Paul SM, Mytelka DS, Dunwiddie CT, et al. How to improve R&D productivity: the pharmaceutical industry’s grand challenge. Nat Rev Drug Discov. 2010;9(3):203–14. https://doi.org/10.1038/nrd3078.
Schuhmacher A, Hinder M, von Stegmann Und Stein A, et al. Analysis of pharma R&D productivity—a new perspective needed. Drug Discov Today. 2023;28(10):103726. https://doi.org/10.1016/j.drudis.2023.103726.
Dowden H, Munro J. Trends in clinical success rates and therapeutic focus. Nat Rev Drug Discov. 2019;18(7):495–6. https://doi.org/10.1038/d41573-019-00074-z.
Sun D, Gao W, Hu H, et al. Why 90% of clinical drug development fails and how to improve it? Acta Pharm Sin B. 2022;12(07):3049–62. https://doi.org/10.1016/j.apsb.2022.02.002.
Article PubMed PubMed Central Google Scholar
Bender A, Cortés-Ciriano I. Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 1: ways to make an impact, and why we are not there yet. Drug Discov Today. 2021;26(2):511–24. https://doi.org/10.1016/j.drudis.2020.12.009.
Chen EP, Bondi RW, Michalski PJ. Model-based target pharmacology assessment (mTPA): an approach using PBPK/PD modeling and machine learning to design medicinal chemistry and DMPK strategies in early drug discovery. J Med Chem. 2021;64(6):3185–96. https://doi.org/10.1021/acs.jmedchem.0c02033.
Niazi SK. The coming of age of AI/ML in drug discovery, development, clinical testing, and manufacturing: the FDA perspectives. Drug Des Devel Ther. 2023;17:2691–725. https://doi.org/10.2147/DDDT.S424991.
Article PubMed PubMed Central Google Scholar
You Y, Lai X, Pan Y, et al. Artificial intelligence in cancer target identification and drug discovery. Signal Transduct Target Ther. 2022;7(1):156. https://doi.org/10.1038/s41392-022-00994-0.
Article PubMed PubMed Central Google Scholar
Kim H, Kim E, Lee I, et al. Artificial intelligence in drug discovery: a comprehensive review of data-driven and machine learning approaches. Biotechnol Bioprocess Eng. 2020;25(6):895–930. https://doi.org/10.1007/s12257-020-0049-y.
Hasankhani A, Bahrami A, Sheybani N, et al. Differential co-expression network analysis reveals key hub-high traffic genes as potential therapeutic targets for COVID-19 pandemic. Front Immunol. 2021;12: 789317. https://doi.org/10.3389/fimmu.2021.789317.
Article PubMed PubMed Central Google Scholar
Kothari C, Osseni MA, Agbo L, et al. Machine learning analysis identifies genes differentiating triple negative breast cancers. Sci Rep. 2020;10(1):10464. https://doi.org/10.1038/s41598-020-67525-1.
Article PubMed PubMed Central Google Scholar
Pal S, Bhattacharya M, Islam MA, et al. ChatGPT or LLM in next-generation drug discovery and development: pharmaceutical and biotechnology companies can make use of the artificial intelligence (AI)-based device for a faster way of drug discovery and development. Int J Surg. 2023;109(12):4382–4. https://doi.org/10.1097/JS9.0000000000000719.
Article PubMed PubMed Central Google Scholar
Yang J, Walker KC, Bekar-Cesaretli AA, et al. Automating biomedical literature review for rapid drug discovery: leveraging GPT-4 to expedite pandemic response. Int J Med Inform. 2024. https://doi.org/10.1016/j.ijmedinf.2024.105500.
Savage N. Drug discovery companies are customizing ChatGPT: here’s how. Nat Biotechnol. 2023;41(5):585–6. https://doi.org/10.1038/s41587-023-01788-7.
Chen EP, Bondi RW, Zhang C, et al. Applications of model-based target pharmacology assessment in defining drug design and DMPK strategies: GSK experiences. J Med Chem. 2022;65(9):6926–39. https://doi.org/10.1021/acs.jmedchem.2c00330.
Yoo J, Kim TY, Joung I, Song SO. Industrializing AI/ML during the end-to-end drug discovery process. Curr Opin Struct Biol. 2023;79: 102528. https://doi.org/10.1016/j.sbi.2023.102528.
Pun FW, Liu BHM, Long X, et al. Identification of therapeutic targets for amyotrophic lateral sclerosis using PandaOmics—an AI-enabled biological target discovery platform. Front Aging Neurosci. 2022;14: 914017. https://doi.org/10.3389/fnagi.2022.914017.
Article PubMed PubMed Central Google Scholar
Kate A, Seth E, Singh A, et al. Artificial intelligence for computer-aided drug discovery. Drug Res (Stuttg). 2023;73(7): e2. https://doi.org/10.1055/a-2105-9762.
Vatansever S, Schlessinger A, Wacker D, et al. Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: state-of-the-arts and future directions. Med Res Rev. 2021;41(3):1427–73. https://doi.org/10.1002/med.21764.
Lv Q, Zhou F, Liu X, et al. Artificial intelligence in small molecule drug discovery from 2018 to 2023: does it really work? Bioorg Chem. 2023;141: 106894. https://doi.org/10.1016/j.bioorg.2023.106894.
Liu G, Catacutan DB, Rathod K, et al. Deep learning-guided discovery of an antibiotic targeting Acinetobacter baumannii. Nat Chem Biol. 2023;19(11):1342–50. https://doi.org/10.1038/s41589-023-01349-8.
Wadood A, Ajmal A, Junaid M, et al. Machine learning-based virtual screening for STAT3 anticancer drug target. Curr Pharm Des. 2022;28(36):3023–32. https://doi.org/10.2174/1381612828666220728120523.
Shen C, Hu Y, Wang Z, et al. Can machine learning consistently improve the scoring power of classical scoring functions? Insights into the role of machine learning in scoring functions. Brief Bioinform. 2021;22(1):497–514.
Shen C, Hu X, Gao J, et al. The impact of cross-docked poses on performance of machine learning classifier for protein-ligand binding pose prediction. J Cheminform. 2021;13(1):81. https://doi.org/10.1186/s13321-021-00560-w.
Article PubMed PubMed Central Google Scholar
Jiang D, Wu Z, Hsieh CY, et al. Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models. J Cheminform. 2021;13(1):12. https://doi.org/10.1186/s13321-020-00479-8.
Article PubMed PubMed Central Google Scholar
Vijayan RSK, Kihlberg J, Cross JB, et al. Enhancing preclinical drug discovery with artificial intelligence. Drug Discov Today. 2022;27(4):967–84. https://doi.org/10.1016/j.drudis.2021.11.023.
Godinez WJ, Ma EJ, Chao AT, et al. Design of potent antimalarials with generative chemistry. Nat Mach Intell. 2022;4(2):180–6. https://doi.org/10.1038/s42256-022-00448-w.
Chen EP, Dutta S, Ho MH, et al. Model-based virtual PK/PD exploration and machine learning approach to define PK drivers in early drug discovery. J Med Chem. 2024;67(5):3727–40. https://doi.org/10.1021/acs.jmedchem.3c02169.
Khan SR, Al Rijjal D, Piro A, et al. Integration of AI and traditional medicine in drug discovery. Drug Discov Today. 2021;26(4):982–92. https://doi.org/10.1016/j.drudis.2021.01.008.
Singh S, Kumar R, Payra S, et al. Artificial intelligence and machine learning in pharmacological research: bridging the gap between data and drug discovery. Cureus. 2023;15(8): e44359. https://doi.org/10.7759/cureus.44359.
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