Assessing the impact of generative AI on medicinal chemistry

Mastering the game of Go with deep neural networks and tree search

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Applications of machine learning in drug discovery and development

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Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules

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Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules.

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Deep learning enables rapid identification of potent DDR1 kinase inhibitors

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Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks

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Computer-based de novo design of drug-like molecules

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GuacaMol: Benchmarking Models for de Novo Molecular Design

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druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico

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On the value of using 3D-shape and electrostatic similarities in deep generative methodsPreprint

Bolcato, G., & Boström, J. (2021) · doi:10.33774/chemrxiv-2021-sqvv9

Artificial Intelligence: As an innovative technique in healthcare sector and as a potent technique to fight against COVID-19

Pandey, P. (2022) · YMER Digital · doi:10.37896/ymer21.07/70

Artificial Intelligence Derived Artemisinin Drug Compound Acts as an Effective Candidate against SARS-CoV-2 Receptors: An In-Silico Study to Combat COVID-19

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Backgrounder—Part 1

Miller, S. M., Moos, W. H., Munk, B. H., Munk, S. A., Hart, C. P., & Spellmeyer, D. C. (2023) · Elsevier eBooks · doi:10.1016/b978-0-12-824304-6.00003-1

Ai-Enhanced Chemical Paradigm: From Molecular Graphs to Accurate Prediction and MechanismPreprint

Jiang, Y., Huang, Z. Z., He, W., Yu, J., Deng, S., Shao, X., Zhu, W., & Shao, X. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4618255

La Era Digital en la Química Farmacéutica: Transformando el Diseño de Medicamentos con Métodos Computacionales

Valdiviezo, J. (2023) · Revista de Química · doi:10.18800/quimica.202302.002 · Cited by 1

Report on scipost_202401_00034v1

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Report on scipost_202401_00034v2

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Report on scipost_202401_00034v2

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Report on scipost_202401_00034v1

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Rapid measurements and phase transition detections made simple by AC-GANs

Ding, J., Tang, H.-K., & Yu, W. C. (2024) · SciPost Physics Core · doi:10.21468/scipostphyscore.7.2.035

Can Generative AI Serve as the Modern-Day White-Collar Knowledge Laborer?

Ramachandran, R. B., & Gupta, C. (2025) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-83753-750-120251010

Cognizing Nature

Alonazi, R. E. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-6270-9.ch011

Generative Ai-Optimized Color Schemes in Elderly Care SpacesPreprint

Zhang, W. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5393926

ANTICIPATORY GOVERNANCE AMBIDEXTERITY: INTEGRATING SCIENTIFIC AND CITIZEN FORESIGHT FOR EMERGING S&T CHALLENGESPreprint

Panizzon, M., Janissek-Muniz, R., & Bas, E. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5414295

Additional file 2 of AI-driven molecular generation of not-patented pharmaceutical compounds using world open patent data

Shimizu, Y., Ohta, M., Ishida, S., Terayama, K., Osawa, M., Honma, T., & Ikeda, K. (2024) · Figshare · doi:10.6084/m9.figshare.26653266

Additional file 1 of Comparison of structure- and ligand-based scoring functions for deep generative models: a GPCR case study

Thomas, M., Smith, R. T., O’Boyle, N. M., de Graaf, C., & Bender, A. (2021) · Figshare · doi:10.6084/m9.figshare.14594366

Additional file 1 of AI-driven molecular generation of not-patented pharmaceutical compounds using world open patent data

Shimizu, Y., Ohta, M., Ishida, S., Terayama, K., Osawa, M., Honma, T., & Ikeda, K. (2024) · Figshare · doi:10.6084/m9.figshare.26653263

Additional file 3 of AI-driven molecular generation of not-patented pharmaceutical compounds using world open patent data

Shimizu, Y., Ohta, M., Ishida, S., Terayama, K., Osawa, M., Honma, T., & Ikeda, K. (2024) · Figshare · doi:10.6084/m9.figshare.26653269

Additional file 1 of MolScore: a scoring, evaluation and benchmarking framework for generative models in de novo drug design

Thomas, M., O’Boyle, N. M., Bender, A., & de Graaf, C. (2024) · Figshare · doi:10.6084/m9.figshare.26727727

