, aiding in the understanding of the spatial properties and interactions of peptide molecules. However, the accuracy of its predictive results may be affected by factors such as molecular conformational changes and computational resources. The most commonly used representation is the SMILES, which is simple and easy to store and transmit, but may suffer from ambiguity and partial loss of chemical information. Another representation is the molecular graph, which describes molecules using a graphical structure of nodes and edges, useful in analyzing the chemical structure and properties of molecules. The function of peptides is determined by the combination of amino acid residue connections. It is critical to accurately capture and express this connection structure in functional peptide design. Since the connection among multiple amino acid residues can be represented as a graph-structured data, graph attention network is particularly suitable for structural representation in peptide design as they effectively capture the information.

Regarding the peptide design methods, deep learning techniques have been widely applied in peptide design in recent years [10–15]. Several methods based on Recurrent Neural Network (RNN), Variational Autoencoder (VAE), and Generative Adversarial Network (GAN) have achieved the generation of novel peptide sequences with desired properties. RNN networks have been successfully applied to generate peptide sequences by learning patterns from known sequences [16–19]. Although RNN can output probability distributions and calculate hidden states for each time step by learning low-dimensional vector representations of peptide molecule character sequences, its training requires a large amount of data and may encounter problems of gradient disappearance or gradient explosion, making learning long-term dependencies difficult. In addition, RNN methods usually only consider random combinations of fragments or design from scratch, ignoring the advantages of the peptide molecular scaffold. VAE and GAN provide improved generation methods. VAE maps the input peptide molecules to a Gaussian distribution in the latent space through the encoder and decoder, and attempts to generate a latent variable distribution similar to the original peptide molecule data by minimizing the reconstruction loss and KL divergence loss [20–23]. However, VAE may produce peptide sequences with lower diversity and may suffer from ’ambiguity’ issues, where the generated samples lack fine-grained granularity. Meanwhile, GAN generates new peptide sequences with desired properties by learning the potential distribution of the training dataset, and optimizes the loss function through competition between the generator and discriminator, achieving mapping from random noise space to molecular space [24–26]. However, GAN may also face the problem of mode collapse, where the generator lacks diversity in generating peptide sequences and is difficult to train due to instability. Therefore, existing peptide design methods based on deep learning often struggle to accurately establish the mechanisms between complex structures, connection relationships in amino acid residues and a variety of drug-like properties. But, reinforcement learning, with its ability to flexibly adjust parameters, can adapt to multiple tasks in drug computation. It follows the Markov decision process, interacting with the chemical space to generate new molecular structures, and uses reward functions to integrate various drug-like properties to optimize the molecules generated. Consequently, in the peptide generation, reinforcement learning has shown impressive performance in exploring the chemical space of peptide generation, and representing the connections between edges using adjacency matrices also provides interpretability.

In the field of peptide generation, we selected the Actor-Critic (A2C) algorithm primarily for its exceptional policy gradient methods, which effectively learn and optimize policies in continuous action spaces. The Critic component of the A2C algorithm evaluates the current policy’s value and provides feedback to the Actor, facilitating rapid convergence to an optimal policy. This feature is particularly important in the context of peptide generation as it allows the model to efficiently explore and utilize interactions between amino acid residues, thus generating peptides with high biological activity. When generating peptide sequences, the vast chemical space resulting from the numerous possible amino acids presents a significant challenge. The A2C framework addresses this by evaluating each generated peptide through a reward mechanism, guiding further generation, which is a sustainable approach for targeted peptide drug research. Moreover, the A2C algorithm excels in multi-task learning. In our application, it is not only essential to generate effective peptide sequences but also to simultaneously optimize multiple objectives such as biological activity and ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties. The real-time feedback loop introduced by the A2C framework effectively balances these objectives, optimizing the overall learning process. With each iteration of the A2C framework, the Graph Attention Network (GAT) becomes more sensitive in capturing connectivity patterns, gradually reducing the chemical space. The resultant drugs are the product of thousands of iterations, enhancing the interpretability of the research.

Therefore, in view of the above technical challenges, we proposed a reinforcement learning generative model with a graph attention mechanism for peptides generation. By leveraging the advantages of the graph attention mechanism, it effectively captured the connectivity structure among amino acid residues in peptides. Furthermore, it harnessed the strengths of reinforcement learning in guiding the search for optimal sequences, providing an innovative approach for peptide design and optimization. The main contributions of our method can be summarized as follows: (1) The method employs a GAT to extract peptide features, making the modeling of peptide molecular structures more efficient. Simultaneously, it effectively captures the connectivity structures between amino acid residues, providing a more efficient analytical basis. (2) The method adopts the reinforcement learning framework based on A2C algorithm and facilitates the generation of novel drug-like peptides with desirable attributes by introducing ADMET properties and optimizing bioactivity within the reward function. (3) The experiments were set up across multiple dimensions and validated through cross-validation. The results demonstrate that the generated drug-like peptides meet specified ADMET properties and bioactivity, with a success rate exceeding 90\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\%$\end{document}, significantly expediting the process of drug-like peptide generation.

Materials and methods

This paper proposed a reinforcement learning-driven generation model for peptides based on a graph attention mechanism. Firstly, this model performed feature extraction using the graph attention mechanism, effectively capturing the connectivity between amino acid residues in peptides. Subsequently, the multi-dimensional feature vectors were input into the reinforcement learning generation framework based on the actor-critic, integrating bioactivity and ADMET properties optimization as reward mechanisms to guide the generation of optimal peptide sequences. The overall framework is illustrated in Figure 1.

Figure 1 Peptide generation framework based on reinforcement learning.

