Pharma’s Bio-AI revolution

Artificial intelligence in healthcare: past, present and future

Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y. (2017) · Stroke and Vascular Neurology · doi:10.1136/svn-2017-000101 · Cited by 4,837 · see citing works

A Landscape of Pharmacogenomic Interactions in Cancer

Iorio, F., Knijnenburg, T. A., Vis, D. J., Bignell, G. R., Menden, M. P., Schubert, M., Aben, N., Gonçalves, E., Barthorpe, S., Lightfoot, H., et al. (2016) · Cell · doi:10.1016/j.cell.2016.06.017 · Cited by 2,090 · see citing works

Precision medicine, AI, and the future of personalized health care

Johnson, K. B., Wei, W., Weeraratne, D., Frisse, M. E., Misulis, K., Rhee, K., Zhao, J., & Snowdon, J. L. (2021) · Clinical and Translational Science · doi:10.1111/cts.12884 · Cited by 1,854 · see citing works

The rise of deep learning in drug discovery

Chen, H., Engkvist, O., Wang, Y., Olivecrona, M., & Blaschke, T. (2018) · Drug Discovery Today · doi:10.1016/j.drudis.2018.01.039 · Cited by 1,770 · see citing works

Deep learning enables rapid identification of potent DDR1 kinase inhibitors

Zhavoronkov, A., Ivanenkov, Y. A., Aliper, A., Veselov, M. S., Aladinskiy, V. A., Aladinskaya, A. V., Terentiev, V. A., Polykovskiy, D. A., Kuznetsov, M. D., Asadulaev, A., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0224-x · Cited by 1,573 · see citing works

Estimated Research and Development Investment Needed to Bring a New Medicine to Market, 2009-2018

Wouters, O. J., McKee, M., & Luyten, J. (2020) · JAMA · doi:10.1001/jama.2020.1166 · Cited by 1,531 · see citing works

Lessons learned from the fate of AstraZeneca's drug pipeline: a five-dimensional framework

Cook, D., Brown, D., Alexander, R., March, R., Morgan, P., Satterthwaite, G., & Pangalos, M. N. (2014) · Nature Reviews Drug Discovery · doi:10.1038/nrd4309 · Cited by 1,344 · see citing works

Organs-on-chips: into the next decade

Low, L. A., Mummery, C., Berridge, B. R., Austin, C. P., & Tagle, D. A. (2021) · Nature Reviews Drug Discovery · doi:10.1038/s41573-020-0079-3 · Cited by 1,029 · see citing works

Contributions of microbiome and mechanical deformation to intestinal bacterial overgrowth and inflammation in a human gut-on-a-chip

Kim, H. J., Li, H., Collins, J. J., & Ingber, D. E. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1522193112 · Cited by 926 · see citing works

Trends in Risks Associated With New Drug Development: Success Rates for Investigational Drugs

DiMasi, J. A., Feldman, L., Seckler, A., & Wilson, A. (2010) · Clinical Pharmacology & Therapeutics · doi:10.1038/clpt.2009.295 · Cited by 847 · see citing works

Addressing bias in artificial intelligence in health care

Parikh, R. B., Teeple, S., & Navathe, A. S. (2019) · JAMA · doi:10.1001/jama.2019.18058 · Cited by 764 · see citing works

Gut-on-a-Chip microenvironment induces human intestinal cells to undergo villus differentiation

Kim, H. J., & Ingber, D. E. (2013) · Integrative Biology · doi:10.1039/c3ib40126j · Cited by 750 · see citing works

Pluripotent stem cells in disease modelling and drug discovery

Avior, Y., Sagi, I., & Benvenisty, N. (2016) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm.2015.27 · Cited by 592 · see citing works

Exploiting machine learning for end-to-end drug discovery and development

Ekins, S., Puhl, A. C., Zorn, K. M., Lane, T. R., Russo, D. P., Klein, J. J., Hickey, A. J., & Clark, A. M. (2019) · Nature Materials · doi:10.1038/s41563-019-0338-z · Cited by 578 · see citing works

Artificial Intelligence and Personalized Medicine

Schork, N. J. (2019) · Cancer treatment and research · doi:10.1007/978-3-030-16391-4_11 · Cited by 518 · see citing works

Quantitative prediction of human pharmacokinetic responses to drugs via fluidically coupled vascularized organ chips

