
==== Front
Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

71837
10.1038/s41598-024-71837-x
Article
GLNNMDA: a multimodal prediction model for microbe-drug associations based on global and local features
Kuang Haiyue
Liu Xin xin.liu@ccsu.edu.cn

Tan Huilin
Zhang Zhen
Zeng Bin
Wang Lei wanglei@xtu.edu.cn

https://ror.org/011d8sm39 grid.448798.e 0000 0004 1765 3577 Big Data Innovation and Entrepreneurship Education Center of Hunan Province, Changsha University, Changsha, 410022 China
6 9 2024
6 9 2024
2024
14 208475 2 2024
30 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Microbes have been demonstrated to be closely linked to diseases that pose a major threat to human health. Computing technologies can help researchers find potential microbe-drug associations more quickly and precisely. In this study, we introduced a novel computational prediction model called GLNNMDA based on global and local features of microbes and drugs to infer possible microbe-drug correlations. In GLNNMDA, we first constructed a heterogeneous network based on known microbe-drug relationships by integrating multiple similarity metrics of drugs and microbes. Subsequently, low-dimensional features will be extracted for nodes in the heterogeneous network by adopting the graph attention encoder. Next, based on combining these low-dimensional features with multiple properties of microbes and drugs to form a new comprehensive feature matrix, we would utilize the GLF module to extract the global and local features for microbes and drugs respectively, and then, we would further fuse these global and local features to come up with predictions of possible microbe-drug associations. Moreover, in order to evaluate the prediction performance of GLNNMDA, under the framework of fivefold cross-validation, intensive comparative experiments and case studies were done on different well-known public databases. The results showed that GLNNMDA obtained the highest AUC values as well as AUPR values of 0.9802 ± 0.0011, 0.9773 ± 0.0021 and 0.8586 ± 0.0004, 0.8008 ± 0.0031 in the two databases, MDAD and aBiofilm, respectively, compared to the state-of-the-art competing prediction methods. In addition, case studies of well-known microorganisms and drugs have demonstrated the effectiveness of GLNNMDA in inferring potential microbial drug associations, which implies that GLNNMDA may be a useful tool for microbe-drug association prediction in the future. The source code is available at: “https://github.com/KuangHaiYue/GLNNMDA.git”.

Subject terms

Computational biology and bioinformatics
Microbiology
http://dx.doi.org/10.13039/501100001809 National Natural Science Foundation of China 62272064 Wang Lei issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

Microorganisms are unicellular or multicellular organisms that include bacteria, viruses, archaea, protozoa, and fungi1. They are found in many different organs within the human body and serve a variety of functions, including protecting the body from pathogens, preserving internal environment homeostasis, controlling gastrointestinal pathology to increase metabolic capacity2, and playing a crucial and incredibly special role in the human body3. Moreover, disorders in microbial populations have been linked to a number of human illnesses, including diabetes4, rheumatoid arthritis5, and even cancer6. Given that bacterial vaginal diseases are linked to higher microbial diversity in the vagina and low microbial diversity in the body is linked to obesity and inflammatory diseases7,8, treating certain diseases can benefit from the restoration of lost beneficial functions and the eradication of harmful microbial activity. This may open the door to the creation of innovative treatments.

Microorganisms have become more resistant in recent years due to the rise in drug classes in medicine9, and the pharmaceutical industry used to grow microorganisms in greenhouses for use in pharmaceuticals10. However, these conventional clinical trials to ascertain the association between medications and bacteria are costly and time-consuming. It can sometimes take a new treatment up to a billion dollars and ten years to reach the market from laboratory research11,12. Because of their accuracy and simplicity, modern computational tools can quickly identify novel microbe-drug interactions and offer targets and research ideas for conventional clinical trials that seek to identify novel medications for the treatment of illnesses. Therefore, researchers need to quickly adapt new computational tools in order to find novel therapeutic alternatives for diseases as well as potential interactions between medications and microbes.

To far, numerous researchers have created a number of databases on microbe-drug associations, which enable them to determine possible connections between germs and medications. The MDAD database, for instance, was created by Sun et al.13 and has 5505 correlations between 180 microorganisms and 1388 medications. The biological, chemical, and structural characteristics of 5027 antimicrobial film agents are included in the aBiofilm database, which documents microbial resistance to medications. Rajput et al. provided a summary of this information14. Furthermore, the DrugVirus database was created by Andersen et al.15, which has 1281 relationships between 83 human viruses and 118 compounded medications. Numerous computational models have been proposed by researchers based on the aforementioned information to infer possible microbe-drug correlations in later studies.

