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

MHNfs: Prompting In-Context Bioactivity Predictions for Low-Data Drug Discovery

Schimunek, J., Luukkonen, S., & Klambauer, G. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c02373 · Cited by 7

Effectively utilizing publicly available databases for cancer target evaluation

Croft, D. R., Lodhia, P., Lourenco, S. D. S., & MacKay, C. (2023) · NAR Cancer · doi:10.1093/narcan/zcad035 · Cited by 7

Unlocking DCAFs To Catalyze Degrader Development: An Arena for Innovative Approaches

Miao, Q., Kadam, V. D., Mukherjee, A., Tan, Z., & Teng, M. (2023) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.3c01209 · Cited by 7

Application and Prospects of Large Language Models in Small-Molecule Drug Discovery

Ma, J., Liu, J., Xu, D., Song, X., & Zhang, Z. (2025) · Analytical Chemistry · doi:10.1021/acs.analchem.5c04083 · Cited by 9

L’intelligence artificielle, une révolution dans le développement des médicaments

Moingeon, P. E., Garbay, C., Dahan, M., Fermont, I., Benmakhlouf, A., Gouyette, A., Poitou, P., Saint-Pierre, A., & pharmaceutiques, L. G. D. V. I. E. S. (2024) · médecine/sciences · doi:10.1051/medsci/2024028 · Cited by 7

Visual analysis of drug research and development based on artificial intelligence

Wei, W., Song, C., Qi, C., Zhang, X., Zhang, X., Pu, R., & Ao, Y. (2024) · Journal of Holistic Integrative Pharmacy · doi:10.1016/j.jhip.2024.12.002 · Cited by 5

Precision oncology in the age of AI: lessons from AI-driven drug discovery and clinical translation

Yoo, W. (2026) · BJC Reports · doi:10.1038/s44276-026-00221-1 · Cited by 6

Flash optimization of drug combinations for Acinetobacter baumannii with IDentif.AI-AMR

You, K., Yazid, N. B. M., Chong, L. M., Hooi, L., Wang, P., Zhuang, I., Chua, S. J., Lim, E., Kok, A. Z. X., Marimuthu, K., et al. (2025) · npj Antimicrobials and Resistance · doi:10.1038/s44259-025-00079-2 · Cited by 6

AI Assistance in the Drug Development Process: Reaching for a Regulatory Framework

Giaramita, H. (2024) · Seton Hall Law Review · doi:10.60095/wbyz9142 · Cited by 4

ElixirSeeker : A Machine Learning Framework Utilizing Fusion Molecular Fingerprints for the Discovery of Lifespan‐Extending Compounds

Pan, Y., Cai, H., Ye, F., et al. (2025) · Aging Cell · doi:10.1111/acel.70116 · Cited by 4

Data science in pharmaceutical R&D: the DISRUPT-DS industry roundtable

Khan, N. S., Senderovitz, T., Weatherall, J. H., Branson, J., Egersdoerfer, B., Genevois-Marlin, E., Jasti, S., Kazi, M., Kumble, R., Loerch, P., et al. (2024) · Nature Reviews Drug Discovery · doi:10.1038/d41573-024-00104-5 · Cited by 5

Drug Discovery and Development Processes

Selvakumar, P., Seenivasan, S., Jadhav, R., et al. (2024) · Advances in healthcare information systems and administration book series · doi:10.4018/979-8-3693-6577-9.ch011 · Cited by 4

Chemistry and Technology Innovation to Advance Green and Sustainable Chemistry

Prabhakaran, P., Bhardwaj, S., Chopra, B., Dhingra, A. K., & Kant, M. (2024) · Wiley eBooks · doi:10.1002/9781394212767.ch12 · Cited by 4

Generative AI in pharmaceutical R&D: From large language models to AI agents to regulation

Weatherall, J., & Meier, C. (2026) · Drug Discovery Today · doi:10.1016/j.drudis.2025.104593 · Cited by 4

Synthesis of Small Molecule Library of Novel Nicotinamide Derivatives as Anticancer Agents and Computational Screening

Malothu, R. M., & Thalari, G. (2025) · ChemistrySelect · doi:10.1002/slct.202404621 · Cited by 3

The Six Ds of Exponentials and drug discovery: A path toward reversing Eroom’s law

