AI AND DEEP LEARNING IN DRUG DISCOVERY: ADVANCES, BENCHMARKS, AND FUTURE CHALLENGES
Main Article Content
Keywords
Artificial Intelligence (AI); Deep Learning (DL); Drug Discovery; Molecular Design; Virtual Screening; AlphaFold; Generative Adversarial Networks (GANs); Reinforcement Learning; Explainable AI; Multimodal Learning; Digital Twin Modeling; Computational Pharmacology; Predictive Modeling; Structural Bioinformatics; Translational Drug Development.
Abstract
Artificial intelligence (AI) and deep learning (DL) are redefining the landscape of modern drug discovery by transforming data-driven prediction, molecular design, and optimization workflows. DL architectures such as convolutional, recurrent, graph-based, and transformer models enable the efficient integration of chemical, biological, and clinical datasets to predict binding interactions, structural conformations, and pharmacokinetic behavior with remarkable accuracy. The incorporation of AlphaFold-like structural predictors, generative adversarial networks (GANs), and reinforcement learning frameworks has accelerated target identification, virtual screening, and de novo compound generation. These developments have significantly reduced the cost and time associated with early-stage drug development while improving hit quality and safety evaluation. However, challenges persist, including limited interpretability, dataset bias, and computational complexity. The review highlights emerging strategies such as explainable AI, multimodal learning, and digital twin modelling to overcome these limitations. Collectively, DL-driven approaches mark a pivotal transition toward predictive, transparent, and sustainable pharmaceutical innovation that bridges computational and experimental discovery.
References
1. Rahuel, J., Rasetti, V., Maibaum, J., Rüeger, H., Göschke, R., Cohen, N.C., Stutz, S., Cumin, F., Fuhrer, W., Wood, J.M. and Grütter, M.G., 2000. Structure-based drug design: the discovery of novel nonpeptide orally active inhibitors of human renin. Chemistry & biology, 7(7), pp.493-504.
2. Cao, D., Zhang, P. and Wang, S., 2024. Advances in structure-based drug design: the potential for precision therapeutics in psychiatric disorders. Neuron, 112(4), pp.526-538.
3. Soni, M. and Pratap, J.V., 2022. Development of novel anti-leishmanials: the case for structure-based approaches. Pathogens, 11(8), p.950.
4. Singh, S., Kumar, R., Payra, S. and Singh, S.K., 2023. Artificial intelligence and machine learning in pharmacological research: bridging the gap between data and drug discovery. Cureus, 15(8).
5. Gupta, R., Srivastava, D., Sahu, M., Tiwari, S., Ambasta, R.K. and Kumar, P., 2021. Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Molecular diversity, 25(3), pp.1315-1360.
6. Selvaraj, C., Chandra, I. and Singh, S.K., 2022. Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries. Molecular diversity, 26(3), pp.1893-1913.
7. Goshisht, M.K., 2024. Machine learning and deep learning in synthetic biology: Key architectures, applications, and challenges. ACS omega, 9(9), pp.9921-9945.
8. Mienye, I.D. and Swart, T.G., 2024. A comprehensive review of deep learning: Architectures, recent advances, and applications. Information, 15(12), p.755.
9. Odah, M., 2025. Artificial Intelligence Meets Drug Discovery: A Systematic Review on AI-Powered Target Identification and Molecular Design.
10. Utti, V., Bikias, T., Agarwal, A.A., Bikia, V., Zhou, A.Y., Shah, M.M. and Daneshjou, R., 2025. Artificial Intelligence in Dermatology Research and Drug Discovery. Dermatologic Clinics.
11. Pal, S., Kaushik, A., Goel, F., Pal, A. and Garg, V.K., 2025. Revolutionizing Early Diagnosis: The Role of Artificial Intelligence in Neurodegenerative Disorders. Journal of Advanced Scientific Research, 16(03), pp.31-37.
12. Nuka, S.T., Chakilam, C., Chava, K., Suura, S.R. and Recharla, M., 2025. AI-Driven Drug Discovery: Transforming Neurological and Neurodegenerative Disease Treatment Through Bioinformatics and Genomic Research. American Journal of Psychiatric Rehabilitation, 28(1), pp.124-135.
13. Thompson, M. and Bennett, J., 2025. The Impact of AI-Powered Diagnostics on Early Detection of Diseases: Lessons Learned from the COVID-19 Pandemic.
14. Chen, W., Liu, X., Zhang, S. and Chen, S., 2023. Artificial intelligence for drug discovery: Resources, methods, and applications. Molecular therapy Nucleic acids, 31, pp.691-702.
15. Mirakhori, F. and Niazi, S.K., 2025. Harnessing the AI/ML in drug and biological products discovery and development: the regulatory perspective. Pharmaceuticals, 18(1), p.47.
16. Gupta, R., Srivastava, D., Sahu, M., Tiwari, S., Ambasta, R.K. and Kumar, P., 2021. Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Molecular diversity, 25(3), pp.1315-1360.
17. Marques, L., Costa, B., Pereira, M., Silva, A., Santos, J., Saldanha, L., Silva, I., Magalhães, P., Schmidt, S. and Vale, N., 2024. Advancing precision medicine: a review of innovative in silico approaches for drug development, clinical pharmacology and personalized healthcare. Pharmaceutics, 16(3), p.332.
