
==== Front
World J Exp Med
WJEM
World Journal of Experimental Medicine
2220-315X
Baishideng Publishing Group Inc

jWJEM.v14.i3.eid96042
10.5493/wjem.v14.i3.96042
96042
Opinion Review
Artificial intelligence as a tool in drug discovery and development
Kokudeva M et al. AI drug discovery
Kokudeva Maria Department of Pharmacology and Toxicology, Faculty of Pharmacy, Medical University of Sofia, Sofia 1000, Bulgaria. kokudeva.mariya@gmail.com

Vichev Mincho Healthcare Solutions, Sofia 1404, Bulgaria

Naseva Emilia Faculty of Public Health, Medical University of Sofia, Sofia 1431, Bulgaria

Miteva Dimitrina Georgieva Department of Genetics, Faculty of Biology, Sofia University St. Kliment Ohridski, Sofia 1164, Bulgaria
Medical Faculty, Sofia University St. Kliment Ohridski, Sofia 1407, Bulgaria

Velikova Tsvetelina Medical Faculty, Sofia University St. Kliment Ohridski, Sofia 1407, Bulgaria

Co-first authors: Maria Kokudeva and Mincho Vichev.

Author contributions: Kokudeva M and Vichev M were involved equally in conceptualizing the idea and writing the draft; Miteva D, Naseva E and Velikova T wrote additional sections in the paper; Vichev M was responsible for the critical revision of the manuscript for relevant intellectual content; Velikova T was responsible for project administration and funding acquisition; All authors approved the final version of the paper prior to submission.

Supported by the European Union-NextGenerationEU, through the National Recovery and Resilience Plan of the Republic of Bulgaria, No. BG-RRP-2.004-0008.

Corresponding author: Maria Kokudeva, PharmD, PhD, Research Assistant, Department of Pharmacology and Toxicology, Faculty of Pharmacy, Medical University of Sofia, ul. Dunav 2, Sofia 1000, Bulgaria. kokudeva.mariya@gmail.com

20 9 2024
20 9 2024
14 3 9604225 4 2024
6 8 2024
12 8 2024
©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
2024
https://creativecommons.org/licenses/by-nc/4.0/ This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial.
The rapidly advancing field of artificial intelligence (AI) has garnered substantial attention for its potential application in drug discovery and development. This opinion review critically examined the feasibility and prospects of integrating AI as a transformative tool in the pharmaceutical industry. AI, encompassing machine learning algorithms, deep learning, and data analytics, offers unprecedented opportunities to streamline and enhance various stages of drug development. This opinion review delved into the current landscape of AI-driven approaches, discussing their utilization in target identification, lead optimization, and predictive modeling of pharmacokinetics and toxicity. We aimed to scrutinize the integration of large-scale omics data, electronic health records, and chemical informatics, highlighting the power of AI in uncovering novel therapeutic targets and accelerating drug repurposing strategies. Despite the considerable potential of AI, the review also addressed inherent challenges, including data privacy concerns, interpretability of AI models, and the need for robust validation in real-world clinical settings. Additionally, we explored ethical considerations surrounding AI-driven decision-making in drug development. This opinion review provided a nuanced perspective on the transformative role of AI in drug discovery by discussing the existing literature and emerging trends, presenting critical insights and addressing potential hurdles. In conclusion, this study aimed to stimulate discourse within the scientific community and guide future endeavors to harness the full potential of AI in drug development.

Artificial intelligence
Drug discovery
Drug development
Decision-making
AI-driven medicine
Healthcare
Public health
==== Body
pmc Core Tip: Embracing artificial intelligence (AI) expedites drug discovery and development by streamlining computational chemistry, molecular modeling, and data mining. Leveraging AI-driven algorithms enhances the accuracy and efficiency of identifying potential drug candidates and predicting their pharmacological properties. Integrating machine learning and deep learning frameworks into pharmaceutical research optimizes decision-making, accelerates drug design cycles, and ultimately advances novel therapies for various diseases.

INTRODUCTION

Drug discovery and development is a long, expensive, and complex process that can often take more than 10 years from molecule identification to medical drug approval and placement on the market. Each stage in the process carries a risk of failure, and most drug applicants never reach the market. This makes the process of drug innovation and development both expensive and inefficient[1,2].