Additional file 1 of De novo design and bioactivity prediction of SARS-CoV-2 main protease inhibitors using recurrent neural network-based transfer learning

Santana, M. V., & Silva, F. P. (2021) · Figshare · doi:10.6084/m9.figshare.13698384

Integrative deep learning strategies to enhance early-stage drug discovery: optimizing computational structure–activity modeling for pharmacotherapeutic innovation

Rezazi, S., Si‐Moussa, C., & Hanini, S. (2026) · Journal of Pharmacy & Pharmaceutical Sciences · doi:10.3389/jpps.2026.16155

Explainable AI for structure-based drug design: Concepts and case studies

Ren, M., Gangwar, S., Sharma, S., Khan, N. A., & Raza, K. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-44430-2.00010-4

HYG-mol: An Interpretable Multimodal Hypergraph Framework for Molecular Property Prediction

Ma, J., Yang, Q., Zhang, L., Liu, H., & Zheng, Y. (2026) · Computational and Structural Biotechnology Journal · doi:10.34133/csbj.0036

The future of drug discovery and biotechnology by the recent advancements in artificial intelligence

Pourrahmani, H. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-45526-1.00007-6

Natural biopolymers as super adsorbents

Sarkar, K., Ghosh, D. K., Patra, R., Ghosh, D., & Das, S. (2026) · Elsevier eBooks · doi:10.1016/b978-0-323-99858-1.00005-9

Molecular Design via Improved Diffusion Models and SELFIES Representation

Zhang, Y., & Xu, M. (2025) · China Automation Congress (CAC) · doi:10.1109/cac67268.2025.11486778

Schema-Based Cognition, Embodied Cognition and Social Cognition: The Impact of AI on STEM Education

Soundarajan, N. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-21147-7_20

Design and in Vitro Neuroprotective Evaluation of Pyrimidine-Based Compounds for Alzheimer’s Therapy

Sharma, V., Rani, G., Rai, S., Yasmeen, A., Kumar, M., Oleti, N., Gulia, S. K., Solanki, R., & Geetha, P. (2026) · Oriental Journal Of Chemistry · doi:10.13005/ojc/420212

Adaptive Distribution-Driven Filtering in Reinforcement Learning applied to GLP-1R Modulator DesignPreprint

Clark-Nicolas, J., Pugliese, A., & Morrison, A. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6750875

Fairness Definitions and Metrics in Deep Reinforcement Learning for Drug Discovery in Healthcare: A Rapid Evidence Review

Shakeri, E., Santos, R. D. S., & Far, B. H. (2026) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac69091.2026.00151

Artificial Intelligence in Drug Design: Emerging Trends and Applications

Gupta, J., Jain, S. D., Agrawal, A., & Gupta, A. K. (2026) · International Journal of Newgen Research in Pharmacy & Healthcare · doi:10.61554/ijnrph.v4i1.2026.235

Improving hit discovery by integrating activity cliff sensitivity into active learning

Kim, J., Kim, Y., Koo, B., Bang, D., & Kim, S. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btag302

Bridging the green skills gap: a framework to align the pharmaceutical workforce with sustainable drug discovery

Alwassil, O. I. (2026) · Future Medicinal Chemistry · doi:10.1080/17568919.2026.2699691

Safety-by-Design Generative Chemistry for Natural Products: Toxicity Boundaries, Interaction Risk, Validation Gates, and Human Oversight

Huy, N. T., Minh, P. Q., & Bich, L. T. (2026) · International Journal of Pharmaceutical and Phytopharmacological Research · doi:10.51847/hjuyamdfcc

Future-Ready Pharmaceutical Sciences and Phytopharmacology: From Molecular Intelligence to Responsible Natural Product-Based Therapeutics

Thompson, D., Mitchell, S., Adams, R., & Brown, J. (2026) · International Journal of Pharmaceutical and Phytopharmacological Research · doi:10.51847/chwxtuymdo

MolT5-Linker: A Transformer-Based Sequence- and Putative Site-Guided Generative Framework for Antibody-Drug Conjugate Linkers

Chen, Y., Wu, F., Liu, Y., Wu, Y., Wang, D., & Wu, J. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6c01342 · Cited by 1

PredictRx: AI based decision support tool for molecular screening for breast cancer drug recommendation

Chauhan, R., Pandey, N., & Zuhairi, M. F. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1881187