18. Shah, M., Patel, M., Shah, M., Patel, M. and Prajapati, M., 2024. Computational transformation in drug discovery: A comprehensive study on molecular docking and quantitative structure activity relationship (QSAR). Intelligent Pharmacy, 2(5), pp.589-595.
19. Achary, P.G., 2020. Applications of quantitative structure-activity relationships (QSAR) based virtual screening in drug design: a review. Mini Reviews in Medicinal Chemistry, 20(14), pp.1375-1388.
20. Qiu, Y. and Wei, G.W., 2023. Artificial intelligence-aided protein engineering: from topological data analysis to deep protein language models. Briefings in Bioinformatics, 24(5), p.bbad289.
21. Ru, X., Xu, L., Han, W. and Zou, Q., 2025. In silico methods for drug-target interaction prediction. Cell Reports Methods, 5(10).
22. Abdollahi, S., Schaub, D.P., Barroso, M., Laubach, N.C., Hutwelker, W., Panzer, U., Gersting, S.Ø.W. and Bonn, S., 2024. A comprehensive comparison of deep learning-based compound-target interaction prediction models to unveil guiding design principles. Journal of Cheminformatics, 16(1), p.118.
23. Abdelkader, G.A. and Kim, J.D., 2024. Advances in protein-ligand binding affinity prediction via deep learning: a comprehensive study of datasets, data preprocessing techniques, and model architectures. Current drug targets, 25(15), pp.1041-1065.
24. Szczepski, K. and Jaremko, Ł., 2025. AlphaFold and what is next: bridging functional, systems and structural biology. Expert Review of Proteomics, 22(2), pp.45-58.
25. Lyu, J., Kapolka, N., Gumpper, R., Alon, A., Wang, L., Jain, M.K., Barros-Álvarez, X., Sakamoto, K., Kim, Y., DiBerto, J. and Kim, K., 2024. AlphaFold2 structures template ligand discovery. BioRxiv, pp.2023-12.
26. Albani, F.G., Alghamdi, S.S., Almutairi, M.M. and Alqahtani, T., 2025. Artificial Intelligence-Driven Innovations in Oncology Drug Discovery: Transforming Traditional Pipelines and Enhancing Drug Design. Drug Design, Development and Therapy, pp.5685-5707.
27. Gangwal, A., Ansari, A., Ahmad, I., Azad, A.K., Kumarasamy, V., Subramaniyan, V. and Wong, L.S., 2024. Generative artificial intelligence in drug discovery: basic framework, recent advances, challenges, and opportunities. Frontiers in pharmacology, 15, p.1331062.
28. Sucharitha, P., Reddy, K.R., Satyanarayana, S.V. and Garg, T., 2022. Absorption, distribution, metabolism, excretion, and toxicity assessment of drugs using computational tools. In Computational approaches for novel therapeutic and diagnostic designing to mitigate SARS-CoV-2 infection (pp. 335-355). Academic Press.
29. Ullah, I., Rios, A., Gala, V. and Mckeever, S., 2021. Explaining deep learning models for tabular data using layer-wise relevance propagation. Applied Sciences, 12(1), p.136.
30. Pramudito, M.A., Fuadah, Y.N., Qauli, A.I., Marcellinus, A. and Lim, K.M., 2024. Explainable artificial intelligence (XAI) to find optimal in-silico biomarkers for cardiac drug toxicity evaluation. Scientific Reports, 14(1), p.24045.
31. Goryanin, I., Goryanin, I. and Demin, O., 2025. Revolutionizing drug discovery: Integrating artificial intelligence with quantitative systems pharmacology. Drug Discovery Today, p.104448.
32. Mondal, A., Chang, L. and Perez, A., 2022. Modelling peptide–protein complexes: docking, simulations and machine learning. QRB discovery, 3, p.e17.
33. Staszak, M., Staszak, K., Wieszczycka, K., Bajek, A., Roszkowski, K. and Tylkowski, B., 2022. Machine learning in drug design: Use of artificial intelligence to explore the chemical structure–biological activity relationship. Wiley Interdisciplinary Reviews: Computational Molecular Science, 12(2), p.e1568.
34. Mysore, V., Patel, N. and Ojewole, A., 2024. Deep Learning for the Structure‐Based Binding Free Energy Prediction of Small Molecule Ligands. Computational Drug Discovery: Methods and Applications, 1, pp.255-273.
35. Aburass, S., Dorgham, O. and Al Shaqsi, J., 2024. A hybrid machine learning model for classifying gene mutations in cancer using LSTM, BiLSTM, CNN, GRU, and GloVe. Systems and Soft Computing, 6, p.200110.
36. Mienye, I.D., Swart, T.G. and Obaido, G., 2024. Recurrent neural networks: A comprehensive review of architectures, variants, and applications. Information, 15(9), p.517.
37. Grisoni, F., 2023. Chemical language models for de novo drug design: Challenges and opportunities. Current Opinion in Structural Biology, 79, p.102527.
38. Kumar, A. and Karki, S.S., 2025. AI & Machine Learning in Lead Discovery: Deep-Learning Architectures for de novo Design, Property Prediction and Inverse QSAR. molecules, 6, p.8.
39. Sheshanarayana, R. and You, F., 2025. Molecular representation learning: cross-domain foundations and future Frontiers. Digital Discovery.
40. Zhang, L., Ouyang, C., Liu, Y., Liao, Y. and Gao, Z., 2023. Multimodal contrastive representation learning for drug-target binding affinity prediction. Methods, 220, pp.126-133.