Feature extraction based on graph attention networks

The graph attention network consists of two parallel components: Graph Attention Mechanism (GAT) and Gated Recurrent Unit (GRU). GAT is responsible for constructing a graph with amino acid residues as nodes and their relationships as edges. Within this graph structure, GAT demonstrates excellent capabilities in extracting relationship features, successfully capturing the connectivity between amino acid residues in peptides as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $h^{\text{feat}}$\end{document}. On the other hand, GRU excels at extracting dependencies in time sequences, deriving multi-dimensional temporal features as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $h^{\text{time}}$\end{document}. Concatenating these obtained feature vectors results in a multidimensional feature vector encoding the amino acid residues.

We incorporated a custom feature encoding method to directly encode the physicochemical properties of amino acid residues into the node features of the GAT. This includes using parameters such as hydrophobicity indices, charge states, and molecular weights as input features, enabling the model to consider these crucial biochemical information when processing nodes. Furthermore, regarding the attention mechanism itself, we modified the calculation of attention scores to integrate these biochemical properties. Specifically, we employed a multi-head attention mechanism based on the prediction of interaction strength between residue pairs, with each head focusing on a particular type of physicochemical interaction (such as hydrophobic interactions, hydrogen bonding ability, etc.) and weighting the effects of these interactions. Additionally, we adjusted the network’s hyperparameters, such as the number of attention layers, the number of heads per layer and the learning rate, to optimize the model’s ability to learn complex interaction patterns in peptide structures. By fine-tuning these parameters, we improved the model’s adaptation to specific biochemical environments, thereby enhancing prediction accuracy and model interpretability.

The GAT model acquires information by weighting and aggregating features from neighboring nodes. Its attention mechanism considers both the features of neighboring nodes and the central node to calculate the importance of each neighboring node. The application of multiple attention heads enables GAT to capture various relationships between amino acid residues, while the stacking of multiple layers aids in learning more complex and hierarchical relationships among these residues. At the core of GAT lies the Graph Attention Layer (GAL), where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $h_{i} \in \mathbb{R}^{F}$\end{document} represents the feature vector for each node, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\mathbf{h} = \{h_{1}, h_{2}, \ldots , h_{N}\}$\end{document} signifies the input to GAL. Here, N denotes the number of nodes in the graph, while F represents the feature dimension of nodes, corresponding to the size of the time window. Based on the computation method of GAT, the attention score \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\alpha _{ij}$\end{document} between node i and node j can be expressed as

(1) \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} \begin{align*} & \alpha_{i j}=\operatorname{softmax}_{j}\left(e_{i j}\right)=\frac{\exp \left(e_{i j}\right)}{\sum_{k \in N_{i}} \exp \left(e_{i k}\right)} \end{align*}\end{document}

(2) \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} \begin{align*} & e_{i j}=\operatorname{LeakyReLU}\left(a^{T}\left[W h_{i} \| W h_{j}\right]\right) \end{align*}\end{document}

In this equation, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $W \in \mathbb{R}^{F^{\prime}}$\end{document} represents the shared feature transformation weight matrix for each layer, while || signifies the concatenation operation. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $a \in \mathbb{R}^{2F^{\prime}}$\end{document} denotes the weight parameters, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $e_{ij}$\end{document} represents the weight coefficient from neighboring node j to node i. Applying softmax normalization to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $e_{ij}$\end{document} yields the weight coefficient \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\alpha _{ij}$\end{document} between nodes i and j. Ultimately, through weighted summation, the representation vector for node i can be obtained as follows:

(3) \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} \begin{align*}& \left.h_{i}^{\prime}=\sigma\left(\sum_{j \in N_{i}} \alpha_{i j} W h_{j}\right]\right)\end{align*}\end{document}

Here, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\sigma $\end{document} represents the sigmoid activation function, while \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $N_{i}$\end{document} denotes the neighboring nodes of node i. The output of GAL is a linear transformation of all node feature vectors, represented as

(4) \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} \begin{align*}& h^{\text{feat}} =F C\left(\left[h_{1}^{\prime}\left\|h_{2}^{\prime}\right\| \cdots \| h_{N}^{\prime}\right]\right)\end{align*}\end{document}

Here, FC refers to the fully connected network, while \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $h^{\text{feat}} \in \mathbb{R}^{\text{hid}}$\end{document} represents the output vector from GAT.

The reinforcement learning generation framework based on actor-critic

The multi-dimensional feature vectors of amino acid residues, generated by the graph attention network, are fed into the reinforcement learning generation framework based on A2C. In this framework, the actor network continually updates its strategy using gradient-based optimization techniques to prioritize actions with higher value. Simultaneously, the reward mechanism integrates bioactivity and ADMET properties optimization, aiming to guide the generation of optimal peptide sequences.

Actor network update strategy

In our method, the actor network within the A2C architecture employs gradient-based optimization techniques to continually update policy parameters \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\theta $\end{document}. The primary objective of this process is to adjust the policy for prioritizing actions with higher value, essentially determining which amino acid residue segment to replace to maximize the expected long-term reward. The update formula for the actor network parameters is as follows:

(5) \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} \begin{align*}& \Delta \theta=\nabla_{\theta} \log (\pi(a \mid s ; \theta)) * A(a \mid s ; \theta, \varphi)\end{align*}\end{document}

Here, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\nabla _{\theta }\log (\pi (a\mid s;\theta ))$\end{document} represents the gradient of the logarithmic probability of the chosen action relative to the policy parameters \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\theta $\end{document}. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $A(a \mid s ; \theta , \varphi )$\end{document} signifies the value of the action, indicating how the policy parameters should be updated to enhance the long-term expected reward. In this process, the value function \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $A(a \mid s ; \theta , \varphi )$\end{document} plays a crucial role in capturing the relative value of taking action a in state s. It’s computed as the difference between the action-value function \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $Q(s, a ; \theta , \varphi )$\end{document} (estimating the long-term reward of taking action a in state s) and the value function \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $V(s ; \varphi )$\end{document}, expressed as follows:

(6) \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} \begin{align*}& A(a \mid s ; \theta, \varphi)=Q(s, a ; \theta, \varphi)-V(s ; \varphi)\end{align*}\end{document}