Machine Learning Integrated Designing and Screening of 8-Hydroxyquinoline Based Metallo-β-Lactamase Inhibitors

Baudino, A. M., & Stone, K. L. (2026) · AI Chemistry · doi:10.3390/aichem1030011

Artificial intelligence for anticancer drug discovery from natural products of macroalgae and sponges: A systematic review

Rahmaniar, W., Sunarwidhi, A. L., & Hernawan, A. (2026) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2026.109280

Hybrid artificial intelligence and quantum annealing as an optimization layer in drug discovery

Ou, C., Liao, J.-C., Huang, C., & Lee, O. K. (2026) · Patterns · doi:10.1016/j.patter.2026.101634

AI-assisted identification and in vitro validation of a MyD88 pathway modulator for inflammatory airway signalling

Gao, J., Sha, X., Wang, R., & Fu, Y. (2026) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2026.109313

Every Molecular Prediction Needs an Evidence Passport Linking Confidence, Applicability, Provenance, and Experimental Consequence

Yamamoto, H., Greco, F., Bianchi, A., & Nakamura, R. (2024) · Pharmacophore · doi:10.51847/wvgrulwufs

Designing Molecules under the Full Weight of Potency, Selectivity, Synthesizability, Safety, and Pharmaceutical Developability

Nzouankeu, J.-B., Mwangi, L., Abega, P., & Dupont, C. (2024) · Pharmacophore · doi:10.51847/bhejqkbzml

One Molecular Language for Chemical Structures, Biological Sequences, Three-Dimensional Conformations, Spectra, Images, and Scientific Text

Fedorova, E., Volkov, A., Morozova, I., & Kuznetsov, D. (2025) · Pharmacophore · doi:10.51847/bwoobpcfdp

Artificial Intelligence-Enabled Drug Discovery after the Hype Cycle: A Bibliometric and Thematic Review of Twenty-Five Years of Research

Stankova, E., Brown, C., Aisyah, S., & Durand, O. (2025) · Pharmacophore · doi:10.51847/enwqtllm3h

AI-enabled discovery of small molecule β-lactamase inhibitors to overcome enzyme diversity and resistance evolution

Bakht, P., Gupta, R., Gangar, T., Ray, A. K., & Pathania, R. (2026) · RSC Medicinal Chemistry · doi:10.1039/d6md00258g

What Transfers from Molecules to Medicines? A Systematic Review of Foundation Models across Pharmaceutical Scales

Walker, J., Harris, O., & Wilson, G. (2026) · Pharmacophore · doi:10.51847/44vwgwgyvz

APPLICATION OF MACHINE LEARNING METHODS IN EARLY DRUG DEVELOPMENT

Maksimov, A. S., & Дергачёва, Ж. М. (2026) · Vestnik Farmacii · doi:10.52540/2074-9457.2026.1.72

Novel Phosphodiesterase-2 Inhibitors Discovered via Structure-Based Virtual Screening Targeting the Allosteric GAF-B Domain

Adeniran, C. A., Myratgeldiyev, G., Zheng, F., & Zhan, C. (2026) · ACS Medicinal Chemistry Letters · doi:10.1021/acsmedchemlett.6c00292

Machine Learning‐Aided Small‐Molecule Virtual Screening: Recent Advances and Future Perspectives

Wang, Y., Bansal, N., Wa, S., Sciabola, S., & Wang, Y. (2026) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.70084

In silico drug discovery against emerging fungal pathogens: advances from virtual screening to molecular dynamics

Prakash, A., & Singh, P. (2026) · In Silico Research in Biomedicine · doi:10.1016/j.insi.2026.100594

From Algorithmic Prediction to Therapeutic Evidence: Artificial Intelligence in Drug Discovery, Translation, and Responsible Governance

da Silva, P. C. R. (2026) · Nexus Science Review · doi:10.66104/337xw232

Artificial intelligence–assisted lead optimization in drug discovery: bridging computational advances and translational challenges

Mohamed, F. A. A. A., & El-sagheir, A. M. K. (2026) · Medicinal Chemistry Research · doi:10.1007/s00044-026-03616-4

Fragment Optimization─Simple SMILES String Manipulation Strategies to Optimize a Hit or Fragment