41. Zeng, X., Li, S.J., Lv, S.Q., Wen, M.L. and Li, Y., 2024. A comprehensive review of the recent advances on predicting drug-target affinity based on deep learning. Frontiers in Pharmacology, 15, p.1375522.
42. Chatzianastasis, M., 2025. Advancements in Graph Representation Learning and Applications in Computational Biology (Doctoral dissertation, Institut Polytechnique de Paris).
43. Chen, J.Y., Wang, J.F., Hu, Y., Li, X.H., Qian, Y.R. and Song, C.L., 2025. Evaluating the advancements in protein language models for encoding strategies in protein function prediction: a comprehensive review. Frontiers in bioengineering and biotechnology, 13, p.1506508.
44. Mswahili, M.E. and Jeong, Y.S., 2024. Transformer-based models for chemical SMILES representation: A comprehensive literature review. Heliyon, 10(20).
45. Kuang, T., Liu, P. and Ren, Z., 2024. Impact of Domain Knowledge and Multi-Modality on Intelligent Molecular Property Prediction: A Systematic Survey. Big Data Mining and Analytics, 7(3), pp.858-888.
46. Vidanagamachchi, S.M. and Waidyarathna, K.M.G.T.R., 2024. Opportunities, challenges and future perspectives of using bioinformatics and artificial intelligence techniques on tropical disease identification using omics data. Frontiers in Digital Health, 6, p.1471200.
47. Lavecchia, A., 2025. Transform drug discovery and development with generative artificial intelligence. Generative Artificial Intelligence for Biomedical and Smart Health Informatics, pp.489-537.
48. Su, X., Hu, P., Li, D., Zhao, B., Niu, Z., Herget, T., Yu, P.S. and Hu, L., 2025. Interpretable identification of cancer genes across biological networks via transformer-powered graph representation learning. Nature biomedical engineering, 9(3), pp.371-389.
49. Choi, S.R. and Lee, M., 2023. Transformer architecture and attention mechanisms in genome data analysis: a comprehensive review. Biology, 12(7), p.1033.
50. Wang, M., Li, W., Yu, X., Luo, Y., Han, K., Wang, C. and Jin, Q., 2023. AffinityVAE: a multi-objective model for protein-ligand affinity prediction and drug design. Computational Biology and Chemistry, 107, p.107971.
51. Li, S., Zhou, J., Xu, T., Huang, L., Wang, F., Xiong, H., Huang, W., Dou, D. and Xiong, H., 2023. Giant: Protein-ligand binding affinity prediction via geometry-aware interactive graph neural network. IEEE Transactions on Knowledge and Data Engineering, 36(5), pp.1991-2008.
52. Kumar, N. and Acharya, V., 2024. Advances in machine intelligence‐driven virtual screening approaches for big‐data. Medicinal Research Reviews, 44(3), pp.939-974.
53. Cui, J., Yang, S., Yi, L., Xi, Q., Yang, D. and Zuo, Y., 2025. Recent advances in deep learning for protein-protein interaction: a review. BioData Mining, 18(1), p.43.
54. de Angelo, R.M., de Sousa, D.S., da Silva, A.P., Chiari, L.P., da Silva, A.B. and Honorio, K.M., 2025. Molecular Docking: State-of-the-Art Scoring Functions and Search Algorithms. In Computer-Aided and Machine Learning-Driven Drug Design: From Theory to Applications (pp. 163-198). Cham: Springer Nature Switzerland.
55. da Silva, M.M.P., Guedes, I.A., Custódio, F.L., da Silva, E.K. and Dardenne, L.E., 2024. Deep learning strategies for enhanced molecular docking and virtual screening. In Structure-Based Drug Design (pp. 177-221). Cham: Springer International Publishing.
56. Ahmad, M. and Teli, T.A., 2024. De Novo Molecular Generation Augmentation for Drug Discovery Using Deep Learning Approaches: A Comparative Study of Variational Autoencoders. Integrative Biomedical Research, 8(10), pp.1-13.
57. Su, Y.Y., Huang, H.C., Lin, Y.T., Chuang, Y.F., Sheu, S.Y. and Lin, C.C., 2025. HEDDI-Net: heterogeneous network embedding for drug-disease association prediction and drug repurposing, with application to Alzheimer’s disease. Journal of Translational Medicine, 23(1), p.57.
58. Jia, L. and Gao, H., 2021. Machine Learning for in silico ADMET prediction. In Artificial Intelligence in Drug Design (pp. 447-460). New York, NY: Springer US.
59. Swanson, K., Walther, P., Leitz, J., Mukherjee, S., Wu, J.C., Shivnaraine, R.V. and Zou, J., 2024. ADMET-AI: a machine learning ADMET platform for evaluation of large-scale chemical libraries. Bioinformatics, 40(7), p.btae416.
60. Robert, K., Kaium, O. and Alasa, L., 2025. Generative AI and Pharmaceutical Innovation: Accelerating Drug Discovery with Deep Learning and Predictive Analytics.
61. Ünlü, A., Ulusoy, E., Yiğit, M.G., Darcan, M. and Doğan, T., 2025. Protein language models for predicting drug–target interactions: Novel approaches, emerging methods, and future directions. Current Opinion in Structural Biology, 91, p.103017.