For example, Zhu et al. combined the structural similarity of drugs with the GIP nuclear similarity of bacteria to create a computer model called HMDAKATZ, which was based on the KATZ measure16. Dong et al. combined a two-part network recommendation algorithm with the network embedding method of metapath2ve to create the HNERMDA computational model, which aims to predict possible associations between microorganisms and medications17. Based on the LapRLS algorithm, Zhu presented a novel computational technique called LRLSMDA18. It computes two objective functions by minimising the cost function and then utilizes the linear averaging approach to convert them into prediction matrices. Using a two-layer convolutional layer-based encoder to learn low-dimensional representations, Huang et al. proposed a Graph2MDA method based on variational graph self-encoder (VGAE)19. A deep neural network-based classifier is then designed to use the learned latent representations to infer potential connections. A microbe-drug two-part network, a microbe-drug heterogeneous network, a microbe-disease-drug heterogeneous network, and two attention mechanisms are all included in the integrated GAT framework, EGATMDA, that Long et al. suggested20. Tan et al. proposed a new predictive model called MDSVDNV to infer potential microbial-drug associations, using the Node2vec network embedding method and the singular value decomposition (SVD) matrix decomposition method to generate linear and nonlinear representations of microbial interactions21. Predicting relationships between biological elements is one of the main functions of bioinformatics. In addition to producing outstanding findings in the field of microbial-drug association prediction, researchers have created a plethora of fantastic techniques that can be utilized in other bioinformatics domains, such as circRNA-disease association forecasting and microbial-disease association prediction. For instance, in 2022, Chen et al. introduced MATHMDA22, a method that uses heterogeneous networks and metapath aggregate graph neural networks to predict the relationships between microbes and diseases. Peng et al. suggested a feature convolution learning with a heterogeneous graph attention network to predict circRNA illness connections. They developed a model named GATCL2CD23 to build a heterogeneous network by calculating the various similarities between circRNA and diseases. Furthermore, Peng et al. used a deep autoencoder to create a representation of each pair's attributes before utilizing a scalable tree-enhanced model to forecast the possible associations between each small molecule miRNA pair24. Ali et al. developed a model called HGTDR44 based on the heterogeneous graph converter of the knowledge graph, this model is used for drug repurposing as well as prediction of drug-protein and disease-protein interrelationships.Fatemeh et al. developed a model called CFSSynergy45, which goes through different architectures to get the features of the proteins and the drugs and then by combining multiple features and similarities for drug synergy prediction c.

Motivated by these aforementioned strategies, in this article, we designed a novel combinatorial neural network called GLNNMDA to infer possible microbial-drug relationships based on the combination of global and local features of microbes and drugs by integrating multiple recently popular deep learning methods. In GLNNMDA, we first created a heterogeneous network by combining the Gaussian kernel similarity and functional similarity of pharmaceutical and microorganism. Next, we inputted the heterogeneous network into the GAT (Graph attention network) to extract low-dimensional features for microorganisms and medications respectively. And then, through combining these newly-obtained low-dimensional features with known drug-disease associations, known microbe-disease associations and the cosine similarities of microbes and drugs, we formed a novel comprehensive microbe-drug feature matrix. Next, global and local features would be extracted for microbes and drugs separately by inputting the comprehensive microbe-drug feature matrix into a GLF module. Eventually, we would fuse these global and local features together to obtain the final prediction scores of microbes and drugs. Furthermore, to evaluate the prediction performance of GLNNMDA, we carried out comprehensive comparative experiments and case studies based on two well-known public databases, and demonstrated that GLNNMDA outperformed representative competing methods, and would be a useful tool for detecting potential microbe-drug associations in the future. By fusing data downloaded from multiple sources, GLNNMDA is able to more thoroughly capture the complex connections between microbes and drugs. By combining the acquired global and local features, the new GLF module in the model GLNNMDA can help better capture the overall patterns and details of the data. However, GLNNMDA also has a lot of disadvantages, for example, because it integrates multiple deep learning techniques, so the structure is a bit complex. In addition, since the model involves multiple processes such as feature extraction and fusion, more computer resources are required.

Materials and methods

Datasets

In this article, two well-known public databases, such as the MDAD and the aBiofilm, would be utilized to verify the prediction performance of GLNNMDA. Firstly, we downloaded 2740 known associations between 1373 drugs and 173 microorganisms from the MDAD. After eliminating those microbe-drug connections having not been verified in MDAD by Wang et al.25, we finally obtained 1121 known associations between 233 medications and 109 diseases, and 402 known associations between 73 microbes and 109 disorders from the MDAD. Next, we downloaded 2884 known associations between 1720 drugs and 140 microbes from the aBiofilm. And in a similar way, we eventually obtained 435 known connections between 103 medications and 72 diseases, and 254 known associations between 59 microorganisms and 72 diseases from the aBiofilm after excluding those microbe-drug relationships having not been validated in aBiolfilm by wang et al.25. Table 1 illustrated the details of all these newly-downloaded data.Table 1 Details of the newly-downloaded data.