Cusack, K. P., & Aquilina, J. M. (2026) · ACS Medicinal Chemistry Letters · doi:10.1021/acsmedchemlett.6c00181

Bridging BET bromodomain and immune checkpoint inhibitors through generative bioorganic frameworks for next-generation cancer immunotherapy

Diab, M. K., Yassen, A. S. A., Fahmy, S. A., Ashour, H. F., & Nafie, M. S. (2026) · RSC Medicinal Chemistry · doi:10.1039/d6md00450d

Artificial intelligence in drug discovery: Integrating data, models, and mechanisms for next-generation therapeutics

Korutla, R., Garg, T., Pol, S. S., Shivandi, V., Gnanam, S., Padari, K. S., & Amal, S. (2026) · Artificial Intelligence in the Life Sciences · doi:10.1016/j.ailsci.2026.100183 · Cited by 1

Drugging the Undruggable in Oncology: Structural Computation, Machine Learning, and Generative AI for Historically Intractable Targets

Stańczak, M., & Niazi, S. K. (2026) · Pharmaceuticals · doi:10.3390/ph19101578

Machine Learning in Drug Discovery: From QSAR Foundations to Deep Learning, Foundation Models, and Toxicity-Ready Prediction

Benachour, Y., Maloukh, L., & Natsheh, A. N. (2026) · IEEE Access · doi:10.1109/access.2026.3741692

From artificial intelligence promise to translational practice in drug discovery: Reproducibility, validation and evidence-based implementation

Gangwal, A., & Lavecchia, A. (2026) · Drug Discovery Today · doi:10.1016/j.drudis.2026.104835 · Cited by 1

Network Pharmacology of Natural Products from Computational Prediction to Experimental Validation

Stephen, E. U., Hew, K., Tan, C.-Y., & Choo, Y. (2026) · Biomedicines · doi:10.3390/biomedicines14102343

Smart Pesticides for Sustainable Crop Protection: Integrating Bio-Nanotechnology, Molecular Precision, and Artificial Intelligence for Next-Generation Agricultural Innovation

Derbalah, A., & Smagghe, G. (2026) · BioNanoScience · doi:10.1007/s12668-026-02946-2

Can autonomous Agentic-AI systems accelerate drug discovery and development in the next decade?

Chaturvedi, N., Haque, S., & Kumar, V. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag583

BindingDB Entry 50010960: Learning Molecular Representations for Medicinal Chemistry.

BindingDB (2021) · UC San Diego · doi:10.7270/q2765jxg

From Siloed Methods to Integrated Pipelines: Coupled Approaches Driving Next-Generation Antibiotic Discovery

Alajmi, A. M., Alshehri, A. A., Fayez, N. A., Almughem, F. A., Alnassar, H. M., Alghamdi, H. A., Alshabibi, M. A., Alshehri, A. A., Alghannam, S. F., Aldkheil, E. A., et al. (2026) · Biomolecules · doi:10.3390/biom16111573

Reply to ‘Assessing the impact of generative AI on medicinal chemistry’

Zhavoronkov, A. A., & Aspuru‐Guzik, A. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0417-3 · Cited by 24

Generative AI Models for Drug Discovery

Tang, B., Ewalt, J., & Ng, H. L. (2021) · Topics in medicinal chemistry · doi:10.1007/7355_2021_124 · Cited by 44

Modern hit-finding with structure-guided de novo design: identification of novel nanomolar adenosine A2A receptor ligands using reinforcement learningPreprint

Thomas, M. C., Matricon, P. G., Gillespie, R. F., Napiórkowska, M., Neale, H., Mason, J. S., Brown, J., Fieldhouse, C., Swain, N. A., Geng, T., et al. (2024) · ChemRxiv · doi:10.26434/chemrxiv-2024-wh7zw-v2 · Cited by 1

Generative Artificial Intelligence in Pharmaceutical Research and Drug Discovery

Sayed, M. A. (2024) · doi:10.13140/rg.2.2.28297.66402

Identification of nanomolar adenosine A2A receptor ligands using reinforcement learning and structure-based drug design

Thomas, M. C., Matricon, P. G., Gillespie, R. F., Napiórkowska, M., Neale, H., Mason, J. S., Brown, J., Harwood, K., Fieldhouse, C., Swain, N. A., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-60629-0 · Cited by 25