62. Zdrazil, B., 2025. Fifteen years of ChEMBL and its role in cheminformatics and drug discovery. Journal of Cheminformatics, 17(1), pp.1-9.
63. Santos, K.B., Guedes, I.A., Karl, A.L. and Dardenne, L.E., 2020. Highly flexible ligand docking: Benchmarking of the DockThor program on the LEADS-PEP protein–peptide data set. Journal of Chemical Information and Modeling, 60(2), pp.667-683.
64. Ji, Y., Zhang, L., Wu, J., Wu, B., Li, L., Huang, L.K., Xu, T., Rong, Y., Ren, J., Xue, D. and Lai, H., 2023, June. Drugood: Out-of-distribution dataset curator and benchmark for ai-aided drug discovery–a focus on affinity prediction problems with noise annotations. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 37, No. 7, pp. 8023-8031).
65. Basu, S. and Wallner, B., 2016. DockQ: a quality measure for protein-protein docking models. PloS one, 11(8), p.e0161879.
66. Collins, K.W., Copeland, M.M., Brysbaert, G., Wodak, S.J., Bonvin, A.M., Kundrotas, P.J., Vakser, I.A. and Lensink, M.F., 2024. CAPRI-Q: The CAPRI resource evaluating the quality of predicted structures of protein complexes. Journal of molecular biology, 436(17), p.168540.
67. Preijers, T., Muller, A.E., Abdulla, A., de Winter, B.C., Koch, B.C. and Sassen, S.D., 2024. Dose individualisation of antimicrobials from a pharmacometric standpoint: the current landscape. Drugs, 84(10), pp.1167-1178.
68. Damaševičius, R., Jagatheesaperumal, S.K., Kandala, R.N., Hussain, S., Alizadehsani, R. and Gorriz, J.M., 2024. Deep learning for personalized health monitoring and prediction: A review. Computational Intelligence, 40(3), p.e12682.
69. Fatunmbi, T.O., 2024. Predicting precision-based treatment plans using artificial intelligence and machine learning in complex medical scenarios.
70. Geaney, A., O’Reilly, P., Maxwell, P., James, J.A., McArt, D. and Salto-Tellez, M., 2023. Translation of tissue-based artificial intelligence into clinical practice: from discovery to adoption. Oncogene, 42(48), pp.3545-3555.
71. Sengupta, S., Maji, L., Das, P.K., Teli, G., Nag, M., Khan, N., Haque, M. and Matada, G.S.P., 2025. Explanatory review on DDR inhibitors: their biological activity, synthetic route, and structure–activity relationship. Molecular Diversity, pp.1-31.
72. Niazi, S.K., 2025. Artificial Intelligence in Small-Molecule Drug Discovery: A Critical Review of Methods, Applications, and Real-World Outcomes. Pharmaceuticals, 18(9), p.1271.
73. Uzoeto, H.O., Cosmas, S., Bakare, T.T. and Durojaye, O.A., 2024. AlphaFold-latest: revolutionizing protein structure prediction for comprehensive biomolecular insights and therapeutic advancements. Beni-Suef University Journal of Basic and Applied Sciences, 13(1), p.46.
74. Warr, W., 2021. National Institutes of Health (NIH) Workshop on Reaction Informatics.
75. Tan, Z., Zhao, Y., Zhou, T. and Lin, K., 2023. Hi-MGT: a hybrid molecule graph transformer for toxicity identification. Journal of Hazardous Materials, 457, p.131808.
76. Chitsazi, R., Wu, Y., GPCR Dock 2021 participants, Stevens, R.C., Zhao, S. and Kufareva, I., 2025. The 4th GPCR Dock: assessment of blind predictions for GPCR-ligand complexes in the era of AlphaFold. bioRxiv, pp.2025-04.
77. Lavecchia, A., 2025. Explainable Artificial Intelligence in Drug Discovery: Bridging Predictive Power and Mechanistic Insight. Wiley Interdisciplinary Reviews: Computational Molecular Science, 15(5), p.e70049.
78. An, G., 2022. Specialty grand challenge: What it will take to cross the valley of death: Translational systems biology,“true” precision medicine, medical digital twins, artificial intelligence and in silico clinical trials. Frontiers in Systems Biology, 2, p.901159.
79. John, A., Qin, B., Kalari, K.R., Wang, L. and Yu, J., 2020. Patient-specific multi-omics models and the application in personalized combination therapy. Future Oncology, 16(23), pp.1737-1750.
80. Núñez Carpintero, I., 2023. Network-based methods for biological data integration in precision medicine.
81. Taha, K., 2024. Employing Machine Learning Techniques to Detect Protein Function: A Survey, Experimental, and Empirical Evaluations. IEEE/ACM Transactions on Computational Biology and Bioinformatics.
82. Brancato, V., Esposito, G., Coppola, L., Cavaliere, C., Mirabelli, P., Scapicchio, C., Borgheresi, R., Neri, E., Salvatore, M. and Aiello, M., 2024. Standardizing digital biobanks: integrating imaging, genomic, and clinical data for precision medicine. Journal of translational medicine, 22(1), p.136.
83. Ballard, J.L., Wang, Z., Li, W., Shen, L. and Long, Q., 2024. Deep learning-based approaches for multi-omics data integration and analysis. BioData Mining, 17(1), p.38.
84. Li, F.Z., 2025. Evaluation of the Generalizability of Machine Learning-Assisted Protein Engineering Methods (Doctoral dissertation, California Institute of Technology).