Herland, A., Maoz, B. M., Das, D., Somayaji, M. R., Prantil‐Baun, R., Novak, R., Cronce, M. J., Huffstater, T. M., Jeanty, S. S. F., Ingram, M., et al. (2020) · Nature Biomedical Engineering · doi:10.1038/s41551-019-0498-9 · Cited by 505 · see citing works

Artificial intelligence in drug discovery: recent advances and future perspectives

Jiménez-Luna, J., Grisoni, F., Weskamp, N., & Schneider, G. (2021) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2021.1909567 · Cited by 464 · see citing works

Robotic fluidic coupling and interrogation of multiple vascularized organ chips

Novak, R., Ingram, M., Marquez, S. L., Das, D., Delahanty, A., Herland, A., Maoz, B. M., Jeanty, S. S. F., Somayaji, M. R., Burt, M. A., et al. (2020) · Nature Biomedical Engineering · doi:10.1038/s41551-019-0497-x · Cited by 444 · see citing works

Organ/body-on-a-chip based on microfluidic technology for drug discovery

Kimura, H., Sakai, Y., & Fujii, T. (2018) · Drug Metabolism and Pharmacokinetics · doi:10.1016/j.dmpk.2017.11.003 · Cited by 435 · see citing works

Role of Artificial Intelligence Applications in Real-Life Clinical Practice: Systematic Review

Yin, J., Ngiam, K. Y., & Teo, H. H. (2021) · Journal of Medical Internet Research · doi:10.2196/25759 · Cited by 419 · see citing works

Economics of New Oncology Drug Development

DiMasi, J. A., & Grabowski, H. G. (2007) · Journal of Clinical Oncology · doi:10.1200/jco.2006.09.0803 · Cited by 348 · see citing works

Utility of spherical human liver microtissues for prediction of clinical drug-induced liver injury

Proctor, W. R., Foster, A. J., Vogt, J., Summers, C., Middleton, B., Pilling, M. A., Shienson, D., Kijanska, M., Ströbel, S., Kelm, J. M., et al. (2017) · Archives of Toxicology · doi:10.1007/s00204-017-2002-1 · Cited by 328 · see citing works

A Decade of FDA-Approved Drugs (2010–2019): Trends and Future Directions

Brown, D. G., & Wobst, H. J. (2021) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.0c01516 · Cited by 326 · see citing works

Mitigating bias in machine learning for medicine

Vokinger, K. N., Feuerriegel, S., & Kesselheim, A. S. (2021) · Communications Medicine · doi:10.1038/s43856-021-00028-w · Cited by 269 · see citing works

Automated, high-throughput derivation, characterization and differentiation of induced pluripotent stem cells

Paull, D., Sevilla, A., Zhou, H., Hahn, A. K., Kim, H., Napolitano, C., Tsankov, A., Shang, L., Krumholz, K., Jagadeesan, P., et al. (2015) · Nature Methods · doi:10.1038/nmeth.3507 · Cited by 263 · see citing works

Deciphering common failures in molecular docking of ligand-protein complexes

Verkhivker, G. M., Bouzida, D., Gehlhaar, D. K., Rejto, P. A., Arthurs, S., Colson, A. B., Freer, S. T., Larson, V., Luty, B. A., Marrone, T. J., et al. (2000) · Journal of Computer-Aided Molecular Design · doi:10.1023/a:1008158231558 · Cited by 240 · see citing works

The Next Era: Deep Learning in Pharmaceutical Research

Ekins, S. (2016) · Pharmaceutical Research · doi:10.1007/s11095-016-2029-7 · Cited by 237 · see citing works

Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 2: a discussion of chemical and biological data

Bender, A., & Cortes-Ciriano, I. (2021) · Drug Discovery Today · doi:10.1016/j.drudis.2020.11.037 · Cited by 232 · see citing works

A human liver microphysiology platform for investigating physiology, drug safety, and disease models

Vernetti, L. A., Senutovitch, N., Boltz, R. C., DeBiasio, R., Shun, T. Y., Gough, A., & Taylor, D. L. (2015) · Experimental Biology and Medicine · doi:10.1177/1535370215592121 · Cited by 230 · see citing works

Duodenum Intestine-Chip for preclinical drug assessment in a human relevant model