By iteratively updating the policy parameters through the above formula, the actor network gradually learns to formulate a strategy that considers long-term rewards associated with desired drug properties in peptide design, striking a balance between exploration and exploitation. Within this process, the critic network is crucial for estimating the expected long-term reward of the current state. The Temporal Difference (TD) error represents the difference between the estimated value function and the observed reward plus the discounted value of the next state. The calculation formula for the TD error is as follows:

(7) \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} \begin{align*}& \text{ TDerror} =R\left(s, a, s^{\prime}\right)+\gamma V\left(s^{\prime} ; \varphi\right)-V(s ; \varphi)\end{align*}\end{document}

Here, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $R\left (s, a, s^{\prime }\right )$\end{document} stands for the reward function, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\gamma $\end{document} represents the discount factor, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $V(s ; \varphi )$\end{document} is the value function of the current state s, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $V\left (s^{\prime } ; \varphi \right )$\end{document} denotes the value function of the next state \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $s^{\prime }$\end{document}.

Reward mechanism integrating ADMET properties and bioactivity

To ensure the high quality of peptide sequence generation, we have added ADMET properties evaluation and molecular docking to the reinforcement learning reward mechanism, the latter used to introduce bioactivity constraints. The ADMET assessment of peptide molecules involves computer-based predictions of physicochemical, pharmacokinetic, absorption, distribution, metabolism, excretion, and toxicity properties. These predictive methods rely on chemical similarity and bioactivity prediction models based on property-relevant targets. The ranges for all ADMET-related parameters were primarily established based on statistically distributed results of existing published drugs, further refined through self-training of the database.

Specifically, for the peptide design of specific target proteins, we can customize the reward function \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $R\left (s, a, s^{\prime }\right )$\end{document} to include ADMET properties as well as bioactivity, where the bioactivity is assessed through docking scores with the target protein. The reward function can be represented as

(8) \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} \begin{align*}& R(s, a, s^{\prime}) = w_{1} \text{ADMET}_{\text{score}}(s^{\prime}) + w_{2} \text{Docking}_{\text{score}}(s^{\prime}, \text{P})\end{align*}\end{document}

Here, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $W_{1}$\end{document} and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $W_{2}$\end{document} are weight factors, representing the importance of ADMET properties and docking scores, respectively. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\text{ADMET}_{\text{score}}(s^{\prime})$\end{document} quantifies the drug-likeness of the peptide sequence in the next state \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $s^{\prime }$\end{document}, while \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\text{Docking}_{\text{score}}(s^{\prime}, \text{P})$\end{document} measures the binding affinity of the peptide sequence in the next state \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $s^{\prime }$\end{document} with the target protein.

To evaluate the ADMET properties of compound molecules, we employed the bioinformatics tool ADMETLab 2.0 for prediction. Meanwhile, we extracted publicly available drug molecular data from the DrugBank database and calculated the key ADMET properties of these drug molecules using ADMETLab 2.0. Furthermore, we plotted density histograms based on the property values of these drug molecules and statistically determined the optimal range values for each property to assess the ADMET properties of our generated molecules. In terms of bioactivity prediction, we used the chemical informatics tool Ledock and assessed the activity of the generated compound molecules by considering binding energy and ligand efficiency. Finally, we incorporated the bioactivity threshold and the optimal range for each ADMET property obtained from statistical results into the reward mechanism to guide the optimization towards these optimal ranges during the molecular generation process, aiming to produce molecules with the best properties.

In the generation of successful lead molecules, bioactivity and ADMET properties each play an equally important role (50\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\%$\end{document} each). Based on this, we initially allocated weights to ADMET properties by incorporating recommendations from field experts and the ranking of these properties’ importance in drug development as reported in the literature. For instance, higher initial weights were assigned to critical properties such as absorption and toxicity, while lower weights were assigned to less critical properties. To ensure optimal model performance, we conducted multiple iterative experiments, comparing the model’s performance under various weight combinations. Ultimately, we established the optimal weight distribution as follows: absorption 25\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\%$\end{document}, distribution 20\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\%$\end{document}, metabolism 20\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\%$\end{document}, excretion 15\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\%$\end{document}, and toxicity 20\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\%$\end{document}. The key is to balance the various properties to ensure that the drug has good bioavailability, appropriate tissue distribution, reasonable metabolism and excretion pathways, and minimal toxicity.

By integrating ADMET properties and docking scores with target proteins in the reward function, the A2C reinforcement learning algorithm can simultaneously optimize both the drug-like properties and bioactivity in peptide design. This method effectively navigates in peptide sequence and conformational space by iteratively updating actor and critic networks, fully considering long-term rewards associated with ADMET properties and target binding affinity, thereby ensuring comprehensive optimization of drug-like properties.

Experimental methods and results analysis

Dataset and preprocessing

We assessed our model’s performance using two recognized benchmark datasets, MOSES [27] and GuacaMol [28]. MOSES consists of approximately 1.9 million biologically active and chemically synthesized small molecules carefully screened from the Zinc database. GuacaMol contains approximately 100 000 molecules screened from the ChEMBL database, all of which meet drug standards. These datasets offer a comprehensive evaluation, examining the diversity, novelty, and chemical validity of the generated molecules, as well as their application in specific drug design tasks. We trained our model with a selected set of 100 000 molecules. During the testing phase, we generated 10 000 molecules using the trained model to verify the model’s performance and the quality of the molecules generated.