Databases (MDAD/aBiofilm)	Microbes	Diseases	Drugs	Known associations	
Known microbe-drug associations	173/140	1373/1720	–	2470/2884	
Known drug-disease associations	–	233/103	109/72	1121/435	
Known microbe-disease associations	73/59	-	109/72	402/254	

Construction of GLNNMDA

As shown in Fig. 1, GLNNMDA consists of the following three major steps:Fig. 1 The flowchart of GLNNMDA.

Step 1: Constructing the heterogeneous network C by combining the Gaussian kernel similarity and functional similarity of microbes and drugs based on the newly-downloaded datasets.

Step 2: Extracting the low-dimensional feature matrix N for microbes and drugs respectively by inputting C into the graph attention encoder.

Step 3: Concatenating the low-dimensional feature matrix N, the adjacency matrix, and the cosine similarities of drugs and microorganisms to form a novel comprehensive microbe-drug feature matrix B, and feeding B into the GLF module to extract the local and global features of microbes and drugs separately, based on which, obtaining the predicted scores of potential microbe-drug associations in final.

Construction of the microbe-drug heterogeneous network C

Construction of the adjacency matrix A

Based on above newly-downloaded dataset S illustrated in Table 1, let nr and nm denote the number of microbes and drugs in S respectively, then we can create an adjacency matrix A∈Rnr∗nm as follows: for any give drug ri, microbe mj and disease dk in S, there is Ai,j=1, if and only if there is a known association between ri and mj in S, or there are both a known association between ri and dk and a known association between dk and mj in S, otherwise there is Ai,j=0.

Calculation of the Gaussian kernel similarities between microbes or drugs

Let Dri and Drj represent the i-th and the j-th rows in A respectively, then the Gaussian kernel similarity GRri,rj∈Rnr∗nr between any two given medicines ri and rj can be calculated in the following way:1 GRri,rj=exp-γ‖Dri-Drj‖2#

2 r=11nr∑i=1r‖Dri‖2#

Similarly, let Smi and Smj denote the i-th and the j-th columns in A separately, then the Gaussian kernel similarity of microorganisms GMmi,mj∈Rnm∗nm between any two given microorganisms mi and mj can be calculated in the following way:3 GMmi,mj=exp-γ‖Smi-Smj‖2#

4 γ=1/(1nm∑i=1nm‖Smi‖2)

Calculation of the functional similarities between microbes or drugs

In order to determine the functional similarity between medications or microbes, we need to determine the semantic similarity between disorders first. As suggested by Wang et al.25, for any given disease di, let Di denote the set that consists of di and all diseases related to di, then the semantic contribution value of each sub-item d in Di can be computed as follows:5 Kdid=1,d=dimaxd′∈childrenofdKdid′2,d∈Diandd≠di#

Besides, for any given disease dj other than di, the semantic similarity between di and dj can be calculated as follows:6 DEdi,dj=∑d∈D1∩D2Kdid+KdjdDK(di)+DK(dj))#

where DKdi=∑d∈DiKdi(d) indicates the sum of semantic contribution values of all sub-items in Di.

Thereafter, for any two given drug ri and rj, let di1 and dj2 denote the number of diseases related to ri and rj respectively, then the drug functional similarity FRri,rj∈Rnr∗nr can be calculated as follows:7 FRri,rj=1di1+dj2∑t=1i1max1≤s≤dj2DEdit,djs+∑s=1j2max1≤t≤di1DEdjt,dis#

For any two given microorganisms mi and mj, we let di3 and dj4 represent the number of diseases associated with mi and mj separately, then the microbial functional similarity FMmi,mj∈Rnm∗nm can be calculated as follows:8 FMmi,mj=1di3+dj4∑p=1i3max1≤k≤dj4DEdip,djk+∑k=1j4max1≤p≤di3DEdjk,dip#

Calculation of the integrated similarities between microbes or drugs

Based on above Eqs. (1), (3), (7) and (8), we can obtain the combinatorial similarities of microbes and diseases as follows:9 IRri,rj=GRri,rj+FRri,rj2,ifFRri,rj≠0GRri,rj,otherwise#

10 IMmi,mj=GMmi,mj+FMmi,mj2,ifFMmi,mj≠0GMmi,mj,otherwise#

In order to improve the reliability of feature expressions of microbes and drugs, we will further implement the random walk with restart (RWR) algorithm on above combinatorial similarities of microbes and diseases to obtain a unique drug similarity matrix Sr and a unique microbial similarity matrix Sm separately, where the algorithm of RWR is as follows:11 qil+1=λMqil+1-λβi#

12 βi,j=1ifi=j0otherwise#

where λ is the restart probability, M is the matrix of transition probabilities, βi∈R1∗m is the starting odds vector for node i , and qil is the likelihood of node i transferring to node l.

Based on these two newly-obtained matrices Sr and Sm, then we can construct a heterogeneous network C∈Rnr+nm∗nr+nm as follows:13 C=SrAATSm#

where AT represents the transpose matrix of A.