85. Emma, L., 2024. Explainable AI for High-Stakes Decision Making in Healthcare.
2. Cao, D., Zhang, P. and Wang, S., 2024. Advances in structure-based drug design: the potential for precision therapeutics in psychiatric disorders. Neuron, 112(4), pp.526-538.
3. Soni, M. and Pratap, J.V., 2022. Development of novel anti-leishmanials: the case for structure-based approaches. Pathogens, 11(8), p.950.
4. Singh, S., Kumar, R., Payra, S. and Singh, S.K., 2023. Artificial intelligence and machine learning in pharmacological research: bridging the gap between data and drug discovery. Cureus, 15(8).
5. Gupta, R., Srivastava, D., Sahu, M., Tiwari, S., Ambasta, R.K. and Kumar, P., 2021. Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Molecular diversity, 25(3), pp.1315-1360.
6. Selvaraj, C., Chandra, I. and Singh, S.K., 2022. Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries. Molecular diversity, 26(3), pp.1893-1913.
7. Goshisht, M.K., 2024. Machine learning and deep learning in synthetic biology: Key architectures, applications, and challenges. ACS omega, 9(9), pp.9921-9945.
8. Mienye, I.D. and Swart, T.G., 2024. A comprehensive review of deep learning: Architectures, recent advances, and applications. Information, 15(12), p.755.
9. Odah, M., 2025. Artificial Intelligence Meets Drug Discovery: A Systematic Review on AI-Powered Target Identification and Molecular Design.
10. Utti, V., Bikias, T., Agarwal, A.A., Bikia, V., Zhou, A.Y., Shah, M.M. and Daneshjou, R., 2025. Artificial Intelligence in Dermatology Research and Drug Discovery. Dermatologic Clinics.
11. Pal, S., Kaushik, A., Goel, F., Pal, A. and Garg, V.K., 2025. Revolutionizing Early Diagnosis: The Role of Artificial Intelligence in Neurodegenerative Disorders. Journal of Advanced Scientific Research, 16(03), pp.31-37.
12. Nuka, S.T., Chakilam, C., Chava, K., Suura, S.R. and Recharla, M., 2025. AI-Driven Drug Discovery: Transforming Neurological and Neurodegenerative Disease Treatment Through Bioinformatics and Genomic Research. American Journal of Psychiatric Rehabilitation, 28(1), pp.124-135.
13. Thompson, M. and Bennett, J., 2025. The Impact of AI-Powered Diagnostics on Early Detection of Diseases: Lessons Learned from the COVID-19 Pandemic.
14. Chen, W., Liu, X., Zhang, S. and Chen, S., 2023. Artificial intelligence for drug discovery: Resources, methods, and applications. Molecular therapy Nucleic acids, 31, pp.691-702.
15. Mirakhori, F. and Niazi, S.K., 2025. Harnessing the AI/ML in drug and biological products discovery and development: the regulatory perspective. Pharmaceuticals, 18(1), p.47.
16. Gupta, R., Srivastava, D., Sahu, M., Tiwari, S., Ambasta, R.K. and Kumar, P., 2021. Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Molecular diversity, 25(3), pp.1315-1360.
17. Marques, L., Costa, B., Pereira, M., Silva, A., Santos, J., Saldanha, L., Silva, I., Magalhães, P., Schmidt, S. and Vale, N., 2024. Advancing precision medicine: a review of innovative in silico approaches for drug development, clinical pharmacology and personalized healthcare. Pharmaceutics, 16(3), p.332.
18. Shah, M., Patel, M., Shah, M., Patel, M. and Prajapati, M., 2024. Computational transformation in drug discovery: A comprehensive study on molecular docking and quantitative structure activity relationship (QSAR). Intelligent Pharmacy, 2(5), pp.589-595.
19. Achary, P.G., 2020. Applications of quantitative structure-activity relationships (QSAR) based virtual screening in drug design: a review. Mini Reviews in Medicinal Chemistry, 20(14), pp.1375-1388.
20. Qiu, Y. and Wei, G.W., 2023. Artificial intelligence-aided protein engineering: from topological data analysis to deep protein language models. Briefings in Bioinformatics, 24(5), p.bbad289.
21. Ru, X., Xu, L., Han, W. and Zou, Q., 2025. In silico methods for drug-target interaction prediction. Cell Reports Methods, 5(10).
22. Abdollahi, S., Schaub, D.P., Barroso, M., Laubach, N.C., Hutwelker, W., Panzer, U., Gersting, S.Ø.W. and Bonn, S., 2024. A comprehensive comparison of deep learning-based compound-target interaction prediction models to unveil guiding design principles. Journal of Cheminformatics, 16(1), p.118.
23. Abdelkader, G.A. and Kim, J.D., 2024. Advances in protein-ligand binding affinity prediction via deep learning: a comprehensive study of datasets, data preprocessing techniques, and model architectures. Current drug targets, 25(15), pp.1041-1065.
24. Szczepski, K. and Jaremko, Ł., 2025. AlphaFold and what is next: bridging functional, systems and structural biology. Expert Review of Proteomics, 22(2), pp.45-58.
25. Lyu, J., Kapolka, N., Gumpper, R., Alon, A., Wang, L., Jain, M.K., Barros-Álvarez, X., Sakamoto, K., Kim, Y., DiBerto, J. and Kim, K., 2024. AlphaFold2 structures template ligand discovery. BioRxiv, pp.2023-12.