Furthermore, to evaluate the bioactivity of peptide molecules generated by the method for a specific target, we selected TIM-3 as the research target and utilized the PeptideAtl as database [29], a comprehensive repository of peptide molecules. PeptideAtl as includes a wide range of known peptide structures from living organisms and scientific experiments, covering a variety of peptides from simple dipeptides to complex multi-peptides. This database offers fundamental molecular structural information alongside data on bioactivity, stability and solubility. After a series of preprocessing, a dataset comprising 800 000 accurately and comprehensively structured peptides was obtained. Due to the potential of TIM-3 as a therapeutic target in immune regulation, cancer treatment, relevance to various diseases and its prospects in drug development, we conducted peptide structure docking and dynamic simulation experiments targeting the significant TIM-3 using Schrödinger suite. Furthermore, we conducted peptide structure display and analysis. Specific details can be found in sections 3.5. In our study, we employed the Protein Preparation Wizard workflow in Maestro [30] for TIM-3 target, which involved the removal of water molecules, protonation of the protein, and optimization based on the OPLS 2005 force field to ensure accuracy and stability of the protein structure.

In this study, the training data we used not only includes traditional amino acids, but also specifically amino acid residues found in marine peptides. This type of residue is characterized by containing multiple amino or carboxyl groups. In fact, during the formation of peptide bonds, only one pair of amino and carboxyl groups participates in the dehydration reaction. To ensure the accuracy of the study, we implemented special measures to prevent non-target amino and carboxyl groups from participating in the reaction. The specific method includes using electron labeling techniques to mark the reactive groups within these residues, and subsequently rewriting the reaction equations to more accurately reflect the peptide bond formation process.

Evaluation metrics

We used a set of metrics from the MOSES and GuacaMol benchmarks to assess the quality and diversity of the generated chemical compounds.

Validity: the percentage of molecules meeting the criteria for chemical validity. A higher degree of validity indicates a more robust understanding of basic chemical rules by the model.

Novelty: this metric evaluates the model’s capacity for innovation by determining the proportion of valid molecules produced that are not found in the training dataset.

Uniqueness: the unique elements present in molecules served to effectively gauge the model’s understanding of the molecular distribution. Greater uniqueness implies the model’s capability to produce a more varied range of molecules.

Internal Diversity: the similarity among molecules in the generated molecular set. As shown in Eq. 9, T represents the Tanimoto similarity between molecules \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\mathrm{~m}_{1}$\end{document} and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\mathrm{~m}_{2}$\end{document} in the generated set G. A greater metric value suggests a wider variety in the collection of generated molecules.

(9) \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} \begin{align*}& \operatorname{IntDiv}_{p}(G)=1-\sqrt[p]{\frac{1}{|\mathrm{G}|^{2}} \sum_{\mathrm{m}_{1}, \mathrm{~m}_{2} \in G} T\left(\mathrm{~m}_{1}, \mathrm{~m}_{2}\right)^{p}}\end{align*}\end{document}

Scaffold similarity: cosine similarity of Bemis–Murcko scaffold frequencies between two sets of compounds. Expressing \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $c_{s}(A)$\end{document} as the frequency of scaffold s appearing in molecules from set A, and defining S as the set of fragments found in either G or R, the metric is defined as shown in Eq. 10:

(10) \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} \begin{align*}& \operatorname{Scaff}(G, R)=\frac{\sum_{s \in S}\left[c_{s}(G) \cdot c_{s}(R)\right]}{\sqrt{\sum_{s \in S} c_{s}^{2}(G)} \sqrt{\sum_{s \in S} c_{s}^{2}(R)}}\end{align*}\end{document}

Fréchet ChemNet distance (FCD): a metric that assesses the chemical and biological likeness of two compound groups. As shown in Eq. 11, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\mu _{G}$\end{document}, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\mu _{R}$\end{document} are mean vectors and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\Sigma _{G}$\end{document}, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\Sigma _{R}$\end{document} are full covariance matrices of activations for molecules from sets G and R, respectively.

(11) \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} \begin{align*}& \begin{gathered} \mathrm{FCD}(G, R)=\left\|\mu_{G}-\mu_{R}\right\|^{2}+\operatorname{Tr}\left(\Sigma_{G}+\Sigma_{R}\right.\\ \left.-2\left(\Sigma_{G} \Sigma_{R}\right)^{1 / 2}\right) \end{gathered}\end{align*}\end{document}

Experimental equipment

The model proposed in the manuscript was developed based on the PyTorch framework and implemented using the Python programming language. To optimize the running efficiency of the model, all code execution and modeling experiments were deployed on high-performance servers equipped in the laboratory. The hardware configuration of this server includes an AMD Ryzen 7 5800X 8-Core processor and 64 GiB of RAM. Additionally, to meet the high computational demands of model training, we also equipped the servers with an NVIDIA GeForce RTX 3090 graphics card.

ADMET properties parameter setting

In the reward mechanism of reinforcement learning, we incorporated the ADMET evaluation and bioactivity prediction for the generated molecules. The ADMET-related assessment of the generated molecules was achieved through the computational prediction of their absorption, distribution, metabolism, excretion, and toxicity characteristics. Specifically, we downloaded 2086 drug molecules that have been published and listed from the DrugBank database [31], and calculated 27 important ADMET property values for these drug molecules through the online website ADMETLab 2.0 platform [32]. Based on the density histograms of the drug molecule properties, we conducted statistics on the optimal interval values for these properties. Finally, the optimal intervals of each ADMET property obtained from the statistics were placed in the reward mechanism, guiding the generation of molecules towards these optimal intervals to generate the most optimal molecules. Among them, the density histogram of the ADMET property parameters of drug molecules can be found in the supporting materials, and the supporting materials include comprehensive explanations and case analyses of absorption, distribution, metabolism, excretion, and toxicity (ADMET) parameters to help understand how these parameters influence the model’s effectiveness. Additionally, the optimal range of ADMET property parameters calculated by computer statistics was shown in Table 1.