Extracting lower-dimensional features for nodes in C

GAT is a model that can reconstruct the node characteristics in a structural network through using several encoder and decoder layers with the same number of levels. This allows for the generation of new node representations by each encoder layer using the correlation between nodes and surrounding layers26 according to the following steps:

Step 1 (Encoder): for a given arbitrary node i, the correlation coefficient eij between i and one of its neighboring node j in the heterogeneous network C can be computed as follows:14 eij=LeakyReluαW∗Ci||W∗Cj,j∈φiv#

15 LeakyRelux=x,x>0μx,otherwise#

where, || denotes the concatenation operation, W is the trainable matrix, α is an operation for feature mapping, Ci is the i-th row of C, φiv is the set of nodes adjacent to node i, and μ is the hyper-parameter. Hence, based on above Eq. (14), the attention score ρij between i and j can be computed as follows:16 ρij=expeij∑k∈φivexpeik#

Thereafter, based on above Eq. (16), we can obtain the new feature representation for node i according to the following Eq. (17):17 Ci′=Relu∑j∈φivρijWCj#

18 Relux=x,x>00,otherwise#

Hence, based on above Eq. (17), we can construct a new matrix N as follows:19 N=MrMm∈Rnr+nm∗k1#

Step 2 (Decoder): in GLNNMDA, we employ the inner product as the decoder in the following way:20 NN=sigmoidN∙NT#

21 sigmoidx=11+e-x#

Step 3 (Optimization): in GLNNMDA, we adopt the the MSE function as the loss function of the GAT, and use the Adam optimizer to optimize the GAT, where the MSE function is defined as follows:22 Loss=1nr+nm∑i=1nr+nm‖NNi-Ci‖2#

The i-th row of NN and C is indicated by NNi and Ci, respectively.

GLF module

In recent years, the neural networks are becoming more and more popular in the field of feature extraction due to their advantages of simple operation and good results. However, the neural networks cannot generalize global and local features well, especially in scale data. Therefore, in GLNNMDA, we designed a module called GLF to extract global and local characteristics of microorganisms and drugs, respectively.

Extraction of the initial features for microbes and drugs

Based on the newly-downloaded dataset of known drug-disease connections, let the number of different diseases be nd in the dataset, then similarly to generation of the microbe-drug adjacency matrix A, it is easy to see that we can obtain a drug-disease adjacency matrix V∈Rnr∗nd. Thereafter, for any two given drug nodes i and j in V, we can compute the cosine similarity Srdisi,j between them as follows:23 Srdisi,j=cosVi,Vj=Vi·VjVi×Vj

where Vi and Vj represent the i-th and the j-th rows in V respectively.

Obviously, in a similar way, based on the newly-downloaded dataset of known microbe-disease connections, for any two given microbial nodes i and j, we can further compute the cosine similarity Smdisi,j between them.

Thereafter, based on above descriptions, we can obtain two feature matrices Bm and Br for microbes and drugs respectively in the following way:24 Br=Mr;Srdis;A#

25 Bm=Mm;Smdis;AT#

Based on above matrices Bm and Br, it is easy to know that we can fuse them together to form a combined microbe-drug characterization matrix B∈Rnr×nm∗2∗k2 as follows:26 B=BrBm#

Thereafter, we will feed the above matrix B into a convolutional neural network to begin the process of features extraction for microbes and drugs. It is important to note that in order to widen the edges, we will put up two convolutional layers and utilize zero padding. In the convolutional neural network, the BatchNorm2d layer and the Relu activation function are specifically contained in each convolutional layer. Here, the purpose of the BatchNorm2d layer is to normalize the data and enhance the capacity of the convolutional neural network for generalization.In the convolutional neural network, the convolutional operation in the i-th layer was defined as follows:27 Fi=ReluFi-1⊙Gi+bi#

where ⊙ represents the convolutional operation, Gi is the trainable matrix, and bi is the offset vector.

Based on above descriptions, it is easy to see that we can obtain an initial feature representation matrix B1∈Rnr×nm∗l∗2∗k2 of microbes and drugs after two convolutional layers.

Extraction of the global features for microbes and drugs

In this section, we will input B1 into the Generalized Mean Pooling (GeM) layer, and then go through the fully connected layer to reduce the feature dimensions to obtain the global feature representation fg for microbes and drugs, where the GeM layer is defined as follows:28 fg,c=12∗k2∑2,k2B1,c,2,k2pc=1,2,⋯.,l1p#

where p > 0 is the hyperparameter.

Extraction of the local features for microbes and drugs

As illustrated in Fig. 2, the local feature extraction process comprises two parts such as the multi-attribute part and the self-attention part, in which, three null convolutional neural networks are included in the multi-attribute part to generate feature mappings with various spatial acceptance fields. After concatenating these newly-acquired features, a 1 × 1 convolutional layer is designed to further process these data. Next, the self-attentive part will be designed to receive these output feature mappings to further model the significance of each local feature point. To be more precisely, the input B1 will be first processed through the BatchNorm2d and the 1 × 1 convolution layers, and then, the attention maps produced by the 1 × 1 convolution layer will be modulated and normalized, and finally a SoftPlus operation will be performed. To obtain the local features fl, we will multiply the newly-acquired attention map with the feature mapping generated previously. The softplus operation is defined as follows:Fig. 2 The local feature extraction process.