In recent years, the use of artificial intelligence (AI) in this industry has increased significantly. Drug discovery requires the analysis of large databases of chemical compounds. This can be achieved rapidly using machine learning techniques[3]. These techniques have their limitations because even a little change in the molecular structure of the drug can drastically alter its effect. Drug discovery involves the analysis and comparison of the properties of different molecular structures and components. In this context, AI tools can automatically scan large datasets quickly, using a composition safety check to pick out the most effective model for a certain goal[4,5].

Several public libraries store chemical and biological data, including ChEMBL[6] and PubChem[7]. They contain information on millions of molecules for various disease targets. These libraries are machine-readable and are used for drug discovery models, including for drug candidate compounds targeting severe acute respiratory syndrome coronavirus 2[8]. AI (mostly machine learning techniques) has also been implemented to evaluate toxicity. For example, the DeepTox platform is used as a model to evaluate the toxicity of certain compounds[9]. Another platform, MoleculeNet, can be used to translate molecular structures and predict toxicity[10].

The assessment of drug-target interactions is another important stage of drug design. The binding affinity between the drug and its target is important for the final product. Molecular docking, one of the most common approaches to predict affinity, is used to study the binding and complex formation between two molecules, such as receptor-ligand interactions[11,12].

Different pharmaceutical companies have used AI to improve drug discovery. Verge Genomics uses AI to predict the effects of some new drugs on patients with Alzheimer’s disease and Parkinson’s disease[13]. Despite their use of automated data analysis, certain drug studies have failed. Most neurological diseases are polygenic, but the company drug data targets one gene. In 2018, Verge Genomics developed an algorithm to identify the pathogenic genes and select drugs to target them all. Thus, the company successfully utilized the vast potential of AI and machine learning algorithms by identifying drugs for neurodegenerative diseases. In 2018, Bayer and Merck received Food and Drug Administration approval to use AI algorithms to support clinical decision making for chronic thromboembolic pulmonary hypertension[14]. This form of chronic thromboembolic pulmonary hypertension is very rare and affects approximately 5/1000000 people annually worldwide. The symptoms resemble chronic obstructive pulmonary disease or asthma, complicating its diagnosis even after numerous medical tests.

Novartis currently uses AI algorithms to classify digital images of different cells[15,16]. Each cell is treated with different experimental molecules. The algorithms group and test molecules with similar effects. Finding biologically active molecules requires complicated analysis. Therefore, to speed up this screening process, Novartis research teams use machine learning algorithms to predict which unknown molecules might be worth exploring.

In 2018, the biotech company Cyclica collaborated with Bayer[17], using AI machine learning to determine the polypharmacological profiles of small molecules and develop more affordable drugs. The company created Ligand Express, an integrated network of cloud technologies expanded with AI that enhances drug design, screening, and personalization.

In line with those mentioned above, AI-driven drug design, development, and delivery are highly trendy topics to discuss. We hypothesize that integrating AI into drug discovery and development processes will significantly enhance efficiency, accuracy, and innovation, leading to the discovery of novel therapeutic agents and the optimization of existing drugs. Our goals for this review were: (1) To assess the current state of AI applications in drug discovery, including machine learning algorithms, predictive modeling, and virtual screening techniques; (2) To explore recent advancements in AI-driven platforms and technologies that have revolutionized drug discovery, such as deep learning algorithms, generative models, and molecular design tools; (3) To investigate how AI has impacted various stages of drug development, from target identification and lead optimization to clinical trials and post-marketing surveillance; (4) To showcase case studies and success stories where AI-driven approaches have led to the discovery of promising drug candidates or repurposing of existing drugs for new indications; (5) To identify key challenges and limitations associated with AI in drug discovery, such as data quality issues, ethical considerations, and regulatory hurdles; and (6) To provide insights into potential future directions and emerging trends in AI-enabled drug discovery, including the integration of multi-omics data, collaborative AI platforms, and AI-driven personalized medicine approaches. The current review is crucial as it addresses the intersection of two rapidly evolving fields: AI and drug discovery. Our review aimed to provide a comprehensive summary for AI experts, drug developers, and healthcare professionals by synthesizing the latest research, methodologies, and best practices. The insights gained from this review will inform academia and industry and contribute to the ongoing efforts to accelerate drug discovery, improve patient outcomes, and address unmet medical needs globally.