26. Albani, F.G., Alghamdi, S.S., Almutairi, M.M. and Alqahtani, T., 2025. Artificial Intelligence-Driven Innovations in Oncology Drug Discovery: Transforming Traditional Pipelines and Enhancing Drug Design. Drug Design, Development and Therapy, pp.5685-5707.
27. Gangwal, A., Ansari, A., Ahmad, I., Azad, A.K., Kumarasamy, V., Subramaniyan, V. and Wong, L.S., 2024. Generative artificial intelligence in drug discovery: basic framework, recent advances, challenges, and opportunities. Frontiers in pharmacology, 15, p.1331062.
28. Sucharitha, P., Reddy, K.R., Satyanarayana, S.V. and Garg, T., 2022. Absorption, distribution, metabolism, excretion, and toxicity assessment of drugs using computational tools. In Computational approaches for novel therapeutic and diagnostic designing to mitigate SARS-CoV-2 infection (pp. 335-355). Academic Press.
29. Ullah, I., Rios, A., Gala, V. and Mckeever, S., 2021. Explaining deep learning models for tabular data using layer-wise relevance propagation. Applied Sciences, 12(1), p.136.
30. Pramudito, M.A., Fuadah, Y.N., Qauli, A.I., Marcellinus, A. and Lim, K.M., 2024. Explainable artificial intelligence (XAI) to find optimal in-silico biomarkers for cardiac drug toxicity evaluation. Scientific Reports, 14(1), p.24045.
31. Goryanin, I., Goryanin, I. and Demin, O., 2025. Revolutionizing drug discovery: Integrating artificial intelligence with quantitative systems pharmacology. Drug Discovery Today, p.104448.
32. Mondal, A., Chang, L. and Perez, A., 2022. Modelling peptide–protein complexes: docking, simulations and machine learning. QRB discovery, 3, p.e17.
33. Staszak, M., Staszak, K., Wieszczycka, K., Bajek, A., Roszkowski, K. and Tylkowski, B., 2022. Machine learning in drug design: Use of artificial intelligence to explore the chemical structure–biological activity relationship. Wiley Interdisciplinary Reviews: Computational Molecular Science, 12(2), p.e1568.
34. Mysore, V., Patel, N. and Ojewole, A., 2024. Deep Learning for the Structure‐Based Binding Free Energy Prediction of Small Molecule Ligands. Computational Drug Discovery: Methods and Applications, 1, pp.255-273.
35. Aburass, S., Dorgham, O. and Al Shaqsi, J., 2024. A hybrid machine learning model for classifying gene mutations in cancer using LSTM, BiLSTM, CNN, GRU, and GloVe. Systems and Soft Computing, 6, p.200110.
36. Mienye, I.D., Swart, T.G. and Obaido, G., 2024. Recurrent neural networks: A comprehensive review of architectures, variants, and applications. Information, 15(9), p.517.
37. Grisoni, F., 2023. Chemical language models for de novo drug design: Challenges and opportunities. Current Opinion in Structural Biology, 79, p.102527.
38. Kumar, A. and Karki, S.S., 2025. AI & Machine Learning in Lead Discovery: Deep-Learning Architectures for de novo Design, Property Prediction and Inverse QSAR. molecules, 6, p.8.
39. Sheshanarayana, R. and You, F., 2025. Molecular representation learning: cross-domain foundations and future Frontiers. Digital Discovery.
40. Zhang, L., Ouyang, C., Liu, Y., Liao, Y. and Gao, Z., 2023. Multimodal contrastive representation learning for drug-target binding affinity prediction. Methods, 220, pp.126-133.
41. Zeng, X., Li, S.J., Lv, S.Q., Wen, M.L. and Li, Y., 2024. A comprehensive review of the recent advances on predicting drug-target affinity based on deep learning. Frontiers in Pharmacology, 15, p.1375522.
42. Chatzianastasis, M., 2025. Advancements in Graph Representation Learning and Applications in Computational Biology (Doctoral dissertation, Institut Polytechnique de Paris).
43. Chen, J.Y., Wang, J.F., Hu, Y., Li, X.H., Qian, Y.R. and Song, C.L., 2025. Evaluating the advancements in protein language models for encoding strategies in protein function prediction: a comprehensive review. Frontiers in bioengineering and biotechnology, 13, p.1506508.
44. Mswahili, M.E. and Jeong, Y.S., 2024. Transformer-based models for chemical SMILES representation: A comprehensive literature review. Heliyon, 10(20).
45. Kuang, T., Liu, P. and Ren, Z., 2024. Impact of Domain Knowledge and Multi-Modality on Intelligent Molecular Property Prediction: A Systematic Survey. Big Data Mining and Analytics, 7(3), pp.858-888.
46. Vidanagamachchi, S.M. and Waidyarathna, K.M.G.T.R., 2024. Opportunities, challenges and future perspectives of using bioinformatics and artificial intelligence techniques on tropical disease identification using omics data. Frontiers in Digital Health, 6, p.1471200.
47. Lavecchia, A., 2025. Transform drug discovery and development with generative artificial intelligence. Generative Artificial Intelligence for Biomedical and Smart Health Informatics, pp.489-537.