29 Softplusx=log1+ex#

Orthogonal fusion

Obviously, based on the newly-obtained global feature fg and local feature fl, the projection fl(i,j) of each local feature point fl.proj(i,j) on the global feature fg can be calculated as follows:30 fl,proji,j=fli,j·fgfg2fg#

Here fl(i,j)·fg is the dot product operation, and fg2 is the L2 paradigm of fg, which are defined as follows:31 fli,j·fg=∑C=1Cfl,ci,jfg,c#

32 fg2=∑c=1cfg,c2#

Thereafter, as shown in Fig. 3, we can obtain the components orthogonal to fg according to the following way:Fig. 3 Schematic of orthogonal complementary vectors.

33 fl,orthi,j=fli,j-fl,proji,j#

Next, these orthogonal components will be aggregated into a completely new vector and passed through a fully-connected layer, based on which, the global and local features will be fused into a new feature matrix K. And then, by flattening K and passing it through a fully-connected layer and a softmax function, we will finally obtain the predicted scorei,j∈Rnr∗nm for potential microbe-drug associations.

Experimental results

In this section, we conducted fivefold cross-validation and used multiple evaluation metrics, including the true positive rate (TPR), the false positive rate (FPR), accuracy, and recall related to the ROC and PR curves, to evaluate the prediction performance of GLNNMDA, which defined as follows:34 TPR=TPTP+FN

35 FPR=FPTN+FP#

36 Precision=TPTN+FP#

37 Recall=TPTP+FN#

38 Accuracy=TP+TNTP+TN+FP+FN#

Here, TP and TN stand for the number of positive and negative samples that were correctly predicted, and FN and FP for the number of positive and negative samples that were wrongly detected, respectively.

Based on above evaluation metrics, we first examined the effect of pertinent model parameters on the prediction performance of GLNNMDA for model optimization. After that, we further evaluated the prediction performance of GLNNMDA against four cutting-edge rival prediction techniques under the framework of k-fold cross-validation. Finally, in order to verify the efficacy of GLNNMDA in real-world applications, we chose a few particular medications and microbes as case studies.

Hyperparameter sensitivity analysis

It is clear that there are various parameters in GLNNMDA, such as the learning rate l and the dropout rate l1 of the GAT, and the learning rate l2 of the GLF module, etc. In this section, we will utilize the AUC values as the performance measure and test each parameter separately on the database of MDAD under the framework of fivefold CVs. During experiments, we will set the range of the value of l to {0.0001, 0.001, 0.01, 0.05, 0.1} by taking into account the experience of previous studies43. From observing Fig. 4, it is easy to see that GLNNMDA can achieve the highest AUC value while l = 0.01. Similarly, we will set the range of the value of l1 to {0.2, 0.4, 0.5, 0.7}. From observing Fig. 5, it is obvious that GLNNMDA can achieve the highest AUC value while l1 = 0.5. Next, we will set the range of the value of l2 to {0.00001, 0.0001, 0.001, 0.01, 0.1}. From observing Fig. 6, it is easy to know that GLNNMDA can achieve the highest AUC value while l2 = 0.0001. Finally, based on the experience of current researchers, the value of the parameter p in the GeM pooling layer of the GLF module will be set to 327.Fig. 4 Effect of the learning rate l of the GAT on the performance of GLNNMDA.

Fig. 5 Effect of the dropout rate l1 of the GAT on the performance of GLNNMDA.

Fig. 6 Effect of the learning rate l2 of the GLF module on the performance of GLNNMDA.

Performance comparison with competitive methods

In this section, we will compare GLNNMDA with four state-of-the-art microbe-drug association prediction models, including MDASAE28, LAGCN29, NTSHMDA30 and GSAMDA31. To assess the prediction performance of these techniques, we adopt the fivefold cross validation (CV) framework proposed by Cai et al.43. And during experiments, we will randomly select 80% of downloaded known associations and unknown associations as the training set while keep the remaining 20% of known associations and unknown associations as the testing set. Moreover, we will use the AUC values, AUPR values and Accuracy rate as the performance metrics, and the five competing models will be run on two different databases such as the MDAD and the aBiofilm respectively. Among these competitive models, MDASAE is a microbe-drug association prediction technique that combines stacked self-encoder with a multi-head attention mechanism. NTSHMDA is a random walk with restart based model aiming to detect potential microbe-disease associations. GSAMDA is a prediction model based on the sparse self-encoder and the graph attention network to infer possible microbe-disease connections. Consequently, the AUC and AUPRC values achieved by GLNNMDA, MDASAE, LAGCN, NTSHMDA, and GSAMDA are listed in Table 2. From observing Table 2, it is easy to see that GLNNMDA can achieve the best AUC values and AUPRC values. Although GLNNMDA could not completely outperform all these competing methods in accuracy values, but it achieved accuracy values of 0.9837 and 0.9815 in the MDAD and aBiofilm databases, respectively, which certainly implies that GLNNMDA is an effective tool for predicting potential microbial associations with drugs. Particularly, in order to ensure the fairness of the experiments, it is worth noting that all models are run based on their original parameters.Table 2 Comparison between GLNNMDA and four state-of-the-art prediction models.