SEARCH STRATEGY

We conducted a comprehensive search across multiple databases, including PubMed, Scopus, Web of Science, IEEE Xplore, and Google Scholar. We focused on various types of studies, such as review articles, research articles, case studies, clinical trials, and meta-analyses. Using Boolean operators, our search terms included (“Artificial intelligence” OR “AI”) AND (“Drug discovery” OR “Drug development”) AND (“Machine learning” OR “Deep learning” OR “Neural networks”) AND (“Pharmaceuticals” OR “Medications” OR “Compounds”), (“Computational chemistry” OR “Chemoinformatics”) AND (“Drug design” OR “Molecular modeling”), and (“Data mining” OR “Big data analytics”) AND (“Pharmacology” OR “Therapeutics”). This strategy allowed us to gather relevant literature on the application of AI in various stages of drug discovery and development, including computational chemistry, molecular modeling, data mining, and pharmacological analysis. The paper selection is presented in Figure 1.

Figure 1 Identification, screening, and selection of papers to include.

CURRENT LANDSCAPE OF DRUG DISCOVERY

Traditional methods and limitations

Historically, drug discovery has relied heavily on trial-and-error methods, where researchers would test compounds to see if they had the desired therapeutic effect[18]. However, traditional methods are associated with high failure rates and lengthy timelines. It often takes years and significant resources to develop a single drug, with many candidates failing in clinical trials[19]. Moreover, traditional methods often lack the depth of understanding required to fully grasp complex biological systems and disease mechanisms[20].

Evolution of technology in pharmaceutical research

High-throughput screening techniques allow researchers to quickly screen large libraries of compounds for potential drug candidates, speeding up the initial stages of drug discovery[21]. Advances in genomics, proteomics, and other omics technologies have provided researchers with a deeper understanding of biological systems and disease pathways[22]. Computational approaches have become increasingly important, allowing researchers to simulate molecular interactions and predict the properties of potential drug candidates[23].

Emergence of AI in drug discovery

AI techniques, particularly machine learning and deep learning, have revolutionized drug discovery by analyzing large datasets, predicting molecular properties, and identifying potential drug candidates[24]. AI algorithms can perform virtual screening of compound libraries to identify molecules with the highest likelihood of binding to specific targets, reducing the time and cost associated with experimental screening[25]. AI models can predict the pharmacokinetic and pharmacodynamic properties of compounds, helping researchers prioritize the most promising candidates for further development[26].

AI algorithms can analyze patient data to identify biomarkers, predict treatment responses, and tailor therapies to individual patients, leading to the development of more effective and targeted treatments[27]. The integration of AI into drug discovery and development has the potential to significantly accelerate the process, reduce costs, and improve the success rate of bringing new drugs to market. However, challenges, such as data quality, regulatory considerations, and ethical concerns, must be addressed to fully realize the benefits of AI in pharmaceutical research.

APPLICATIONS OF AI IN DRUG DISCOVERY

Target identification and validation

The process of drug discovery starts with molecule searching. Certain small molecule databases can match specific health problems. Ranges of docking software are developed to explore molecular bioactivity[28]. Researchers also use analogues of already known molecules[29].

High-throughput screening and data analysis

High-throughput screening is a process for identifying potential molecules to be included in further analysis. The molecules identified in the previous step are screened to identify the most applicable to the health problem of interest. The process also includes molecule stability and interactions[30]. Wide varieties of methods are employed, including neural network, multiple linear regression, decision tree, and the analysis of variance[31,32].

Predictive modeling for drug design

Drug design is an important process. It includes the form (for example, tablet or solution) as well as the excipients (the other ingredients). AI is also applied at this stage. The tested parameters include the blend bulk and tapped density, flowability, angle of repose, appearance, friability, resistance to crushing, and time of disintegration of the tablet[33].

Biomarker discovery and validation

In the era of molecular medicine, biomarker discovery enhances the drug discovery process. Biomarker discovery requires a large number of samples to be collected and thoroughly analyzed in a uniform manner. Validation ensures that the marker is reproducible and reliable, and that its sensitivity and specificity are acceptable. AI could be employed in this step. Within the drug development context, biomarkers are used as an outcome measure in clinical trials, helping the identification and validation of drug targets. Thus, the right treatment for each patient based on the biomarkers tested would be found[34].