Kasendra, M. J., Luc, R., Yin, J., Manatakis, D. V., Kulkarni, G., Lucchesi, C., Sliz, J. D., Apostolou, A. P., Sunuwar, L., Obrigewitch, J., et al. (2020) · eLife · doi:10.7554/elife.50135 · Cited by 227 · see citing works

Enhancing preclinical drug discovery with artificial intelligence

Vijayan, R., Kihlberg, J. O., Cross, J. B., & Poongavanam, V. (2021) · Drug Discovery Today · doi:10.1016/j.drudis.2021.11.023 · Cited by 185 · see citing works

An analysis of the use of animal models in predicting human toxicology and drug safety

Bailey, J., Thew, M., & Balls, M. (2014) · Alternatives to Laboratory Animals · doi:10.1177/026119291404200306 · Cited by 182 · see citing works

Clinical development times for innovative drugs

Brown, D. G., Wobst, H. J., Kapoor, A., Kenna, L. A., & Southall, N. T. (2021) · Nature Reviews Drug Discovery · doi:10.1038/d41573-021-00190-9 · Cited by 176 · see citing works

Chemistry-First Approach for Nomination of Personalized Treatment in Lung Cancer

McMillan, E. A., Ryu, M.-J., Diep, C. H., Mendiratta, S., Clemenceau, J. R., Vaden, R. M., Kim, J.-H., Motoyaji, T., Covington, K. R., Peyton, M., et al. (2018) · Cell · doi:10.1016/j.cell.2018.03.028 · Cited by 141 · see citing works

A glass-based, continuously zonated and vascularized human liver acinus microphysiological system (vLAMPS) designed for experimental modeling of diseases and ADME/TOX

Li, X., George, S. M., Vernetti, L. A., Gough, A., & Taylor, D. L. (2018) · Lab on a Chip · doi:10.1039/c8lc00418h · Cited by 138 · see citing works

2021 FDA approvals

Mullard, A. (2022) · Nature Reviews Drug Discovery · doi:10.1038/d41573-022-00001-9 · Cited by 128 · see citing works

An Overview of Organs-on-Chips Based on Deep Learning

Li, J., Chen, J., Bai, H., Wang, H., Hao, S., Ding, Y., Peng, B., Zhang, J., Li, L., & Huang, W. (2022) · Research · doi:10.34133/2022/9869518 · Cited by 113 · see citing works

Chemistry in Times of Artificial Intelligence

Gasteiger, J. (2020) · ChemPhysChem · doi:10.1002/cphc.202000518 · Cited by 98 · see citing works

Robotic high-throughput biomanufacturing and functional differentiation of human pluripotent stem cells

Tristan, C. A., Ormanoglu, P., Slamecka, J., Malley, C., Chu, P.-H., Jovanovic, V. M., Gedik, Y., Jethmalani, Y., Bonney, C., Barnaeva, E., et al. (2021) · Stem Cell Reports · doi:10.1016/j.stemcr.2021.11.004 · Cited by 80 · see citing works

Multi-Organs-on-Chips for Testing Small-Molecule Drugs: Challenges and Perspectives

Çeçen, B., Karavasili, C., Nazir, M., Bhusal, A., Dogan, E., Shahriyari, F., Tamburacı, S., Büyüköz, M., Kozacı, L. D., & Miri, A. K. (2021) · Pharmaceutics · doi:10.3390/pharmaceutics13101657 · Cited by 37 · see citing works

Rationality over fashion and hype in drug design

Medina‐Franco, J. L., Martínez‐Mayorga, K., Gortari, E. F., Kirchmair, J., & Bajorath, J. (2021) · F1000Research · doi:10.12688/f1000research.52676.1 · Cited by 36 · see citing works

Phase-In to Phase-Out—Targeted, Inclusive Strategies Are Needed to Enable Full Replacement of Animal Use in the European Union

Marshall, L. J., Constantino, H., & Seidle, T. (2022) · Animals · doi:10.3390/ani12070863 · Cited by 30 · see citing works

Systematic interrogation of diverse Omic data reveals interpretable, robust, and generalizable transcriptomic features of clinically successful therapeutic targets

Rouillard, A. D., Hurle, M. R., & Agarwal, P. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006142 · Cited by 28 · see citing works

Deep Machine Learning for Computer-Aided Drug Design

Bajorath, J. (2022) · Frontiers in Drug Discovery · doi:10.3389/fddsv.2022.829043 · Cited by 27 · see citing works