Tropsha, A., Martin, H., & Cherkasov, A. R. (2025) · Drug Discovery Today · doi:10.1016/j.drudis.2025.104341 · Cited by 5

Editorial: Insights in silico methods and artificial intelligence for drug discovery: 2022

Medina‐Franco, J. L. (2023) · Frontiers in Drug Discovery · doi:10.3389/fddsv.2022.1126955 · Cited by 3

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

da Silva, R. G. L. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4672439 · Cited by 3

Artificial intelligence in small-molecule drug discovery

Martinelli, C., Repetto, M., & Curigliano, G. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13671-9.00012-0 · Cited by 3

Scoring protein-ligand binding structures through learning atomic graphs with inter-molecular adjacency

Wang, D. D., & Huang, Y. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013074 · Cited by 2

What patents on AI-derived drugs reveal

Freilich, J., & Rai, A. K. (2025) · Science · doi:10.1126/science.adw1972 · Cited by 3

Harnessing AI-driven reverse docking in drug discovery: a comprehensive review of opportunities, challenges, and emerging trends

Durojaye, O. A., Bellah, S. F., Uzoeto, H. O., Okoro, N. O., Cosmas, S., Ajima, J. N., Arazu, A. V., Ezechukwu, S. P., Ezechukwu, C. S., & Odiba, A. S. (2025) · Journal of Molecular Modeling · doi:10.1007/s00894-025-06480-y · Cited by 3

Deep learning for small-molecule drug discovery: From molecular design to clinical translation

Oliveira, R. I., Pereira, T., Abbasi, M., Salvador, J. A. R., & Arrais, J. P. (2025) · Journal of Pharmaceutical Analysis · doi:10.1016/j.jpha.2025.101533 · Cited by 4

India's Drug Discovery and Development Industry: The Quest for Novel Therapeutic Treatments

Differding, E. (2026) · ChemMedChem · doi:10.1002/cmdc.70304 · Cited by 2

Pediatric Sarcomas: The Next Generation of Molecular Studies

Giannikopoulos, P., & Parham, D. M. (2022) · Cancers · doi:10.3390/cancers14102515 · Cited by 4

Artificial intelligence for science and engineering: A priority for public investment in research and development

Hey, T. (2023) · OECD eBooks · doi:10.1787/7b7b1bce-en · Cited by 4

Applications of Molecular Docking Studies in SARS-CoV-2 Targeted Drug Discovery and the Gains Achieved through Molecular Docking

Yıldırım, M., & Çeli̇k, İ. (2024) · Biomedical engineering · doi:10.5772/intechopen.1004804 · Cited by 2

Mining Medicinally Relevant Bioreduction Substrates Inspired by Ligand-Based Drug Design

Rago, A. J., Zoi, I., Gartman, J. A., McDaniel, K. A., Jana, N., Liu, D., & Bai, W. (2024) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.4c01129 · Cited by 2

Atom-Level Quantum Pretraining Enhances the Spectral Perception of Molecular Graphs in Graphormer

Fallani, A., Arjona-Medina, J. A., Chernichenko, K. Y., Ирекович, Н. Р., Wegner, J. K., & Tkatchenko, A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72381-0_7 · Cited by 2

Rampant proteolysis at the intersection of therapy-induced hypoalbuminemia and acute pancreatitis

Leonard, S.-E., Kenis, P. J. A., & Perkins, R. C. (2022) · 4open · doi:10.1051/fopen/2022011 · Cited by 2

Lessons from shortcomings in machine learning for medical imaging

Varoquaux, G. P., & Cheplygina, V. (2023) · OECD eBooks · doi:10.1787/b885eecd-en · Cited by 2

DDE KG Editor: A data service system for knowledge graph construction in geoscience

Hou, C., Liu, K., Wang, T., Shi, S., Yan, L., Zhu, Y., Hu, X., Wang, C., Zhou, C., & Lv, H. (2024) · Geoscience Data Journal · doi:10.1002/gdj3.245 · Cited by 3

The usage of transcriptomics datasets as sources of Real-World Data for clinical triallingPreprint

Matos‐Filipe, P., García‐Illarramendi, J. M., Jorba, G., Oliva, B., Farrés, J., & Mas, J. M. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.11.10.515995 · Cited by 4

Discovery of Novel Antiosteoporosis Leads with Bone Resorption Inhibition and Anabolic Promotion through a Chemotype-Assembly Approach