AI-DRIVEN DRUG DEVELOPMENT

In recent years, AI technology has revolutionized pharmaceutical research, ushering in a new era of drug development and revolutionizing medicine discovery, testing, and patient delivery. This revolution can transform various stages of the drug development pipeline, from accelerating preclinical research to optimizing clinical trial design and enabling personalized medicine approaches.

Accelerating preclinical research

Preclinical research involves extensive and time-consuming safety and efficacy assessments of potential drug candidates. This process is expensive, challenging, and often unsuccessful. Here, AI can streamline data analysis, predict drug interactions, and identify promising compounds. Machine learning algorithms have been developed to predict the toxicity of potential drug candidates[35]. Using AI-driven platforms, researchers can rapidly screen thousands of compounds and prioritize the most promising candidates, significantly reducing the time and cost of preclinical testing[36].

Optimization of clinical trial design

Conventional trial designs are often flawed and inefficient, leading to high costs, lengthy timelines, and sometimes inconclusive results. AI can improve patient recruitment, trial outcomes, and novel therapy development by tailoring inclusion criteria and treatment protocols based on predictive analytics[37]. In addition, AI-powered algorithms can analyze patient data, identify relevant biomarkers, and stratify patient populations to optimize trial design[38]. AI-driven simulations allow researchers to explore virtual trial scenarios, refine study protocols, and mitigate risks before initiating costly and time-consuming clinical trials.

Personalized medicine approaches

The advancement of personalized medicine is one of the most promising applications of AI in drug development. The paradigm of disease treatment and drug development is shifting towards personalized therapies to achieve better results for individual patients. AI can accelerate this trend by improving diagnostics, collecting personalized information, and assisting clinical decisions[39]. AI algorithms can store and analyze patient data, such as genetic profiles, clinical histories, and lifestyle factors[40]. This can significantly alleviate the burden of extensive data collection and analysis on researchers, facilitating their speedy and efficient work by allowing them to focus on the clinical scenario. In addition, AI technology can identify biomarkers associated with drug responses or disease progression, further improving targeted therapies with maximal efficacy and minimal adverse effects[41]. In conclusion, AI-driven drug development can transform the pharmaceutical industry by optimizing preclinical research, clinical trial design, and personalized treatment. Researchers can benefit from AI to expedite the discovery and delivery of innovative therapies that address unmet clinical needs and improve patient outcomes.

We present an overview of AI utilization in drug development in Figure 2.

Figure 2 An entire workflow of artificial intelligence applications in drug discovery and development, highlighting the different stages and the corresponding artificial intelligence techniques used. AI: Artificial intelligence; QSAR: Quantitative structure-activity relationship. The figure was generated using brainstorming from OpenAI. (2024). ChatGPT [Large language model]. https://chatgpt.com/c/d95bc2d2-a53a-492d-a78d-6946bc43cef2.

CHALLENGES AND ETHICAL CONSIDERATIONS

Data quality and bias in AI models

Ensuring the quality and reliability of data used to train AI models is crucial for accurate predictions and decision-making. Biases, inaccuracies, and incompleteness in the data can lead to flawed results[42]. AI models can inherit biases present in the training data, leading to biased predictions or decisions. This is particularly concerning in healthcare, where biases related to race, sex, or socioeconomic status can impact patient outcomes[43]. Developing techniques to identify and mitigate biases in AI models, as well as ensuring diverse and representative training datasets, are essential for ethical AI applications in drug discovery[44].

Interpretability and transparency

Many AI models, particularly deep learning models, are often considered ”black boxes” because their internal workings are not easily interpretable by humans. This lack of transparency raises concerns about how decisions are made and undermines trust in AI systems[45]. There is a growing need for interpretable AI models, where the reasoning behind predictions or recommendations can be understood by domain experts and regulatory authorities[46]. Establishing mechanisms to ensure accountability for AI-driven decisions, including transparency about model training and validation, is crucial for maintaining ethical standards in drug discovery[47].

