AI in small-molecule drug discovery: a coming wave?

Computational approaches streamlining drug discovery

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Self-Driving Laboratories for Chemistry and Materials Science

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Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR

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Macrocycles in Drug Discovery─Learning from the Past for the Future

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Big data in basic and translational cancer research

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Drug discovery and development: introduction to the general public and patient groups

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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

A guide to artificial intelligence for cancer researchers

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Unleashing the power of generative AI in drug discovery

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Generative artificial intelligence in drug discovery: basic framework, recent advances, challenges, and opportunities

Gangwal, A., Ansari, A., Ahmad, I., Azad, A. K., Kumarasamy, V., Subramaniyan, V., & Wong, L. S. (2024) · Frontiers in Pharmacology · doi:10.3389/fphar.2024.1331062 · Cited by 216

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 211

Multi-modal molecule structure–text model for text-based retrieval and editing

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How successful are AI-discovered drugs in clinical trials? A first analysis and emerging lessons

KP Jayatunga, M., Ayers, M., Bruens, L., Jayanth, D., & Meier, C. (2024) · Drug Discovery Today · doi:10.1016/j.drudis.2024.104009 · Cited by 166

A generative AI-discovered TNIK inhibitor for idiopathic pulmonary fibrosis: a randomized phase 2a trial

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Discovery of senolytics using machine learning

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Artificial Intelligence in Pharmaceutical Sciences

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Drug design on quantum computers

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Navigating the complexities of drug development for inflammatory bowel disease

Honap, S., Jairath, V., Danese, S., & Peyrin‐Biroulet, L. (2024) · Nature Reviews Drug Discovery · doi:10.1038/s41573-024-00953-0 · Cited by 123

A survey of generative AI for de novo drug design: new frontiers in molecule and protein generation

Tang, X., Dai, H., Knight, E., Wu, F., Li, Y., Li, T., & Gerstein, M. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae338 · Cited by 144

Past, Present, and Future Perspectives on Computer-Aided Drug Design Methodologies

Bassani, D., & Moro, S. (2023) · Molecules · doi:10.3390/molecules28093906 · Cited by 116

First fully-automated AI/ML virtual screening cascade implemented at a drug discovery centre in Africa

Turón, G., Hlozek, J., Woodland, J. G., Kumar, A., Chibale, K., & Duran‐Frigola, M. (2023) · Nature Communications · doi:10.1038/s41467-023-41512-2 · Cited by 99

Analysis of pharma R&D productivity – a new perspective needed

Schuhmacher, A., Hinder, M., Stein, A. V. S. U., Hartl, D., & Gassmann, O. (2023) · Drug Discovery Today · doi:10.1016/j.drudis.2023.103726 · Cited by 99

Application of Artificial Intelligence In Drug-target Interactions Prediction: A Review

Liao, Q., Zhang, Y., Chu, Y., Ding, Y., Liu, Z., Zhao, X., Wang, Y., Wan, J., Ding, Y., Tiwari, P., et al. (2025) · npj Biomedical Innovations · doi:10.1038/s44385-024-00003-9 · Cited by 82

Artificial intelligence enabled ChatGPT and large language models in drug target discovery, drug discovery, and development

Chakraborty, C., Bhattacharya, M., & Lee, S. (2023) · Molecular Therapy — Nucleic Acids · doi:10.1016/j.omtn.2023.08.009 · Cited by 81

Structure and dynamics in drug discovery

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Translation of AI into oncology clinical practice

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The advancement of artificial intelligence in biomedical research and health innovation: challenges and opportunities in emerging economies

da Silva, R. G. L. (2024) · Globalization and Health · doi:10.1186/s12992-024-01049-5 · Cited by 108

The rise of degrader drugs

Teng, M., & Gray, N. S. (2023) · Cell chemical biology · doi:10.1016/j.chembiol.2023.06.020 · Cited by 63

Artificial intelligence-driven drug development against autoimmune diseases

Moingeon, P. E. (2023) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2023.04.005 · Cited by 69

Real-World Molecular Out-Of-Distribution: Specification and Investigation

Tossou, P., Wognum, C., Craig, M. J., Mary, H., & Noutahi, E. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01774 · Cited by 77

Artificial Intelligence in Small-Molecule Drug Discovery: A Critical Review of Methods, Applications, and Real-World Outcomes

Niazi, S. K. (2025) · Pharmaceuticals · doi:10.3390/ph18091271 · Cited by 61

GPCNDTA: Prediction of drug-target binding affinity through cross-attention networks augmented with graph features and pharmacophores

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Data-centric challenges with the application and adoption of artificial intelligence for drug discovery