Methods	AUC	AUPR	Accuracy	
MDAD	aBiofilm	MDAD	aBiofilm	MDAD	aBiofilm	
MDASAE	0.9565 ± 0.0016	0.9613 ± 0.0007	0.3455 ± 0.0031	0.3585 ± 0.0013	0.9874	0.9751	
LAGCN	0.8601 ± 0.0070	0.8601 ± 0.0109	0.3635 ± 0.0051	0.3602 ± 0.0055	0.9413	0.9373	
NTSHMDA	0.8502 ± 0.0020	0.8502 ± 0.0022	0.1892 ± 0.0056	0.1892 ± 0.0078	0.9896	0.9882	
GSAMDA	0.9488 ± 0.0005	0.9353 ± 0.0120	0.4520 ± 0.0007	0.4672 ± 0.0051	0.9860	0.9794	
GLNNMDA	0.9802 ± 0.0011	0.9773 ± 0.0021	0.8586 ± 0.0004	0.8008 ± 0.0031	0.9837	0.9815	
Significant values are in bold.

In addition, we further present ROC and PR curves for all these competing models in Figs. 7, 8, 9, and 10 to more intuitively illustrate the benefits of GLNNMDA, from which, it is easy to see that GLNNMDA outperforms all these state-of-the-art models. And especially, from analyzing the Table 2 and Fig. 7 and Fig. 9, it is easy to know that GLNNMDA can achieve the best AUC value of 0.9802 and AUPR value of 0.8586 on the MDAD, respectively. From observing the Table 2 and Fig. 8 and Fig. 10, it can be seen that GLNNMDA can achieve the best AUC value of 0.9773 and AUPR value of 0.8008 on the aBiofilm, separately. The experimental results on both MDAD and MDAD clearly show that GLNNMDA is superior to all rival approaches and has the highest prediction performance.Fig. 7 ROC curves of five competitive methods on MDAD.

Fig. 8 ROC curves of five competitive methods on aBiofilm.

Fig. 9 PR curves of five competitive methods on MDAD.

Fig. 10 PR curves of five competitive methods on aBiofilm.

Moreover, through analyzing above experimental results, we found that the reason that why GLNNMDA significantly outperforms other methods in terms of precision-recall curves lies in the following three aspects: Firstly, GLNNMDA has a more efficient feature selection mechanism that can take advantage of key features in the data and avoid noise interference, which makes GLNNMDA better at predicting a small number of categories. Secondly, GLNNMDA contains some complex structures or mechanisms (for example, combinations of multiple neural networks, etc.) that allow it to better adapt to the distribution of data. Thirdly, GLNNMDA performs more detailed hyperparameter tuning during the model training process, making it obtain the best results on different performance indicators. And besides, we also found that the reason that why other methods fail to outperform stochastic prediction lies in the following aspects: Firstly, some models may be too simple to capture the more complex relationships in the data, resulting in a prediction performance comparable to that of stochastic prediction. Secondly, these methods may fail to handle imbalanced data effectively, resulting in models that incorrectly predict a small number of classes and fail to improve AUPR. Thirdly, these methods may be deficient in feature extraction, failing to adequately mine the potential information in the data. This leads to poor performance of the built models.

Case studies

In order to further verify the prediction performance of GLNNMDA, in this section, we will select two medications (including Ciprofloxacin and Moxifloxacin) and one microbe (Mycobacterium tuberculosis) as case studies in the following way: firstly, we will implement GLNNMDA on the MDAD database to obtain the top 20 predicted medications or microbes. And then, we will search the PubMed (https://pubmed.ncbi.nlm.nih.gov) for these pharmaceuticals or microorganisms to see if there are any reports about them. Among these selected microbes and drugs, Ciprofloxacin is widely utilized in clinical practice due to its good pharmacokinetic and antibacterial capabilities, as well as its low occurrence of side effects32. Besides, Ciprofloxacin is more effective against microbial communities when taken in combination with different antibiotic drugs, according to recent studies33. Ciprofloxacin has been shown to be beneficial in treating a range of systemic ailments, including both acute and chronic urinary tract infections34. In vitro and clinical settings, bacterial resistance to ciprofloxacin is uncommon. As a strong antibacterial agent, ciprofloxacin offers a useful substitute for parenterally administered broad-spectrum antibiotics35.