Regulatory and ethical implications

AI applications in drug discovery must adhere to regulatory requirements set forth by agencies, such as the Food and Drug Administration and European Medicines Agency. Ensuring compliance with regulations designed for traditional drug development processes presents challenges due to the unique nature of AI technologies[48]. Demonstrating the safety and efficacy of AI-generated drug candidates or treatment recommendations is essential for regulatory approval. Robust validation and testing procedures are necessary to mitigate risks to patients[49]. Protecting patient privacy and securing sensitive healthcare data used in AI applications is paramount. Adhering to data protection regulations, such as the General Data Protection Regulation and the Health Insurance Portability and Accountability Act, is essential to maintain trust and ethical standards[50].

Addressing these challenges and ethical considerations is crucial for the responsible and ethical use of AI in drug discovery and development. Collaborative efforts between researchers, regulatory bodies, and ethicists are essential to develop guidelines and frameworks that promote the ethical use of AI while maximizing its potential benefits in healthcare.

FUTURE DIRECTIONS AND INNOVATIONS

Advancements in AI technologies

Deep learning is advanced machine learning that could be applied in the field of drug discovery. It is a neural network that can extract information from public databases and create scientific conclusions based on them. Deep learning is applicable to reduce the costs of the clinical trials by predicting their outcome before they start[51]. Another promising application of AI in the field of drug discovery is drug repurposing. Finding new applications for already existing drugs reduces the time and cost of their development[52]. Another new trend in the field of drug discovery and development is AI application in nanotechnologies, especially nanocarriers. AI is also crucial in smart drug release systems that deliver the medicine when it is needed[53].

Collaborative approaches and industry trends

Application of AI in the field of drug discovery requires a multidisciplinary approach by default. Collaboration between researchers, clinical experts, engineers, and data managers is crucial. Thus, multidisciplinary education is required to meet the new demands of pharmaceutical trends[53].

Potential impact on drug development pipelines

AI can speed up the drug discovery and development process and reduce costs. The resources not allocated to drug discovery could be invested into drug searching for different diseases. This could have a large positive impact on public health.

INTEGRATION OF AI INTO MAINSTREAM DRUG DISCOVERY

The use of AI in mainstream drug research signifies a watershed moment set to transform the pharmaceutical business. Adoption strategies and industry readiness are critical components of this transformation, which need strong frameworks for AI adoption and organizational readiness[54].

Training and skill development programs are critical for providing professionals with the requisite skills in AI-driven approaches, guaranteeing smooth integration and realizing the potential advantages. Overcoming industrial opposition and skepticism requires proactive actions to address concerns about AI technology dependability, ethical issues, and data security. Collaboration among stakeholders, regulatory authorities, and AI developers is critical for building confidence and accelerating wider adoption[54]. As AI evolves and demonstrates its usefulness in drug development, proactive involvement, ongoing education, and open communication will be critical in managing difficulties and maximizing the promise of AI-driven advances in the pharmaceutical sector.

In Table 1, we present the AI techniques used for drug development, their application areas, key benefits, and challenges.

Table 1 Applications of artificial intelligence in drug discovery and development

Application area	AI techniques used	Key benefits	Challenges	
Target identification	Machine learning, deep learning	Identifying novel drug targets, high accuracy	Data quality, complexity of biological systems	
Drug screening	Virtual screening, predictive models	Faster screening of compounds, cost-effective	False positives/negatives, model validation	
Lead optimization	QSAR models, reinforcement learning	Improved candidate selection, reduced development time	Integration with traditional methods, data scarcity	
Preclinical development	Image analysis, natural language processing	Enhanced understanding of drug toxicity and efficacy	Interpretation of complex data, standardization	
Clinical trials	Predictive analytics, patient recruitment algorithms	Optimized trial design, better patient stratification	Ethical concerns, data privacy	
Personalized medicine	Genomic data analysis, personalized algorithms	Tailored treatments, improved patient outcomes	Data integration, regulatory issues	
AI: Artificial intelligence; QSAR: Quantitative structure-activity relationship.

CONSIDERATIONS FOR SMALL AND LARGE PHARMACEUTICAL COMPANIES

Customization of AI tools

Small pharmaceutical companies may benefit from customizable AI tools that are tailored to their specific research needs and capabilities. Customization allows them to focus on targeted areas of drug discovery where AI can provide the most value. Larger pharmaceutical companies may have the resources to develop or acquire sophisticated AI platforms that can be customized for various stages of drug development, from target identification to clinical trial optimization. Customization enables them to integrate AI seamlessly into their existing workflow and infrastructure[55].