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The promise of explainable deep learning for omics data analysis: Adding new discovery tools to AI

Santorsola, M., & Lescai, F. (2023) · New Biotechnology · doi:10.1016/j.nbt.2023.06.002 · Cited by 44

A Top-Down Design Approach for Generating a Peptide PROTAC Drug Targeting Androgen Receptor for Androgenetic Alopecia Therapy

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AI's potential to accelerate drug discovery needs a reality check

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Accelerated Discovery of Macrocyclic CDK2 Inhibitor QR-6401 by Generative Models and Structure-Based Drug Design

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Accelerating material design with the generative toolkit for scientific discovery

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AI-Driven Drug Discovery for Rare Diseases

Gangwal, A., & Lavecchia, A. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01966 · Cited by 48

Future directions in regulatory affairs

Chisholm, O., & Critchley, H. (2023) · Frontiers in Medicine · doi:10.3389/fmed.2022.1082384 · Cited by 38

A historical perspective of biomedical explainable AI research

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Oral ENPP1 inhibitor designed using generative AI as next generation STING modulator for solid tumors

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Self-supervised learning in drug discovery

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Promiscuous, persistent and problematic: insights into current enterococcal genomics to guide therapeutic strategy

Hourigan, D., Stefanovic, E., Hill, C., & Ross, R. P. (2024) · BMC Microbiology · doi:10.1186/s12866-024-03243-2 · Cited by 37

Metabolic crosstalk: Extracellular ATP and the tumor microenvironment in cancer progression and therapy

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NCH-DDA: Neighborhood contrastive learning heterogeneous network for drug–disease association prediction

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Prediction of multi-relational drug–gene interaction via Dynamic hyperGraph Contrastive Learning

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Quality by digital design to accelerate sustainable medicines development

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Leveraging artificial intelligence in antibody-drug conjugate development: from target identification to clinical translation in oncology

Lu, Y., Huang, W., Li, Y., Xu, Y., Wei, Q., Sha, C., & Guo, P. (2025) · npj Precision Oncology · doi:10.1038/s41698-025-01159-2 · Cited by 43

The Search for Antibacterial Inhibitors Targeting Cell Division Protein FtsZ at Its Nucleotide and Allosteric Binding Sites

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The changing landscape of medicinal chemistry optimization

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Comprehensive detection and characterization of human druggable pockets through binding site descriptors

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Targeting metabolic pathways to counter cancer immunotherapy resistance

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Large language models facilitating modern molecular biology and novel drug development

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Artificial intelligence for drug delivery: Yesterday, today and tomorrow

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Structural bioinformatics for rational drug design

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Artificial intelligence for antiviral drug discovery in low resourced settings: A perspective

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Industrializing AI-powered drug discovery: lessons learned from the Patrimony computing platform

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The grand challenge of discovering new cardiovascular drugs

Hong, C. C. (2022) · Frontiers in Drug Discovery · doi:10.3389/fddsv.2022.1027401 · Cited by 19

An overview of recent advances and challenges in predicting compound-protein interaction (CPI)

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Generative AI in drug discovery and development: the next revolution of drug discovery and development would be directed by generative AI

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On the application of artificial intelligence in virtual screening

Thaingtamtanha, T., Ravichandran, R., & Gentile, F. (2025) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2025.2508866 · Cited by 17

Innovative strategies for bone organoid: Synergistic application and exploration of advanced technologies

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Artificial intelligence makes a splash in small-molecule drug discovery

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Drug innovation via integration of traditional Chinese and Western medicine

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Targeted therapy in acute myeloid leukemia: Resistance and overcoming strategy

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Application of Pharmacokinetic Prediction Platforms in the Design of Optimized Anti-Cancer Drugs

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New directions in psychiatric drug development: promising therapeutics in the pipeline

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Polypharmacology prediction: the long road toward comprehensively anticipating small-molecule selectivity to de-risk drug discovery

Manen-Freixa, L., & Antolín, A. A. (2024) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2024.2376643 · Cited by 15

Advances in AI-based strategies and tools to facilitate natural product and drug development

Basnet, B. B., Zhou, Z., Wei, B., & Wang, H. (2025) · Critical Reviews in Biotechnology · doi:10.1080/07388551.2025.2478094 · Cited by 17

Artificial Intelligence-Driven Innovations in Pharmaceutical Development and Drug Delivery Systems

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Foundation models in drug discovery: Phenomenal growth today, transformative potential tomorrow?