Table 1 The ADMET in silico properties with optimal parameters ranges

Properties	Parameters	Optimal values	Parameters	Optimal values	Parameters	Optimal values	
Absorption	Caco-2 Permeability	−5.3 to −4.5	MDCK Permeability	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $> 2 \times 10^{-6}$\end{document}	HIA	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0-0.03$\end{document}	
Distribution	Volume	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0-2$\end{document}	BBB Penetration	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0-0.2$\end{document}			
Metabolism	CYP 1A2 substrate	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0-1$\end{document}	CYP 2C9 substrate	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0-1$\end{document}	CYP 2C19 substrate	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0-1$\end{document}	
	CYP 2D6 substrate	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0-1$\end{document}	CYP 3A4 substrate	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0-1$\end{document}			
Excretion	CL	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0-10$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $T_{1/2}$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0-1$\end{document}			
Toxicities	Acute toxicity	0 or 1	Genotoxicity	0 or 1	hERG toxicity	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0-0.2$\end{document}	
	AMES Toxicity	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0-0.07$\end{document}	Carcinogencity	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0-0.3$\end{document}	Eye Corrosion	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0-0.03$\end{document}	
	Bioconcentration Factor	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0-1$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $IGC_{50}$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $2-5.5$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $LC_{50}FM$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $2-6.5$\end{document}	
	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $LC_{50}DM$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $3-7$\end{document}	NR-AR	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0-0.05$\end{document}	NR-AR-LBD	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0-0.05$\end{document}	
	NR-Aromatase	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0-0.2$\end{document}	NR-ER-LBD	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0-0.2$\end{document}	SR-ATAD5	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0-0.2$\end{document}	

Baseline methods comparison

To evaluate our method, we employed several different methods based on the MOSES and GuacaMol datasets except activity, including non-neural baseline methods such as NGram generation models and Hidden Markov Models (HMM), along with CharRNN [33], AAE [34], JTN-VAE [35], LatentGAN [36], Organ [37], VAE [34], and MoFlow [38].We carried out molecular generation using the MOSES and GuacaMol datasets. We sampled 10 000 molecules and assessed various metrics for the molecules generated.

Table 2 presented the performance comparison between the baseline and our model on the MOSES dataset. It highlighted that our model exceled in achieving novelty and diversity. However, the validity of our model slightly suffered due to the irregular probability distribution in fragment selection and substitution processes. On the other hand, JT-VAE attained a perfect validity score of 100\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\%$\end{document} by leveraging its tree decomposition approach and the use of predefined subgraphs, which circumvented the need to learn SMILES syntax. Nonetheless, JT-VAE’s performance on the other three metrics was less remarkable. Similarly, MoFlow also reached a 100\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\%$\end{document} validity score, attributed to its validity checking mechanisms, and matched 100\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\%$\end{document} in scaffold similarity. Yet, its internal diversity was compromised, indicating the model’s limited capability in effectively altering the scaffold. This makes it less ideal for scaffold transformations.

Table 2 The performances comparison of different models on the MOSES dataset

Models	Validity	Novelty	Int-div	Scaf-sim	
NGram	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0.238$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0.969$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0.864$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0.530$\end{document}	
HMM	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0.076$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0.999$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0.810$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0.206$\end{document}	
CharRNN	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0.975$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0.842$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0.856$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0.924$\end{document}	
AAE	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0.937$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0.793$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0.856$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0.902$\end{document}	
JTN-VAE	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\mathbf{1.0}$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0.915$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0.845$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0.892$\end{document}	
LatentGAN	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0.897$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0.949$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0.857$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0.886$\end{document}	
MoFlow	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\mathbf{1.0}$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0.996$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0.617$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\mathbf{1.0}$\end{document}	
Ours	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0.980$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\mathbf{1.0}$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\mathbf{0.902}$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0.917$\end{document}	

Table 3 showcased the efficacy of both the baseline and our model within the GuacaMol dataset. The SMILES LSTM, AAE, and VAE models demonstrated impressive scores in validity, uniqueness, and novelty, all exceeding 80\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\%$\end{document}. In contrast, the Organ model lagged substantially, with notably lower scores in validity (37.9\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\%$\end{document}), novelty (68.7\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\%$\end{document}), and an FCD score of 0, highlighting its challenges in adequately learning the training data’s distribution and considering molecular scaffold structures. The AAE model, while capturing training data features effectively, resulted in a clustering of similar molecules in the embedding space, thereby reducing the FCD scores. Conversely, our model stood out by excelling in all measured criteria, notably achieving perfect scores in uniqueness and novelty. This firmly establishes its superior overall performance compared to the others.

Table 3 The performances comparison of different models on the GuacaMol dataset

Models	Validity	Uniqueness	Novelty	FCD	
SMILES LSTM	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0.959$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\mathbf{1.0}$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0.912$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0.913$\end{document}	
Organ	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0.379$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0.841$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0.687$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0$\end{document}	
AAE	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0.822$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\mathbf{1.0}$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0.998$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0.529$\end{document}	
VAE	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0.870$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0.999$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0.974$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0.863$\end{document}	
Ours	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\mathbf{0.983}$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\mathbf{1.0}$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\mathbf{1.0}$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\mathbf{0.920}$\end{document}	

In addition, we conducted comparative analyses using three key molecular properties of drugs: logP, SA, and QED, while our model was named RLP.

In the process of drug design, logP value was a key indicator for evaluating the hydrophilicity and lipophilicity of drug molecules, which was crucial for predicting the absorption, distribution, metabolism, and excretion (ADME) properties. The core optimization mechanism of our RLP model focused on generating molecules with moderate logP values. These molecules can effectively penetrate biomembranes while maintaining sufficient water solubility for stable presence in the in vivo environment. The comparative experimental results in Figure 2 showed that the molecules generated by the RLP model exhibited a significant aggregation trend within the ideal range of logP. In addition, compared with other models, our model showed a narrower and sharper distribution in the peak region of logP, indicating its efficiency and specificity in generating drug-like peptide compounds.

Figure 2 The logP property distribution for different methods.