Resource allocation and return on investment

Small pharmaceutical companies must carefully allocate limited resources when investing in AI technologies. They need to assess the potential return on investment of implementing AI tools and prioritize projects with the highest likelihood of success. Large pharmaceutical companies have greater financial resources and may allocate significant budgets to AI initiatives[55]. However, they also face pressure to demonstrate tangible return on investment and ensure that AI investments align with strategic business objectives.

GLOBAL COLLABORATIONS AND DATA SHARING

Small pharmaceutical companies may lack access to large datasets necessary to train AI models effectively. Collaborating with academic institutions, research organizations, or larger pharmaceutical companies can provide access to diverse datasets and expertise, facilitating more robust AI applications. In contrast, large pharmaceutical companies often have extensive internal datasets, but collaboration with external partners can still be beneficial. Engaging in global collaborations and data sharing initiatives allows them to access additional resources, validate AI models across diverse populations, and accelerate drug discovery efforts[53].

Both small and large pharmaceutical companies can leverage AI in drug discovery and development, but considerations, such as customization of AI tools, resource allocation, and global collaborations differ based on their size, resources, and organizational capabilities.

CONCLUSION

This review showed that integrating AI can revolutionize drug discovery and development. Utilizing machine learning algorithms, deep learning techniques, and data analytics, AI can expedite target identification, optimize lead compounds, and predict pharmacokinetics and toxicity. By acknowledging the inherent challenges, including insufficient resources and model interpretability on a larger scale, this review demonstrated the need for robust validation and ethical considerations in AI-driven drug development. Nevertheless, the future demonstrates a pressing need for industry stakeholder and research community collaboration to overcome these hurdles and harness the full potential of AI to drive innovation and improve patient outcomes in medical research.

Conflict-of-interest statement: All authors declare they have no conflicts of interest to disclose.

Provenance and peer review: Invited article; Externally peer reviewed.