Huang, D., Zhao, C., Li, R., Yao, N., Xu, J., & Gu, Q. (2024) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.4c00909 · Cited by 1

Experimental Assays: Chemical Properties, Biochemical and Cellular Assays,and In Vivo Evaluations

Serafim, M. S. M., Reis, E. V. D. S., Coelho-dos-Reis, J. G. A., Abrahão, J. S., & O’Donoghue, A. J. (2024) · Computer-aided drug discovery and design · doi:10.1007/978-3-031-76718-0_12 · Cited by 1

High-Throughput Techniques in Radiochemistry

Bowden, G. D., Lu, Y., Jones, J., van Dam, R. M., Pascali, G., & Scott, P. J. H. (2025) · Springer eBooks · doi:10.1007/978-3-031-84632-8_18 · Cited by 1

Toward Validated Quantum Mechanical Workflows Predicting pH-Dependent Properties: Benchmarking Protocols for Conformational Sampling, Model Solvent, Basis Set, Density Functional, and Empirical Corrections

Agbaglo, D. A., Ho, M. T. N., Frisch, M. J., et al. (2025) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.5c01484 · Cited by 1

Artificial Intelligence and Cancer Immunotherapy

Shah, A., Bhat, A. A., Bhat, M. R., Assad, A., & Macha, M. A. (2025) · Springer eBooks · doi:10.1007/978-981-96-8176-1_16 · Cited by 1

Benchmarking Sequence-Based Compound–Protein Interaction Prediction through Constructing a Debiased Data Set CDPN

Hao, Y., Li, B., Huang, D., et al. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c02040 · Cited by 1

Artificial Intelligence at Seventy: From Symbolic Aspirations to Emergent Realities

Ochoa-Barragán, R., Raya-Tapia, A. Y., López-Flores, F. J., Márquez, C. R., & Ponce‐Ortega, J. M. (2026) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.5c04476 · Cited by 1

Leveraging artificial intelligence in tissue regenerative engineering via small-molecule libraries

Hosseini, F. S., Whitfield, T., Mitchell, J., Li, Y., & Lo, K. W. ‐. (2026) · Drug Discovery Today · doi:10.1016/j.drudis.2026.104623 · Cited by 2

Antibacterial drug discovery: challenges and preclinical promises from synthetic small molecules

Fernandes, G., Kim, S., dos Santos, J. L., Wang, Y., Soudy, C., Redmond, J. M., Castagnolo, D., & Todd, M. H. (2026) · Chemical Society Reviews · doi:10.1039/d5cs00617a · Cited by 4

Advancing Antiviral Design: Integrating Natural Products, Computation and Targeted Delivery

Lanrewaju, A. A., Enitan, A. M., Sabiu, S., & Swalaha, F. M. (2026) · Chemical Biology & Drug Design · doi:10.1111/cbdd.70365 · Cited by 2

MNGCCL: Multi-neighborhood graph collaborative contrastive learning for drug-disease association prediction

Mi, C.-L., Liu, J., Wang, X., Shang, J., Zhu, R., Wang, J., & Dai, L.-Y. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10822460 · Cited by 2

Ai-Driven Discovery of Brain-Penetrant Galectin-3 Inhibitors for Alzheimer's Disease TherapyPreprint

Ye, Z., Liu, X., Xu, J., Zheng, S., Zhong, J., Yang, Y., Xie, Y., Zhang, H., Chen, J., Dai, C., et al. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5144196 · Cited by 1

Data Science for Analytical Development and Quality Control

Bodiwala, K. B., Lalwani, R., Jain, Z., & Gajjar, A. K. (2025) · Wiley eBooks · doi:10.1002/9781394287383.ch11 · Cited by 1

Targeting the super elongation complex for oncogenic transcription driven tumor malignancies: Progress in structure, mechanisms and small molecular inhibitor discovery

Wu, X., Xie, Y., Zhao, K., & Lü, J. (2023) · Advances in cancer research · doi:10.1016/bs.acr.2022.12.007

High-performance computing leadership to enable advances in artificial intelligence and a thriving compute ecosystem

Tourassi, G. D., Shankar, M. A., & Wang, F. (2023) · OECD eBooks · doi:10.1787/0e1b4c2f-en

Eroom’s Law and the decline in the productivity of biopharmaceutical R&D

Scannell, J. W. (2023) · OECD eBooks · doi:10.1787/6f256d17-en

The end of Moore’s Law? Innovation in computer systems continues at a high pace

Kressel, H. (2023) · OECD eBooks · doi:10.1787/63e48242-en · Cited by 1

Quantifying the “cognitive extent” of science and how it has changed over time and across countries

Milojević, S. (2023) · OECD eBooks · doi:10.1787/1f717652-en

Interpretability: Should – and can – we understand the reasoning of machine-learning systems?