Moxifloxacin is a fluoroquinolone antimicrobial drug with strong antimicrobial activity against common respiratory pathogens such as Streptococcus pneumoniae, Haemophilus influenzae, Catamorhynchus as well as some Staphylococcus aureus36. Encapsulation of Moxifloxacin into liposomes (especially cationic vesicles) can enhance the antimicrobial properties37. However, Moxifloxacin induces the occurrence of DRESS syndrome with pulmonary involvement38. Spinal muscular atrophy (SMA) is a genetic disorder, and Moxifloxacin, initially used as an antibiotic, could be repositioned for SMA treatment39.

The reults of case studies of Ciprofloxacin and Moxifloxacin are illustrated in Tables 3 and 4 separately, from which, it is easy to see that there are 16 out of the top 20 predicted microbes having been demonstrated to be linked to Ciprofloxacin, while 17 out of the top 20 predicted microbes having been demonstrated to be linked to Moxifloxacin.Table 3 The top 20 predicted Ciprofloxacin-associated microbes.

Top	Drug	Microbe	Evidence	
1	Ciprofloxacin	Schistosoma	PMID:26109619	
2	Ciprofloxacin	Eikenella corrodens	PMID:11399011	
3	Ciprofloxacin	Edwardsiella tarda	PMID:34427511	
4	Ciprofloxacin	Streptococcus pyogenes	PMID:35294769	
5	Ciprofloxacin	Pseudomonas chlororaphis	Unconfirmed	
6	Ciprofloxacin	Enterobacter cloacae	PMID:30691920	
7	Ciprofloxacin	Clostridium leptum	Unconfirmed	
8	Ciprofloxacin	Vibrio campbellii	PMID:36773438	
9	Ciprofloxacin	Marinobacter hydrocarbonoclasticus	PMID:32506034	
10	Ciprofloxacin	Serratia liquefaciens	PMID:10,965,096	
11	Ciprofloxacin	Halomonas pacifica	Unconfirmed	
12	Ciprofloxacin	Dengue Virus Type 2	Unconfirmed	
13	Ciprofloxacin	Thermus thermophilus	PMID:32846159	
14	Ciprofloxacin	Proteus vulgaris	PMID:34638966	
15	Ciprofloxacin	Mycobacterium leprae	PMID:3549940	
16	Ciprofloxacin	Bacteroides uniformis	PMID:11408211	
17	Ciprofloxacin	Pseudomonas aeruginosa	PMID:30605076	
18	Ciprofloxacin	Bacillus anthracis	PMID:26094508	
19	Ciprofloxacin	Sphingomonas sp. Ibu-2	PMID:35698364	
20	Ciprofloxacin	Klebsiella pneumoniae	PMID:16081970	

Table 4 The top 20 predicted Moxifloxacin-associated microbes.

Top	Drug	Microbe	Evidence	
1	Moxifloxacin	Pantoea agglomerans	PMID:24475334	
2	Moxifloxacin	Obesumbacterium proteus	Unconfirmed	
3	Moxifloxacin	Streptococcus gordonii	PMID:29160117	
4	Moxifloxacin	Bacillus amyloliquefaciens	PMID:37265494	
5	Moxifloxacin	Pseudomonas libaniensis	Unconfirmed	
6	Moxifloxacin	Trueperella pyogenes	PMID:33552877	
7	Moxifloxacin	Proteus vulgaris	PMID:12482994	
8	Moxifloxacin	Hepatitis C virus	PMID:31112805	
9	Moxifloxacin	Streptococcus sobrinus	PMID:29160117	
10	Moxifloxacin	Kocuria rhizophila	PMID:37662443	
11	Moxifloxacin	Firmicutes	PMID:28981383	
12	Moxifloxacin	Staphylococcus epidermis	PMID:37610872	
13	Moxifloxacin	Neisseria gonorrhoeae	PMID:31643179	
14	Moxifloxacin	Candida parapsilosis	PMID:20,455,400	
15	Moxifloxacin	Dengue Virus Type 2	PMID:30496157	
16	Moxifloxacin	Shewanella oneidensis	Unconfirmed	
17	Moxifloxacin	Haemophilus influenzae	PMID:31643179	
18	Moxifloxacin	Streptococcus pyogenes	PMID:36853816	
19	Moxifloxacin	Schistosoma	PMID:9513158	
20	Moxifloxacin	Plasmodium falciparum	PMID:33485067	

As the most common infectious agent related cause of death, Mycobacterium tuberculosis is responsible for tuberculosis (TB), which killed 1.7 million people in 201640. Mycobacterium tuberculosis is acid-resistant41, and is an aerobic, slow-growing bacteria that requires two to seven hours in direct sunshine to destroy in sputum42. Table 5 shows that 17 out of the top 20 predicted medications have been demonstrated by literature to be linked to Mycobacterium tuberculosis.Table 5 Top 20 predicted Mycobacterium tuberculosis-associated drugs.