Peer-review model: Single blind

Specialty type: Research and experimental medicine

Country of origin: Bulgaria

Peer-review report’s classification

Scientific Quality: Grade B

Novelty: Grade B

Creativity or Innovation: Grade C

Scientific Significance: Grade B

P-Reviewer: Ye XJ S-Editor: Liu JH L-Editor: Filipodia P-Editor: Yu HG
==== Refs
1 Hughes JP Rees S Kalindjian SB Philpott KL Principles of early drug discovery Br J Pharmacol 2011 162 1239 1249 21091654
2 Bohr H Chapter 3 - Drug discovery and molecular modeling using artificial intelligence AI Health 2020 61 83
3 Ekins S Puhl AC Zorn KM Lane TR Russo DP Klein JJ Hickey AJ Clark AM Exploiting machine learning for end-to-end drug discovery and development Nat Mater 2019 18 435 441 31000803
4 Zhang L Tan J Han D Zhu H From machine learning to deep learning: progress in machine intelligence for rational drug discovery Drug Discov Today 2017 22 1680 1685 28881183
5 Lavecchia A Deep learning in drug discovery: opportunities, challenges and future prospects Drug Discov Today 2019 24 2017 2032 31377227
6 Gaulton A Bellis LJ Bento AP Chambers J Davies M Hersey A Light Y McGlinchey S Michalovich D Al-Lazikani B Overington JP ChEMBL: a large-scale bioactivity database for drug discovery Nucleic Acids Res 2012 40 D1100 D1107 21948594
7 Kim S Exploring Chemical Information in PubChem Curr Protoc 2021 1 e217 34370395
8 Ashraf FB Akter S Mumu SH Islam MU Uddin J Bio-activity prediction of drug candidate compounds targeting SARS-Cov-2 using machine learning approaches PLoS One 2023 18 e0288053 37669264
9 Mayr A Klambauer G Unterthiner T Hochreiter S DeepTox: Toxicity Prediction using Deep Learning Front Environ Sci 2016 3
10 Wu Z Ramsundar B Feinberg EN Gomes J Geniesse C Pappu AS Leswing K Pande V MoleculeNet: a benchmark for molecular machine learning Chem Sci 2018 9 513 530 29629118
11 Wallach HA Dzamba MI AtomNet: a deep convolutional neural network for bioactivity prediction in structure-based drug discovery. arXiv; 2015
12 Marr B How is AI used in healthcare—5 powerful real-world examples that show the latest advances. Forbes; 2018
13 The CONVERGE® Platform Last accessed: April 23, 2024. Available from: https://www.vergegenomics.com/approach
14 FDA Grants Breakthrough Device Status to Healthcare AI Software Last accessed April 23, 2024. Available from: https://hitinfrastructure.com/news/fda-grants-breakthrough-device-status-to-healthcare-ai-software#:~:text=December%2005%2C%202018%20%2D%20The%20FDA,hypertension%20(CTEPH)%20pattern%20recognition
15 Artificial intelligence decodes cancer pathology images Last accessed April 23, 2024. Available from: https://www.novartis.com/stories/artificial-intelligence-decodes-cancer-pathology-images
16 Machine learning poised to accelerate drug discovery Last accessed April 23, 2024. Available from: https://www.novartis.com/stories/machine-learning-poised-accelerate-drug-discovery
17 Cyclica Drives Drug Discovery Through AI-Augmented Integrated Network of Cloud-Based Technologies together with Bayer Last accessed April 23, 2024. Available from: https://www.cyclicarx.com/press-releases/cyclica-drives-drug-discovery-through-ai-augmented-integrated-network-of-cloud-based-technologies-together-with-bayer
18 Harvey A The role of natural products in drug discovery and development in the new millennium IDrugs 2010 13 70 72 20127553
19 Begley CG Ellis LM Drug development: Raise standards for preclinical cancer research Nature 2012 483 531 533 22460880
20 Korinek M Hsieh PS Chen YL Hsieh PW Chang SH Wu YH Hwang TL Randialic acid B and tomentosolic acid block formyl peptide receptor 1 in human neutrophils and attenuate psoriasis-like inflammation in vivo Biochem Pharmacol 2021 190 114596 33964283
21 Tingaud-Sequeira A Carnevali O Cerdà J Cathepsin B differential expression and enzyme processing and activity during Fundulus heteroclitus embryogenesis Comp Biochem Physiol A Mol Integr Physiol 2011 158 221 228 21059400
22 Wingender E The TRANSFAC project as an example of framework technology that supports the analysis of genomic regulation Brief Bioinform 2008 9 326 332 18436575
23 Baig MH Ahmad K Rabbani G Danishuddin M Choi I Computer Aided Drug Design and its Application to the Development of Potential Drugs for Neurodegenerative Disorders Curr Neuropharmacol 2018 16 740 748 29046156
24 Dana D Gadhiya SV St Surin LG Li D Naaz F Ali Q Paka L Yamin MA Narayan M Goldberg ID Narayan P Deep Learning in Drug Discovery and Medicine; Scratching the Surface Molecules 2018 23
25 Urban G Bache KM Phan D Sobrino A Shmakov AK Hachey SJ Hughes C Baldi P Deep Learning for Drug Discovery and Cancer Research: Automated Analysis of Vascularization Images IEEE/ACM Trans Comput Biol Bioinform 2019 16 1029 1035 29993583