Cartwright, H. (2023) · OECD eBooks · doi:10.1787/2d0478b9-en

Preface

OECD (2023) · OECD eBooks · doi:10.1787/f1262928-en

Wird KI neue Medikamente gegen Herzkrankheiten hervorbringen?

Glaser, M., Ritterhoff, J., Most, P., & Wade, R. C. (2023) · Aktuelle Kardiologie · doi:10.1055/a-2131-2843

Computational drug discovery and design

Anika, M., & Ariyanto, Y. S. (2023) · Crystallography Reviews · doi:10.1080/0889311x.2023.2281998

Beyond the Hype: The Reality of Implementing AI to Drive Drug Discovery Success

Segall, M. (2024) · GEN Biotechnology · doi:10.1089/genbio.2024.0042

The path to adoption of open source AI for drug discovery in Africa

Turón, G., & Duran‐Frigola, M. (2024) · Artificial Intelligence in the Life Sciences · doi:10.1016/j.ailsci.2024.100118 · Cited by 1

FraHNN: A Fragment-Based Hypergraph Neural Network Model for Molecular Property Prediction

Liu, Z., Zhao, Y., Zhang, H., He, S., Bo, X., & Zan, P. (2024) · Communications in computer and information science · doi:10.1007/978-981-96-0188-2_22

AI-driven antimicrobial peptides for drug development

Arora, Y., Lalwani, H., Kumar, A., Mathur, P., Chaturvedi, N., & Mishra, V. (2025) · Methods in microbiology · doi:10.1016/bs.mim.2024.08.003

OCSAug: diffusion-based optical chemical structure data augmentation for improved hand-drawn chemical structure image recognition

Kim, J., & Choi, J. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-07406-4

Integration and Innovation of AIDD and CADD: Cutting-Edge Technologies and Future Trends in Accelerating Drug Development

Song, D. (2025) · Journal of Diseases and Medicinal Plants · doi:10.11648/j.jdmp.20251102.13

AI in Medicine Recent Progress Opportunities and Future Directions

Sodiqovna, I., & Park, J. C. (2024) · International Conference on Future Networks and Distributed Systems (ICFNDS) · doi:10.1145/3726122.3726267

A Personalized and Preventive Medicine

Moingeon, P. E. (2025) · Springer eBooks · doi:10.1007/978-3-031-91668-7_9

The Predicted Drug

Moingeon, P. E. (2025) · Springer eBooks · doi:10.1007/978-3-031-91668-7_7

The Potential of Generative AI in Drug Development

Gupta, D., Sudhakar, R., Mujawar, S. S., Kadam, R., Patil, V., Kumar, A., Ahire, P., & Gupta, K. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-2043-4.ch005

Basics for AI drug discovery

Inoue, T., & Yuki, S. (2024) · Japanese Journal of Pesticide Science · doi:10.1584/jpestics.w24-30

The application status and development trend of artificial intelligence in chemical science - based on bibliometric and knowledge graph analysis

Xu, Y. (2025) · International Conference on Educational Digitalization and Computer Science in the Guangdong-Hong Kong-Macao Greater Bay Area · doi:10.1145/3746469.3746569

AI Data Applications in Drug Design, Development, and Diagnostics: A Comprehensive Approach to Modern Medicine

Singh, R., Pandey, P., Kumar, A., & Mishra, M. K. (2025) · Current Bioinformatics · doi:10.2174/0115748936370246250903052830

Challenges and Future Directions in Al for Drug Discovery

Zhang, J., Shao, X., & Fan, X. (2026) · Springer eBooks · doi:10.1007/978-3-031-98022-0_27

Drug discovery in plastic surgery

Tang, Y., Wang, J., & Wong, A. K. (2026) · Elsevier eBooks · doi:10.1016/b978-0-323-91168-9.00103-4