Cellular Metabolomics by a Universal Redox-Reactive Nanosensors Array: From the Cell Level to Tumor-on-a-Chip Analysis

Krivitsky, V., Zverzhinetsky, M., Krivitsky, A., Hsiung, L.-C., Naddaka, V. I., Gabriel, I., Lefler, S., Conroy, J., Burstein, L., & Patolsky, F. (2019) · Nano Letters · doi:10.1021/acs.nanolett.9b00052 · Cited by 23 · see citing works

Artificial intelligence can improve patients’ experience in decentralized clinical trials

Thomas, K. A., & Kidziński, Ł. (2022) · Nature Medicine · doi:10.1038/s41591-022-02034-4 · Cited by 21 · see citing works

Current status of clinical research using artificial intelligence techniques

Karekar, S. R., & Vazifdar, A. K. (2021) · Perspectives in Clinical Research · doi:10.4103/picr.picr_25_20 · Cited by 17 · see citing works

Artificial Intelligence for Drug Discovery: Are We There Yet?

Hasselgren, C., & Oprea, T. I. (2024) · Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-040323-040828 · Cited by 236 · see citing works

Tribulations and future opportunities for artificial intelligence in precision medicine

Carini, C., & Seyhan, A. A. (2024) · Journal of Translational Medicine · doi:10.1186/s12967-024-05067-0 · Cited by 175 · see citing works

Organoids: development and applications in disease models, drug discovery, precision medicine, and regenerative medicine

Yao, Q., Cheng, S., Pan, Q., Yu, J., Cao, G., Li, L., & Cao, H. (2024) · MedComm · doi:10.1002/mco2.735 · Cited by 97 · see citing works

The changing scenario of drug discovery using AI to deep learning: Recent advancement, success stories, collaborations, and challenges

Chakraborty, C., Bhattacharya, M., Lee, S., Wen, Z., & Lo, Y.-H. (2024) · Molecular Therapy — Nucleic Acids · doi:10.1016/j.omtn.2024.102295 · Cited by 79 · see citing works

The role of machine learning in predictive toxicology: A review of current trends and future perspectives

Ajisafe, O. M., Adekunle, Y. A., Egbon, E., Ogbonna, C. E., & Olawade, D. B. (2025) · Life Sciences · doi:10.1016/j.lfs.2025.123821 · Cited by 57 · see citing works

The applications and advances of artificial intelligence in drug regulation: A global perspective

Lixia, F., Jia, G., Liu, Z., Pang, X., & Cui, Y. (2024) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2024.11.006 · Cited by 50 · see citing works

Bridging silicon and carbon worlds with digital twins and on-chip systems in drug discovery

Akbarialiabad, H., Seyyedi, M. S., Paydar, S., Habibzadeh, A., Haghighi, A., & Kvedar, J. C. (2024) · npj Systems Biology and Applications · doi:10.1038/s41540-024-00476-9 · Cited by 27 · see citing works

Artificial Intelligence for Organoids Multidimensional Assessment

Mo, Y., Wang, J., Yang, H., Bai, L., Xu, K., & Su, J. (2025) · SmartMat · doi:10.1002/smm2.70016 · Cited by 25 · see citing works

Practical Three-Component Regioselective Synthesis of Drug-Like 3-Aryl(or heteroaryl)-5,6-dihydrobenzo[h]cinnolines as Potential Non-Covalent Multi-Targeting Inhibitors To Combat Neurodegenerative Diseases

Mousavi, H., Rimaz, M., & Zeynizadeh, B. (2024) · ACS Chemical Neuroscience · doi:10.1021/acschemneuro.4c00055 · Cited by 20 · see citing works

Advanced AI Applications for Drug Discovery

Yingngam, B., & Sethabouppha, B. (2024) · Advances in medical technologies and clinical practice book series · doi:10.4018/979-8-3693-2333-5.ch003 · Cited by 11 · see citing works

Novel emerging cell and organoid systems for the study of drug metabolism and toxicity in humans

Gracey, E. G., & Lampe, J. N. (2025) · Drug Metabolism and Disposition · doi:10.1016/j.dmd.2025.100188 · Cited by 10 · see citing works