48. Su, X., Hu, P., Li, D., Zhao, B., Niu, Z., Herget, T., Yu, P.S. and Hu, L., 2025. Interpretable identification of cancer genes across biological networks via transformer-powered graph representation learning. Nature biomedical engineering, 9(3), pp.371-389.
49. Choi, S.R. and Lee, M., 2023. Transformer architecture and attention mechanisms in genome data analysis: a comprehensive review. Biology, 12(7), p.1033.
50. Wang, M., Li, W., Yu, X., Luo, Y., Han, K., Wang, C. and Jin, Q., 2023. AffinityVAE: a multi-objective model for protein-ligand affinity prediction and drug design. Computational Biology and Chemistry, 107, p.107971.
51. Li, S., Zhou, J., Xu, T., Huang, L., Wang, F., Xiong, H., Huang, W., Dou, D. and Xiong, H., 2023. Giant: Protein-ligand binding affinity prediction via geometry-aware interactive graph neural network. IEEE Transactions on Knowledge and Data Engineering, 36(5), pp.1991-2008.
52. Kumar, N. and Acharya, V., 2024. Advances in machine intelligence‐driven virtual screening approaches for big‐data. Medicinal Research Reviews, 44(3), pp.939-974.
53. Cui, J., Yang, S., Yi, L., Xi, Q., Yang, D. and Zuo, Y., 2025. Recent advances in deep learning for protein-protein interaction: a review. BioData Mining, 18(1), p.43.
54. de Angelo, R.M., de Sousa, D.S., da Silva, A.P., Chiari, L.P., da Silva, A.B. and Honorio, K.M., 2025. Molecular Docking: State-of-the-Art Scoring Functions and Search Algorithms. In Computer-Aided and Machine Learning-Driven Drug Design: From Theory to Applications (pp. 163-198). Cham: Springer Nature Switzerland.
55. da Silva, M.M.P., Guedes, I.A., Custódio, F.L., da Silva, E.K. and Dardenne, L.E., 2024. Deep learning strategies for enhanced molecular docking and virtual screening. In Structure-Based Drug Design (pp. 177-221). Cham: Springer International Publishing.
56. Ahmad, M. and Teli, T.A., 2024. De Novo Molecular Generation Augmentation for Drug Discovery Using Deep Learning Approaches: A Comparative Study of Variational Autoencoders. Integrative Biomedical Research, 8(10), pp.1-13.
57. Su, Y.Y., Huang, H.C., Lin, Y.T., Chuang, Y.F., Sheu, S.Y. and Lin, C.C., 2025. HEDDI-Net: heterogeneous network embedding for drug-disease association prediction and drug repurposing, with application to Alzheimer’s disease. Journal of Translational Medicine, 23(1), p.57.
58. Jia, L. and Gao, H., 2021. Machine Learning for in silico ADMET prediction. In Artificial Intelligence in Drug Design (pp. 447-460). New York, NY: Springer US.
59. Swanson, K., Walther, P., Leitz, J., Mukherjee, S., Wu, J.C., Shivnaraine, R.V. and Zou, J., 2024. ADMET-AI: a machine learning ADMET platform for evaluation of large-scale chemical libraries. Bioinformatics, 40(7), p.btae416.
60. Robert, K., Kaium, O. and Alasa, L., 2025. Generative AI and Pharmaceutical Innovation: Accelerating Drug Discovery with Deep Learning and Predictive Analytics.
61. Ünlü, A., Ulusoy, E., Yiğit, M.G., Darcan, M. and Doğan, T., 2025. Protein language models for predicting drug–target interactions: Novel approaches, emerging methods, and future directions. Current Opinion in Structural Biology, 91, p.103017.
62. Zdrazil, B., 2025. Fifteen years of ChEMBL and its role in cheminformatics and drug discovery. Journal of Cheminformatics, 17(1), pp.1-9.
63. Santos, K.B., Guedes, I.A., Karl, A.L. and Dardenne, L.E., 2020. Highly flexible ligand docking: Benchmarking of the DockThor program on the LEADS-PEP protein–peptide data set. Journal of Chemical Information and Modeling, 60(2), pp.667-683.
64. Ji, Y., Zhang, L., Wu, J., Wu, B., Li, L., Huang, L.K., Xu, T., Rong, Y., Ren, J., Xue, D. and Lai, H., 2023, June. Drugood: Out-of-distribution dataset curator and benchmark for ai-aided drug discovery–a focus on affinity prediction problems with noise annotations. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 37, No. 7, pp. 8023-8031).
65. Basu, S. and Wallner, B., 2016. DockQ: a quality measure for protein-protein docking models. PloS one, 11(8), p.e0161879.
66. Collins, K.W., Copeland, M.M., Brysbaert, G., Wodak, S.J., Bonvin, A.M., Kundrotas, P.J., Vakser, I.A. and Lensink, M.F., 2024. CAPRI-Q: The CAPRI resource evaluating the quality of predicted structures of protein complexes. Journal of molecular biology, 436(17), p.168540.
67. Preijers, T., Muller, A.E., Abdulla, A., de Winter, B.C., Koch, B.C. and Sassen, S.D., 2024. Dose individualisation of antimicrobials from a pharmacometric standpoint: the current landscape. Drugs, 84(10), pp.1167-1178.
68. Damaševičius, R., Jagatheesaperumal, S.K., Kandala, R.N., Hussain, S., Alizadehsani, R. and Gorriz, J.M., 2024. Deep learning for personalized health monitoring and prediction: A review. Computational Intelligence, 40(3), p.e12682.