26 Lim J Ryu S Park K Choe YJ Ham J Kim WY Predicting Drug-Target Interaction Using a Novel Graph Neural Network with 3D Structure-Embedded Graph Representation J Chem Inf Model 2019 59 3981 3988 31443612
27 Chen Z Liu X Hogan W Shenkman E Bian J Applications of artificial intelligence in drug development using real-world data Drug Discov Today 2021 26 1256 1264 33358699
28 Lavecchia A Di Giovanni C Virtual screening strategies in drug discovery: a critical review Curr Med Chem 2013 20 2839 2860 23651302
29 Sivakumar B Kaliappan I Lead drug discovery from imidazolinone derivatives with Aurora kinase inhibitors PHAR 2023 70 1529 1540
30 Malo N Hanley JA Cerquozzi S Pelletier J Nadon R Statistical practice in high-throughput screening data analysis Nat Biotechnol 2006 24 167 175 16465162
31 Dara S Dhamercherla S Jadav SS Babu CM Ahsan MJ Machine Learning in Drug Discovery: A Review Artif Intell Rev 2022 55 1947 1999 34393317
32 Shockley KR Gupta S Harris SF Lahiri SN Peddada SD Quality Control of Quantitative High Throughput Screening Data Front Genet 2019 10 387 31143201
33 Behei N Tryhubchak O Pryymak B Development of amlodipine and enalapril combined tablets based on quality by design and artificial neural network for confirming of qualitative composition PHAR 2022 69 779 789
34 Kraus VB Biomarkers as drug development tools: discovery, validation, qualification and use Nat Rev Rheumatol 2018 14 354 362 29760435
35 Pu L Naderi M Liu T Wu HC Mukhopadhyay S Brylinski M eToxPred: a machine learning-based approach to estimate the toxicity of drug candidates BMC Pharmacol Toxicol 2019 20 2 30621790
36 Mak KK Pichika MR Artificial intelligence in drug development: present status and future prospects Drug Discov Today 2019 24 773 780 30472429
37 Harrer S Shah P Antony B Hu J Artificial Intelligence for Clinical Trial Design Trends Pharmacol Sci 2019 40 577 591 31326235
38 Hutson M How AI is being used to accelerate clinical trials Nature 2024 627 S2 S5 38480968
39 Johnson KB Wei WQ Weeraratne D Frisse ME Misulis K Rhee K Zhao J Snowdon JL Precision Medicine, AI, and the Future of Personalized Health Care Clin Transl Sci 2021 14 86 93 32961010
40 Schork NJ Artificial Intelligence and Personalized Medicine Cancer Treat Res 2019 178 265 283 31209850
41 Michelhaugh SA Januzzi JL Jr Using Artificial Intelligence to Better Predict and Develop Biomarkers Heart Fail Clin 2022 18 275 285 35341540
42 Siegert MJ Priscu JC Alekhina IA Wadham JL Lyons WB Preface Philos Trans A Math Phys Eng Sci 2016 374
43 Househ MS Aldosari B Alanazi A Kushniruk AW Borycki EM Big Data, Big Problems: A Healthcare Perspective Stud Health Technol Inform 2017 238 36 39 28679881
44 Mehrabi N Morstatter F Saxena N Lerman K Galstyan A Reducing Bias in AI: A Survey on Sources of Bias and Mitigation Strategies. 2019 Preprint. Available from: arXiv:1908.09635
45 Lipton ZC The Mythos of Model Interpretability. 2016 Preprint. Available from: arXiv:1606.03490
46 Zitnik M Nguyen F Wang B Leskovec J Goldenberg A Hoffman MM Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities Inf Fusion 2019 50 71 91 30467459
47 Wachter S Mittelstadt B Russell C Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR SSRN J
48 Zhavoronkov A Artificial Intelligence for Drug Discovery, Biomarker Development, and Generation of Novel Chemistry Mol Pharm 2018 15 4311 4313 30269508
49 Draper P Farkas S Artificial Intelligence and Deep Learning in Pathology: A Tutorial on the Basic Principles and Recent Advances. 2019 Preprint. Available from: arXiv:1901.00515
50 Hiwale M Walambe R Potdar V Kotecha K A systematic review of privacy-preserving methods deployed with blockchain and federated learning for the telemedicine Healthc Anal (NY) 2023 3 100192
51 Yadav S Singh A Singhal R Yadav JP Revolutionizing drug discovery: The impact of artificial intelligence on advancements in pharmacology and the pharmaceutical industry Intelligent Pharm 2024 2 367 380
52 Yang F Zhang Q Ji X Zhang Y Li W Peng S Xue F Machine Learning Applications in Drug Repurposing Interdiscip Sci 2022 14 15 21 35066811
53 Vidhya KS Sultana A M NK Rangareddy H Artificial Intelligence's Impact on Drug Discovery and Development From Bench to Bedside Cureus 2023 15 e47486 37881323
54 Visan AI Negut I Integrating Artificial Intelligence for Drug Discovery in the Context of Revolutionizing Drug Delivery Life (Basel) 2024 14
55 Vora LK Gholap AD Jetha K Thakur RRS Solanki HK Chavda VP Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design Pharmaceutics 2023 15