Function-Driven Molecular Design Enabled by Instruction-Tuned Large Language ModelsPreprint

Yang, Q., Wang, X., Wang, Y., Zhu, R., Li, H., Wu, X., Zhang, X., Zhou, M., Pu, H., Cai, K., et al. (2026) · Research Square · doi:10.21203/rs.3.rs-8819034/v1

Site-specific photosensitizer conjugation and anti-viral application of protein targeting SARS-CoV-2

Jiang, Y., Zou, C., Zhu, S., Fang, X., Zhang, X., Zhou, X., & Huang, M. (2026) · Dyes and Pigments · doi:10.1016/j.dyepig.2026.113660

Structure‐Based Analysis of Sertraline’s Binding Affinity With CYP2B6: An In Silico DFT and Molecular Docking Approach

Ahmadi, S. A., Razavi, R., & Tajaldini, H. (2026) · Journal of Chemistry · doi:10.1155/joch/8437866

From Research to Impact: A Comprehensive Platform for AI Model Deployment in Drug Discovery

Koletou, E., Mu, L., Stoyanova, R., Muñoz, J. O. G., Kozieł, K., Cisło, P., Maffioli, L., Adamczyk, D., Witkowski, J., Bartosik, M., et al. (2026) · Artificial Intelligence in the Life Sciences · doi:10.1016/j.ailsci.2026.100171

AI-Driven Innovations in Pulmonary Medicine

Aminabee, S. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7759-9.ch011

Computer-aided drug discovery: historical foundations, practical AI tools, and emerging ethical considerations

Clarke, R., Palazzo, G., Ruzicka, J., Ferla, S., & Bassetto, M. (2026) · Frontiers in Pharmacology · doi:10.3389/fphar.2026.1866562

Artificial Intelligence and Other Emerging Technologies for Radiopharmaceutical Development

Bowden, G. D., Webb, E., Jackson, I. M., James, M. L., & Scott, P. J. H. (2026) · Springer eBooks · doi:10.1007/978-3-032-26476-3_36

AI-Assisted Pharmaceutical Formulation Design: Comparative Development and Experimental Evaluation of Sustained-Release Lornoxicam Tablets

Abdulkarim, M., Bawazir, W., Issa, A. A., et al. (2026) · Pharmaceuticals · doi:10.3390/ph19071070

Thinking about the Impact of Artificial Intelligence on U.S. Health Care Costs and Spending Growth

Kocher, B., Zhao, B., & Duffy, E. L. (2026) · NEJM Catalyst · doi:10.1056/cat.25.0509 · Cited by 1

Role of AI and Machine Learning in Designing Next-Generation Therapeutics

Kaushik, A., Verma, G., Bharadava, K., Kumar, S., & Kaushal, R. S. (2026) · Springer eBooks · doi:10.1007/978-981-92-0477-9_17

Reassessing R&D outcomes of leading pharmaceutical companies

Schuhmacher, A., Witting, M., Hartl, D., Hinder, M., & Gassmann, O. (2026) · Drug Discovery Today · doi:10.1016/j.drudis.2026.104763

Isocitrate Dehydrogenase (IDH) Mutations in Hematological Malignancies: Epidemiology, Mechanisms, and Therapeutic Strategies

Xu, J., Bai, H., & Hu, L. (2026) · MedComm – Future Medicine · doi:10.1002/mef2.70070

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

Susquehanna Chorale Spring Concert "Roots and Wings"

Hawkins, D. (2017)

Genentech taps Affimed’s antibodies

Cross, R. (2018) · C&EN Global Enterprise · doi:10.1021/cen-09635-buscon15

KOMPLIKASI REGIONAL ANESTESI PADA PEDIATRIK

Sari, D. (2023) · Jurnal Komplikasi Anestesi · doi:10.22146/jka.v5i1.7322

Enxerto livre de arteria epigastrica inferior: avaliacao hemodinamica e angiografica pre e pos-operatoria

Dib, A., Puig, L. B., Salvadori, D., et al. (1988) · Arquivos Brasileiros de Cardiologia

Interleucinas na encefalopatia hipóxico-isquêmica

Ceccon, M. E. J. R. (2003) · Jornal de Pediatria · doi:10.1590/s0021-75572003000400002 · Cited by 14