69. Fatunmbi, T.O., 2024. Predicting precision-based treatment plans using artificial intelligence and machine learning in complex medical scenarios.
70. Geaney, A., O’Reilly, P., Maxwell, P., James, J.A., McArt, D. and Salto-Tellez, M., 2023. Translation of tissue-based artificial intelligence into clinical practice: from discovery to adoption. Oncogene, 42(48), pp.3545-3555.
71. Sengupta, S., Maji, L., Das, P.K., Teli, G., Nag, M., Khan, N., Haque, M. and Matada, G.S.P., 2025. Explanatory review on DDR inhibitors: their biological activity, synthetic route, and structure–activity relationship. Molecular Diversity, pp.1-31.
72. Niazi, S.K., 2025. Artificial Intelligence in Small-Molecule Drug Discovery: A Critical Review of Methods, Applications, and Real-World Outcomes. Pharmaceuticals, 18(9), p.1271.
73. Uzoeto, H.O., Cosmas, S., Bakare, T.T. and Durojaye, O.A., 2024. AlphaFold-latest: revolutionizing protein structure prediction for comprehensive biomolecular insights and therapeutic advancements. Beni-Suef University Journal of Basic and Applied Sciences, 13(1), p.46.
74. Warr, W., 2021. National Institutes of Health (NIH) Workshop on Reaction Informatics.
75. Tan, Z., Zhao, Y., Zhou, T. and Lin, K., 2023. Hi-MGT: a hybrid molecule graph transformer for toxicity identification. Journal of Hazardous Materials, 457, p.131808.
76. Chitsazi, R., Wu, Y., GPCR Dock 2021 participants, Stevens, R.C., Zhao, S. and Kufareva, I., 2025. The 4th GPCR Dock: assessment of blind predictions for GPCR-ligand complexes in the era of AlphaFold. bioRxiv, pp.2025-04.
77. Lavecchia, A., 2025. Explainable Artificial Intelligence in Drug Discovery: Bridging Predictive Power and Mechanistic Insight. Wiley Interdisciplinary Reviews: Computational Molecular Science, 15(5), p.e70049.
78. An, G., 2022. Specialty grand challenge: What it will take to cross the valley of death: Translational systems biology,“true” precision medicine, medical digital twins, artificial intelligence and in silico clinical trials. Frontiers in Systems Biology, 2, p.901159.
79. John, A., Qin, B., Kalari, K.R., Wang, L. and Yu, J., 2020. Patient-specific multi-omics models and the application in personalized combination therapy. Future Oncology, 16(23), pp.1737-1750.
80. Núñez Carpintero, I., 2023. Network-based methods for biological data integration in precision medicine.
81. Taha, K., 2024. Employing Machine Learning Techniques to Detect Protein Function: A Survey, Experimental, and Empirical Evaluations. IEEE/ACM Transactions on Computational Biology and Bioinformatics.
82. Brancato, V., Esposito, G., Coppola, L., Cavaliere, C., Mirabelli, P., Scapicchio, C., Borgheresi, R., Neri, E., Salvatore, M. and Aiello, M., 2024. Standardizing digital biobanks: integrating imaging, genomic, and clinical data for precision medicine. Journal of translational medicine, 22(1), p.136.
83. Ballard, J.L., Wang, Z., Li, W., Shen, L. and Long, Q., 2024. Deep learning-based approaches for multi-omics data integration and analysis. BioData Mining, 17(1), p.38.
84. Li, F.Z., 2025. Evaluation of the Generalizability of Machine Learning-Assisted Protein Engineering Methods (Doctoral dissertation, California Institute of Technology).
85. Emma, L., 2024. Explainable AI for High-Stakes Decision Making in Healthcare.
Article Sidebar
Published
Oct 25, 2025
Article Details
How to Cite
L. Sowjanya Upadhyayula, Mallu Mallikarjuna, Madduri Padmavathamma, & GV Ramesh Babu. (2025). AI AND DEEP LEARNING IN DRUG DISCOVERY: ADVANCES, BENCHMARKS, AND FUTURE CHALLENGES. Journal of Population Therapeutics and Clinical Pharmacology, 32(9), 1532-1551. https://doi.org/10.53555/3cf6ss76
Section
Articles
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
All articles published in JPTCP are licensed under Copyright Creative Commons Attribution-NonCommercial 4.0 International License.
Author Biographies
L. Sowjanya Upadhyayula
Department of Computer Sciences, Sri Venkateswara University, Tirupati, Andhra Pradesh, India.
Mallu Mallikarjuna
Department of Virology, Sri Venkateswara University, Tirupati, Andhra Pradesh, India.
Madduri Padmavathamma
Department of Pharmacy, Sri Padmavathi Women’s Polytechnic College, Tirupati, Andhra Pradesh. India,
GV Ramesh Babu
Department of Computer Sciences, Sri Venkateswara University, Tirupati, Andhra Pradesh, India.
How to Cite
L. Sowjanya Upadhyayula, Mallu Mallikarjuna, Madduri Padmavathamma, & GV Ramesh Babu. (2025). AI AND DEEP LEARNING IN DRUG DISCOVERY: ADVANCES, BENCHMARKS, AND FUTURE CHALLENGES. Journal of Population Therapeutics and Clinical Pharmacology, 32(9), 1532-1551. https://doi.org/10.53555/3cf6ss76