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Pretraining graph transformers with atom-in-a-molecule quantum properties for improved ADMET modeling

Fallani, A., Ирекович, Н. Р., Arjona-Medina, J. A., Wegner, J. K., Tkatchenko, A., & Chernichenko, K. (2025) · Journal of Cheminformatics · doi:10.1186/s13321-025-00970-0 · Cited by 21

A structure-based framework for selective inhibitor design and optimization

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Machine Learning for De Novo Molecular Generation: A Comprehensive Review

Chen, Y., & Xue, W. (2026) · ACS Chemical Neuroscience · doi:10.1021/acschemneuro.5c00861 · Cited by 12

Transforming drug discovery with a high-throughput AI-powered platform: A 5-year experience with Patrimony

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AI-driven discovery of brain-penetrant Galectin-3 inhibitors for Alzheimer's disease therapy

Liu, X., Xu, J., Zheng, S., Yang, Y., Xie, Y., Liu, J., Zhong, J., Zhang, H., Chen, J., Dai, C., et al. (2025) · Pharmacological Research · doi:10.1016/j.phrs.2025.107834 · Cited by 10

In Silico Design and Investigation of Novel Thiazetidine Derivatives as Potent Inhibitors of PrpR in Mycobacterium tuberculosis

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Geometrical and thermodynamic stability of govaniadine scaffold adducts with dopamine receptor D1

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Clinical Implications of Isocitrate Dehydrogenase Mutations and Targeted Treatment of Acute Myeloid Leukemia with Mutant Isocitrate Dehydrogenase Inhibitors—Recent Advances, Challenges and Future Prospects

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Prospects and Ethics of ChatGPT in Biomedical Research and Medical Practice: An Emphasis on Nanotechnology

Faiyazuddin, M., Gholap, A. D., Maqsood, S., Abbas, S., Chaudhari, Y. S., Sharmila, S., & Webster, T. J. (2024) · Journal of Biomedical Nanotechnology · doi:10.1166/jbn.2024.3925 · Cited by 10

The Role of Artificial Intelligence in Drug Discovery and Pharmaceutical Development: A Paradigm Shift in the History of Pharmaceutical Industries

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AI-Driven Drug Discovery, Design, and Development in Immunological Disorders

Ogidi, O. I., Alfred-Ugbenbo, D., & Poripo, B. E. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-9725-1.ch005 · Cited by 10

Advancing active compound discovery for novel drug targets: insights from AI-driven approaches

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LIGHTHOUSE illuminates therapeutics for a variety of diseases including COVID-19

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GA-XGBoost, an explainable AI technique, for analysis of thrombin inhibitory activity of diverse pool of molecules and supported by X-ray

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Large language models for Alzheimer's disease drug discovery

Alkam, T., Tarshizi, E., & Benschoten, A. H. V. (2025) · Journal of Alzheimer s Disease · doi:10.1177/13872877251346890 · Cited by 9

Artificial intelligence‐enabled digital biomedical engineering

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Alternative Approaches to Counter Multidrug-Resistant Bacterial Pathogens

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Applications of AI/ML in accelerating the development of pulmonary drug delivery system

Jiang, J., Zhou, Z., Peng, T., Huang, Z., Pan, X., & Wu, C. (2025) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2025.11.028 · Cited by 7

AI‐Driven Acceleration of Fluorescence Probe Discovery

蒋雪峰, Li, Y. W., Tian, X., Yang, S., Luo, R., Zhou, C., Liu, Y., Hu, J., Feng, S., Gan, L., et al. (2025) · Advanced Science · doi:10.1002/advs.202515604 · Cited by 8

Artificial intelligence in drug discovery — what it is, where we stand and the path forward

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Introduction to Regulatory Affairs and Different Regulatory Bodies for Pharmaceutical Products and Impact of Digitalization on Regulatory Affairs

Huma, T., & Peng, Z. (2023) · Pharmacology & Pharmacy · doi:10.4236/pp.2023.1411030 · Cited by 7

Artificial Intelligence in Cancer Immunotherapy: Navigating Challenges and Unlocking Opportunities

Xiang, W., Yu, L., Chen, X. S., & Herold, M. J. (2024) · Engineering · doi:10.1016/j.eng.2024.12.014 · Cited by 8

The continuing importance of chemical intuition for the medicinal chemist in the era of Artificial Intelligence

Hann, M. M., & Keserű, G. M. (2025) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2025.2450785 · Cited by 7

Harnessing digital health technologies and real-world evidence to enhance clinical research and patient outcomes

Mahadik, S., Sen, P., & Shah, E. J. (2025) · Digital Health · doi:10.1177/20552076251362097 · Cited by 15

Multimodal out-of-distribution individual uncertainty quantification enhances binding affinity prediction for polypharmacology

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Accelerated drug development using a digital formulator and a self-driving tableting data factory

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