The synthetic accessibility (SA) index provided us with a quantifiable method to assess the difficulty of synthesizing compounds. A lower SA value indicated that the compound was easier to synthesize, particularly crucial in the early stages of drug development. The SA distribution map in Figure 3 showed that the molecules generated by the RLP model had generally lower SA values than those provided by other models on the MOSES platform. This suggested that not only did the RLP model theoretically generate molecules with potential bioactivity, but these molecules were also more feasible to synthesize in actual chemical processes. This result highlighted the practical potential of the RLP model. It can optimize molecules that meet drug design parameters. Additionally, these molecules can be effectively synthesized in the laboratory.

Figure 3 The SA property distribution for different methods.

Quantitative Estimation of Drug-likeness (QED) was a significant metric for assessing the overall drug-like properties of compounds. Figure 4 illustrated that the molecules exhibited by the RLP model were concentrated in the high-value region of the QED distribution graph, indicating that our model can generate compounds with higher overall drug-like properties.By observing the peak distribution, we can infer that the molecules generated by the RLP model were close to known drugs in multiple dimensions such as molecular weight, polarity, lipophilicity, and molecular geometry. This was particularly important as it directly affected the bioavailability and pharmacological activity of molecules. Compared to other models on the MOSES platform, the RLP model had significant advantages in generating molecules with high QED values, which further confirmed its effectiveness in drug candidate discovery.

Figure 4 The QED property distribution for different methods.

Target-specific peptide molecules generation

For evaluating the bioactivity of targeting specific targets, we selected TIM-3 as the research target, which was an immune-regulatory protein belonging to the immunoglobulin superfamily [39]. Due to the potential of TIM-3 as a therapeutic target in immune regulation, cancer treatment, and its relevance to various diseases, we conducted peptide structure docking and dynamic simulation experiments. These experiments targeted the significant TIM-3 using the Schrödinger suite, highlighting its prospects in drug development. Furthermore, we conducted peptide structure display and analysis.

Simulation experiments based on molecular dynamics

This study used the dynamics simulation module of Schrodinger software to conduct molecular dynamics simulations of the complex between the peptides generated in the last 10 epochs of the model and the TIM-3 protein target. The main purpose of the experiment was to verify the stability of the interaction between peptides and TIM-3 protein in a simulated biological environment, and to observe the changes in protein dynamic behavior. For this purpose, we used root mean square deviation (RMSD) and root mean square fluctuation (RMSF) for evaluation and analysis.

The RMSD results in Figure 5 indicated that most peptides maintained consistently low and stable values in the entire simulation process. This suggested that following binding with the TIM-3 target protein, the overall conformation of the peptides remained stable without significant structural alterations. This indicates a highly tight and stable binding with TIM-3. It was worth noting that during the simulation process of 1000 frames, even though some peptides showed fluctuations at the beginning, they gradually stabilized afterwards. This further confirmed that the binding between peptides and TIM-3 had a high stability after reaching equilibrium. Additionally, the small range of RMSD fluctuations reflected the specificity and tightness of the binding, indicating that peptides had promising potential for bioactivity.

Figure 5 Using RMSD to demonstrate the interaction stability between peptides and TIM-3 target.

The RMSF results in Figure 6 revealed localized flexibility changes in certain residues of the TIM-3 protein upon binding with the peptides. The data indicated that while there was a degree of fluctuation, the RMSF values for most residues remained at lower levels. This suggests that the binding of peptides to proteins did not induce significant structural changes in the protein. Furthermore, it validates the specificity and stability of peptide binding to TIM-3. Additionally, specific residues (such as residues 40–50 and 90–100) exhibited higher RMSF values, implying their potential key roles in regulating protein activity, where their flexibility upon binding to peptide might facilitate biological functionality.

Figure 6 Using RMSF to illustrate changes in protein dynamic behavior.

These peptides demonstrated significant adaptability and stability within the binding pocket of TIM-3, offering valuable dynamic structural insights for future drug design targeting the TIM-3 receptor. This held crucial significance for the development of anti-tumor therapies.

Comparison of different models based on Ledock

To assess the predictive capabilities of different models on the dataset, this study investigated three Ledock-based models: the traditional docking scoring function-MM/PBSA [40], a statistical learning model-Random Forest (RF) [41], and a deep learning model-3DCNN [42]. These models were employed to predict the affinity between generated peptides and target proteins, followed by an in-depth analysis of the experimental validation results.

The traditional docking scoring function-MM/PBSA 

Firstly, the compounds in the dataset were optimized and energy minimized. Next, a flexible docking strategy was adopted to allow these compounds to freely explore within the binding pocket of the target. This process utilizes the Ledock tool for molecular docking, which combined heuristic search methods such as simulated annealing and genetic algorithm to comprehensively explore molecular binding poses. To ensure result stability, each compound underwent 100 repeated dockings. After the docking was completed, Ledock was used to score each pose. Following this, further evaluation was conducted using the MM/PBSA method. This method collectively considered van der Waals forces, electrostatic interactions, polarization, and solvent effects to calculate the overall binding free energy. The results are shown in Table 4.

Table 4 Affinity comparison (kcal/mol) between MM/PBSA method and actual value

compounds	actual affinity	MM/PBSA	error	
Naphthalene	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $-8.5$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $-7.2$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $1.3$\end{document}	
Anthracene	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $-9.2$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $-8.0$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $1.2$\end{document}	
Toluene	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $-7.8$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $-6.4$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $1.4$\end{document}	
Phenanthrene	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $-10.1$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $-9.5$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0.6$\end{document}	
BenzoicAcid	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $-8.3$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $-7.0$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $1.3$\end{document}	
Acetanilide	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $-9.0$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $-8.2$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0.8$\end{document}	
Benzene	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $-7.9$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $-6.5$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $1.4$\end{document}	
Chlorobenzene	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $-8.6$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $-7.1$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $1.5$\end{document}	
Aniline	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $-9.5$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $-8.0$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $1.5$\end{document}	
Acetophenone	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $-8.7$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $-7.2$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $1.5$\end{document}	

Based on the evaluation using MM/PBSA, this method provided a reasonably predictive capability that aligns well with actual values. Specifically, the MM/PBSA method exhibited an \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\mathbb{R}^{2}$\end{document} value of 0.72 on the validation set, indicating its capacity to explain 72\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\%$\end{document} of the variability in the validation set. Simultaneously, the mean squared error (MSE) was 1.8, indicating an average prediction error of 1.8 kcal/mol.