Top	Microbe	Drug	Evidence	
1	Mycobacterium tuberculosis	Dicloxacillin	PMID:31914982	
2	Mycobacterium tuberculosis	Cidofovir	PMID:8841740	
3	Mycobacterium tuberculosis	S-carboxylmethyl-L-cysteine	PMID:31350832	
4	Mycobacterium tuberculosis	alpha-Defensin-3	PMID:36566255	
5	Mycobacterium tuberculosis	faropenem medoxomil	PMID:27742641	
6	Mycobacterium tuberculosis	para-Benzoquinone	PMID:36952383	
7	Mycobacterium tuberculosis	D/L-Aspartate	PMID:35132949	
8	Mycobacterium tuberculosis	Cefamandole	PMID:20961112	
9	Mycobacterium tuberculosis	(1-(4-chlorophenoxy)-3-[(4, 6-dimethyl-2-pyrimidinyl) thio]-2- propanol)	Unconfirmed	
10	Mycobacterium tuberculosis	2-(4-Fluorophenyl)-N-((2R,3R,4S,5S,6R)-2,4,5-trihydroxy-6-(hydroxymethyl)tetrahydro-2H-pyran-3-yl)acetamide	Unconfirmed	
11	Mycobacterium tuberculosis	(10R,11R)-Hydnocarpin	PMID:26273725	
12	Mycobacterium tuberculosis	Citropin 1.1	PMID:31336137	
13	Mycobacterium tuberculosis	Flucloxacillin	PMID:29217612	
14	Mycobacterium tuberculosis	para-ethoxyaniline	Unconfirmed	
15	Mycobacterium tuberculosis	(RW)4D	PMID:21622798	
16	Mycobacterium tuberculosis	4-Methoxychalcone	PMID:30223438	
17	Mycobacterium tuberculosis	Magainin-II	PMID:25355048	
18	Mycobacterium tuberculosis	Gemifloxacin	PMID:24211230	
19	Mycobacterium tuberculosis	5-octyl-1,3-thiazolidine-2,4-dione	PMID:31703818	
20	Mycobacterium tuberculosis	Hexyl gallate	PMID:18837315	

Discussion and conclusion

Researches indicate that humans and microorganisms share an intimate, mutually restrictive, and interdependent interaction. Rapid development of new computational techniques to find new microbe-drug relationships can make a huge contribution to human health and the medical industry. In this manuscript, we proposed a novel combinatorial neural network prediction model called GLNNMDA to infer potential microbe-drug associations. The biggest advantages of GLNNMDA lie in the following two aspects. Firstly, we constructed a heterogeneous network combining multiple similarity metrics of microorganisms and drugs and fed it to GAT to obtain low-dimensional feature matrices of microorganisms and drugs, which can more comprehensively capture the complex relationships between microorganisms and drugs. Secondly, we designed a GLF module to extract and fuse global and local features of microbes and drugs, which helps to better capture the overall trends and details of the data. Results of intensive comparative experiments and case studies demonstrated that GLNNMDA performed better than state-of-the-art computational methods, which indicated that GLNNMDA holds great promise for new drug discovery and clinical treatment as well as for identifying potential microbial-drug associations. Furthermore, the challenge of association prediction among other biological entities, like the prediction of the association between a microorganism and a disease, can be addressed with GLNNMDA as well. GLNNMDA offers certain benefits, but it also has certain limitation. For instance, there is insufficient evidence of an association between some of the microbes predicted by GLNNMDA and particular drug associations, therefore by depending only on pertinent data from today, it is practically hard to accurately depict the intricate interactions between bacteria and medications due to the limits of the dataset employed in this study. Furthermore, we have mainly adhered to the precise settings set by other researchers, such as p in the GeM pooling layer, and have not fine-tuned many of the hyperparameters. We will think about data improvement and in-depth study to adjust the model to deal with this problem in the future.

Supplementary Information

Supplementary Information 1.

Supplementary Information 2.

Supplementary Information 3.

Supplementary Information 4.

Supplementary Information 5.

Supplementary Information 6.

Supplementary Information 7.

Supplementary Information 8.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-71837-x.

Acknowledgements

The authors thank the referees for suggestions that helped improve the paper substantially

Author contributions

H.K., H.T. and L.W. produced the main ideas, and did the modeling, computation and analysis and also wrote the manuscript. X.L., Z.Z., B.Z. and L.W. provided supervision and effective scientific advice and related ideas, research design guidance, and added value to the article through editing and contributing completions. All authors contributed to the article and approved the submitted version.

Funding

This work was partly sponsored by the National Natural Science Foundation of China (No.62272064), and the Natural Science Foundation of Hunan Province (No.2023JJ60185).

Data availability

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.

Competing interests

The authors declare no competing interests.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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