Artificial Intelligence Powered Biomarker Discovery: A Large-Scale Analysis of 236 Studies Across 19 Therapeutic Areas and 147 DiseasesPreprint

Abbasi, A. F., Naveed, S., Asim, M. N., Sajjad, M., Vollmer, S. J., & Dengel, A. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.28.672795 · Cited by 8 · see citing works

Strategic partnerships for AI-driven drug discovery: The role of relational dynamics

Kint, S., Dolfsma, W. A., & Robinson, D. (2024) · Drug Discovery Today · doi:10.1016/j.drudis.2024.104242 · Cited by 8 · see citing works

AI in Predictive Toxicology

Yingngam, B. (2024) · Advances in medical technologies and clinical practice book series · doi:10.4018/979-8-3693-3212-2.ch005 · Cited by 4 · see citing works

The Use of Data Mining for Obtaining Deeper Insights into the Fabrication of Prednisolone-Loaded Chitosan Nanoparticles

Nasereddin, J., Wadi, R. A., Alkilani, A. Z., Khalil, A. A., Natour, M. A., & Dayyih, W. A. (2024) · AAPS PharmSciTech · doi:10.1208/s12249-024-02756-3 · Cited by 2 · see citing works

THE ERA OF ARTIFICIAL INTELLIGENCE AND BIG DATA: A PATHWAY TO PHARMA 4.0

(2023) · International Journal of Biology Pharmacy and Allied Sciences · doi:10.31032/ijbpas/2023/12.10.1018

Introduction

Kamei, K. (2024) · The Journal of the Institute of Electrical Engineers of Japan · doi:10.1541/ieejjournal.144.189

Predicting Efficacy of Drugs for Amyotrophic Lateral Sclerosis Using Recurrent Neural Networks and LSTM

Venna, D., Polagani, A., & Sowreddy, P. (2025) · International Conference on Emerging Trends in Industry 4.0 Technologies (ICETI4T) · doi:10.1109/iceti4t63625.2025.11132206

Bladder Organoids for Healthy and Diseased Tissue Modeling

Sevim, S., & Akbulut, D. (2026) · Stem cell biology and regenerative medicine · doi:10.1007/978-3-032-10024-5_2

Intelligent organ-on-chip platforms: Machine learning in predictive and personalized drug delivery

Khurana, T., Ganguly, S., & Kiran Raj, M. (2026) · Biomicrofluidics · doi:10.1063/5.0306883

The mirage of intelligence: AI fallacies and the fragile foundations of pharma’s future

Pant, P. (2026) · AI and Ethics · doi:10.1007/s43681-026-01301-1

Immunocompetent Lung-on-a-Chip: Engineering the Pulmonary Immune Microenvironment for Disease Modeling

Li, X., Li, Y., Xu, J., Sun, R., Liu, W., Chen, X., Zhu, L., Zhao, Q., Li, J., Li, Y., et al. (2026) · Biosensors · doi:10.3390/bios16100547

The Role of AI and Machine Learning in Drug Discovery

Singh, A. K., Vishwakarma, V. K., & Verma, N. K. (2025) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.22753764

Integrating Artificial Intelligence with Emerging Pharmaceutical Technologies: Current Progress, Clinical Translation, and Future Challenges

Sharma, P., Tiwari, S., Park, N., & Szeleszczuk, Ł. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27177864

REVOLUTIONIZING DRUG DEVELOPMENT: HOW AI AND MACHINE LEARNING ARE SHAPING THE FUTURE OF MEDICINE-A REVIEW

MAHADEVASWAMY, G., SIVANATHAN, G., RAJAGOPAL, S., ANGAMUTHU, G., & Venkatesh, D. N. (2025) · International Journal of Applied Pharmaceutics · doi:10.22159/ijap.2025v17i3.53457 · Cited by 3 · see citing works

AI AND MACHINE LEARNING IN ACCELERATING DRUG DESIGN: OPPORTUNITIES, CHALLENGES, AND FUTURE DIRECTIONS

Ferdouse*, M. Z., Islam, M. R., Bhowmik, N. R., Habibullah, M. N. M., & Sharma, D. (2025) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.16574536

The Role of Artificial Intelligence, Machine Learning, and Bioinformatics in Revolutionizing Drug Development

Muthuswamy, R., Mozhi, M. T., Kamaleeshwari, R., & Kowshika, G. (2025) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.15713848