The statistical learning model-RF 

Firstly, the data underwent standardization and normalization processes to ensure uniform scaling across various features. Given the molecular descriptors such as size, polarity, and hydrophobicity, these steps aided in enhancing prediction accuracy. Subsequently, an ensemble model, the RF, was employed to improve predictive accuracy and effectively avoid overfitting. This model primarily utilized Ledock docking scores as its input, combined with other descryptors. The model parameters were set at 100 trees and a maximum depth of 10, adjusted based on cross-validation results to optimize performance.

Table 5 showed the comparison between the RF method and the actual results, clearly demonstrating the excellent performance of the RF model. Its predictive performance was significantly better than the traditional MM/PBSA method. On the validation set, the model presented an \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\mathbb{R}^{2}$\end{document} value of 0.78, indicating that it can explain 78\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\%$\end{document} of the variability in the data. In addition, the mean square error (MSE) was 1.4 kcal/mol, which was lower than that of the MM/PBSA method. This highlights its superior predictive accuracy.

Table 5 Affinity comparison (kcal/mol) between RF method and actual value

compounds	actual affinity	MM/PBSA	error	
Naphthalene	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $-8.5$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $-8.2$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0.3$\end{document}	
Anthracene	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $-9.2$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $-9.0$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0.2$\end{document}	
Toluene	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $-7.8$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $-7.5$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0.3$\end{document}	
Phenanthrene	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $-10.1$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $-9.9$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0.2$\end{document}	
BenzoicAcid	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $-8.3$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $-8.1$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0.2$\end{document}	
Acetanilide	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $-9.0$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $-8.8$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0.2$\end{document}	
Benzene	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $-7.9$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $-7.6$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0.3$\end{document}	
Chlorobenzene	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $-8.6$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $-8.4$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0.2$\end{document}	
Aniline	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $-9.5$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $-9.2$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0.3$\end{document}	
Acetophenone	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $-8.7$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $-8.5$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0.2$\end{document}	

The deep learning model-3DCNN 

Firstly, the Ledock docking data was transformed into a 3D grid format, visually representing the molecular 3D structure and its spatial properties such as hydrophobicity and polarity. Each grid point contained numerical values representing specific attributes.

Subsequently, the deep learning model 3DCNN was used to train these data, which included three convolutional layers for capturing spatial features. Additionally, it included two fully connected layers for learning more advanced features and a output layer for the prediction combining affinity. During the training process, MSE was employed as the loss function, and the Adam optimizer was utilized to update parameters. Additionally, Dropout layers were added to prevent model overfitting.

Table 6 showed the comparison between the 3DCNN model and the actual results, clearly demonstrating the excellent predictive performance of the 3DCNN model. The \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\mathbb{R}^{2}$\end{document} value on the validation set was 0.83, indicating that the model can explain 83\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\%$\end{document} of the variability in the data. In addition, a MSE of only 1.2 kcal/mol indicated a close relationship between the model’s predicted results and actual values.

Table 6 Affinity comparison (kcal/mol) between 3DCNN method and actual value

compounds	actual affinity	MM/PBSA	error	
Naphthalene	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $-8.5$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $-7.7$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0.8$\end{document}	
Anthracene	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $-9.2$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $-8.6$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0.6$\end{document}	
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From the above results, it can be seen that the 3DCNN method used in this article had slightly better predictive performance than the other two methods. The physics-based scoring of MM/PBSA might not have fully captured all interactions, leading to larger predictive errors. While RF exhibited good generalization, it lacked a deep interpretation of interactions. In the context of peptide generation, 3DCNN models demonstrate specific advantages. Compared with traditional docking scoring functions (MM/PBSA) and RF models, 3DCNN can more effectively capture and utilize the spatial features in the three-dimensional structure of peptide molecules, thereby significantly improving the prediction accuracy of the model. Based on the performance of determination coefficient (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\mathbb{R}^{2}$\end{document}) and MSE indicators, the 3DCNN demonstrated the best predictive performance but required substantial computational resources and lacked interpretability. Overall, the 3DCNN showed the highest predictive accuracy, yet its complexity and computational demands might limit its widespread application.

Display and analysis of generated peptide structures

This study conducted a LigPrep docking experiment using Maestro (version 3.5) in the Schrodinger suite. Initially, the peptide structures generated from the last 10 epochs under-went chemical standardization. This included simulating ionization at pH range -2.0 to 7.0 to mimic the acidic and basic conditions in a biological system, alongside desalting and generating tautomers, which ensured peptide analysis representing their authentic states in the organism. Through these processes, specific chiralities of the peptides were retained, which was crucial for understanding their binding modes with the target. Subsequently, the peptides were optimized using the OPLS 2005 force field, accurately simulating their three-dimensional structures and electron distribution. This provided precise initial models for the subsequent docking experiments.

In Figure 7, we observed that peptides generated in the last 10 epochs exhibited a strong binding affinity with TIM-3, with binding energies consistently below -8 kcal/mol. The most representative peptide demonstrated a binding energy of -9.79 kcal/mol, suggesting a potentially robust interaction with TIM-3. This heightened affinity may stem from optimized arrangements of specific residues in the peptides and their unique spatial configurations, which can efficiently interact with TIM-3 binding sites. Further analysis revealed that the peptide with the lowest binding energy formed multiple hydrogen bonds and hydrophobic interactions within TIM-3’s binding pocket, which helped stabilize the binding between the peptide and the protein. Additionally, specific side chains of the peptide potentially engaged in additional van der Waals interactions or ionic bonds, further enhancing its binding with TIM-3.

Figure 7 The structure and affinity values of peptides generated in the last 10 epochs of the model.

In conclusion, these experimental outcomes demonstrated that through precise chemical standardization and optimization, we had successfully generated a series of drug-like peptides with high binding affinities. These peptides held significant potential value for future developments in anti-tumor therapy.

Ablation studies

To validate the impact of ADMET properties and bioactivity optimization on generating high-quality molecules, we conducted a series of ablation experiments and analyzed the validity, novelty, internal diversity, and scaffold similarity of the generated molecules. As shown in Table 7, the model incorporating ADMET properties and bioactivity optimization significantly improved validity, with the validity score increasing from a minimum of 0.872 to 0.985. This improvement was likely due to bioactivity ensuring that the molecules can effectively interact with the target, while ADMET properties ensured favorable absorption, distribution, metabolism, and excretion characteristics, as well as low toxicity in vivo. The combination of these factors enabled the generation of molecules with good in vitro and in vivo performance. Additionally, the scaffold similarity (from 0.86 and 0.873 to 0.930) was more ideal. This indicated that considering both ADMET properties and bioactivity optimization made the generated molecules more likely to have scaffolds known for good ADMET and bioactivity characteristics, thereby increasing scaffold similarity. In contrast, the inclusion of ADMET properties and bioactivity optimization in the model had no significant impact on internal diversity, but rather generally being slightly lower, which, in turn, led to a generally higher novelty. This might be because meeting ADMET properties and bioactivity requirements typically required molecules to possess specific chemical features and structures, resulting in a narrower chemical space and thus reduced internal structural diversity.

Table 7 The impact of ADMET properties and bioactivity optimization on the model performance

Bioactivity	ADMET	Validity	Novelty	Int-div	Scaf-sim	
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	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\checkmark $\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0.914$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0.999$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0.795$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0.876$\end{document}	
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\checkmark $\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\checkmark $\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\mathbf{0.985}$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\mathbf{1.0}$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\mathbf{0.836}$\end{document}	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\mathbf{0.930}$\end{document}	

Peptide synthesis

Utilizing computer-aided algorithms, we have ultimately selected 72 peptide molecules that meet ADMET properties and exhibit the highest bioactivity (see supporting materials for peptide molecule sequences). By analyzing the surface potential and druggable pockets of the TIM-3 target protein, we designed and synthesized a series of tetrapeptides and measured their binding affinity to the protein through wet lab experiments. Combining molecular docking and molecular dynamics simulations, we performed a comprehensive analysis of the tetrapeptide structures and experimental data. Based on the active groups, we conducted structural modifications by substituting amino acids at key positions with different electric properties and side chain lengths. Concurrently, we analyzed amino acids at other positions, designed and synthesized a second round of mutants. As shown in Figure 8, we preliminarily screened two tetrapeptide molecules with binding better to the target protein.

Figure 8 The two synthesized tetrapeptide molecules.

Conclusion

We proposed a reinforcement learning-driven generative model for peptide generation based on graph attention mechanisms. This model integrated the advantages of graph attention mechanisms, effectively capturing the connectivity structure between amino acid residues in peptides. And it utilized the advantages of reinforcement learning in guiding optimal sequence search, using bioactivity and ADMET properties optimization as reward mechanisms, providing a new method for peptide design and optimization. This study developed novel peptide drugs through computer-aided design, solving the problems of high cost and low efficiency in traditional drug discovery. Bioinformatics algorithms were used to improve the prediction accuracy and synthesis feasibility of drug design, aiming to provide more efficient and lower-cost alternative solutions for preclinical drug discovery. Our proposed approach not only significantly reduces the resource consumption and environmental impact of drug development by reducing the need for chemical synthesis and biological testing in the laboratory, but also accelerates the conversion rate of drugs from concept to market. In addition, the adoption of computational methods can increase the flexibility and iteration speed of experimental design, providing impetus for continuous improvement and innovation.

Despite achieving certain successes in this study, there remain limitations. The dataset might not fully cover the entire space of peptides, potentially impacting the model’s generalization ability. Additionally, the model could be influenced by biases in the training data, leading to potential prediction biases. Moreover, the A2C reinforcement learning framework is weak in interpretability and requires significant computational resources, which may limit its applicability. To overcome these limitations, future research might consider employing data augmentation techniques (such as rotations, translations, etc.) to enhance model robustness. Additionally, integrating 2D and 3D structural data with bioactivity information through multi-modal learning can enrich the model’s input. Exploring novel models like Transformers, diffusion models, etc., could better adapt to peptide characteristics. Applying interpretability techniques (such as SHAP, LIME) could aid in understanding the model’s decision-making process. These endeavors hold promise in paving new paths for the future advancement of drug discovery.

Key Points

The method employs a GAT to extract peptide features for effectively capturing the connectivity structures between amino acid residues.

The method adopts the reinforcement learning framework based on A2C algorithm and facilitates the generation of novel drug-like peptides with desirable attributes by introducing ADMET properties and optimizing bioactivity within the reward function.

We have conducted experiments on multiple dimensions, and compared with current mainstream models, our model has competitiveness in generating drug-like peptides that meet specified ADMET properties and bioactivity in terms of efficiency and quality.

Acknowledgements

All who have contributed explicitly in some way to the study have been included in the list of authors. We would like to thank Marine Big Data Center of Institute for Advanced Ocean Study of Ocean University of China for providing supercomputing cluster. This work was supported by the Shandong Provincial Natural Science Foundation of China \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $(ZR2023MF104)$\end{document}.

Supplementary Material

Supporting_materials_bbae444

 

Conflict of interest: None declared.

Data availability

The datasets involved in this method and the source code are freely available to academic users at https://github.com/p1acemker/MomdTDSRL.git.
==== Refs
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