Artificial intelligence in chemistry and drug design

Can the pharmaceutical industry reduce attrition rates?

Kola, I., & Landis, J. (2004) · Nature Reviews Drug Discovery · doi:10.1038/nrd1470 · Cited by 4,167

Handbook of Molecular Descriptors

Todeschini, R., & Consonni, V. (2000) · Methods and Principles in Medicinal Chemistry · doi:10.1002/9783527613106 · Cited by 4,107

PubChem 2019 update: improved access to chemical data

Kim, S., Chen, J., Cheng, T., Gindulyte, A., He, J., He, S., Li, Q., Shoemaker, B. A., Thiessen, P. A., Yu, B., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky1033 · Cited by 3,249

Applications of machine learning in drug discovery and development

Vamathevan, J., Clark, D., Czodrowski, P., Dunham, I., Ferran, E., Lee, G., Li, B., Madabhushi, A., Shah, P., Spitzer, M., et al. (2019) · Nature Reviews Drug Discovery · doi:10.1038/s41573-019-0024-5 · Cited by 3,228

ZINC 15 – Ligand Discovery for Everyone

Sterling, T., & Irwin, J. J. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5b00559 · Cited by 3,224

MoleculeNet: a benchmark for molecular machine learning

Wu, Z., Ramsundar, B., Feinberg, E. N., Gomes, J., Geniesse, C., Pappu, A. S., Leswing, K., & Pande, V. (2018) · Chemical Science · doi:10.1039/c7sc02664a · Cited by 3,107

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

Gómez-Bombarelli, R., Wei, J. N., Duvenaud, D., Hernández-Lobato, J. M., Sánchez-Lengeling, B., Sheberla, D., Aguilera-Iparraguirre, J., Hirzel, T. D., Adams, R. P., & Aspuru-Guzik, A. (2018) · ACS Central Science · doi:10.1021/acscentsci.7b00572 · Cited by 3,106

A Deep Learning Approach to Antibiotic Discovery

Stokes, J. M., Yang, K., Swanson, K., Jin, W., Cubillos-Ruiz, A., Donghia, N. M., MacNair, C. R., French, S., Carfrae, L. A., Bloom-Ackermann, Z., et al. (2020) · Cell · doi:10.1016/j.cell.2020.01.021 · Cited by 2,463

ChEMBL: towards direct deposition of bioassay data

Mendez, D., Gaulton, A., Bento, A. P., Chambers, J., De Veij, M., Félix, E., Magariños, M. P., Mosquera, J. F., Mutowo, P., Nowotka, M., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky1075 · Cited by 2,372

p-σ-π Analysis. A Method for the Correlation of Biological Activity and Chemical Structure

Hansch, C., & Fujita, T. (1964) · Journal of the American Chemical Society · doi:10.1021/ja01062a035 · Cited by 2,313

Explainable artificial intelligence for targeted protein degradation predictions

Prael, F. J., III, Blank, J., Forrester, W. C., Shen, L., & Rodríguez-Pérez, R. (2024) · Artificial Intelligence in the Life Sciences · doi:10.1016/j.ailsci.2024.100121 · Cited by 2

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

HVLGAN: hybrid hierarchical scaled attention-enabled latent model for structure-based drug discovery

Hole, S. R., Lakshmanan, M., Jeevaraj, R., Srinubabu, M., Salotagi, S., & Kolluru, V. (2026) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2026.2613686 · Cited by 2

Artificial Intelligence: A Major Landmark in the Novel Drug Discovery Pathway for the Remarkable Advancement in the Healthcare System

Debnath, R., Ikbal, A. M. A., Choudhury, A., Mandal, S. C., & Palit, P. (2024) · Interdisciplinary biotechnological advances · doi:10.1007/978-981-97-1148-2_19 · Cited by 2

Molecular Generation, QSAR, and Molecular Dynamic Simulation Studies for Virtual Screening of DNA Polymerase Theta Inhibitors

Qin, Z., Liu, L., Gao, M., Feng, W., Huang, C., & Liu, W. (2024) · Current Computer - Aided Drug Design · doi:10.2174/0115734099305142240508051830 · Cited by 2

A Precise Survey on Multi-agent in Medical Domains

Alshehri, A., Alshahrani, F., & Shah, H. (2023) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2023.01406107 · Cited by 2

Impact of Artificial Intelligence on Clinical Research

Zoghbi, M. E., Malhotra, A., Bilal, M., & Shaukat, A. (2024) · Gastrointestinal Endoscopy Clinics of North America · doi:10.1016/j.giec.2024.10.002 · Cited by 1

Perceptions of Pharmacy Students Toward Artificial Intelligence in Pharmacy

Saeed, A. A., Zamel, A. M. A., Dafallah, A. S., Fagir, A. S. E., Idris, B. A., Youseef, D. A., Saeed, T. B., Idris, W. A., & Hamid, A. (2024) · Matrix Science Pharma · doi:10.4103/mtsp.mtsp_9_24 · Cited by 1

The Role of Artificial Intelligence in the Discovery of New Drugs

Barcelos, M. P., & da Silva, C. H. T. D. P. (2024) · Engineering materials · doi:10.1007/978-3-031-75984-0_14 · Cited by 1

Artificial intelligence in sustainable manufacturing and chemical processes

Ismail, Z., Clauser, N. M., Zafar, A., Minofar, B., Benettayeb, A., Chetthamrongchai, P., Vallejos, M. E., Sher, F., Batmunkh, E., & Hamilton, J. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-34076-5.00019-5 · Cited by 1

Active Learning in Drug Discovery: Revolutionizing Chemical Space Exploration

Suri, K., Yadav, A., Paul, P., Dhanjal, J. K., Chatterjee, B., & Murugan, N. A. (2026) · Springer eBooks · doi:10.1007/978-3-031-98022-0_13 · Cited by 1

МЕТОДИ IN CEREBRO – НОВА ПАРАДИГМА ДОСЛІДЖЕНЬ У МЕДИЧНІЙ ХІМІЇ ТА ДИЗАЙНІ ЛІКАРСЬКИХ ЗАСОБІВ

Головенко, М. Я. (2026) · Medical and Clinical Chemistry · doi:10.11603/mcch.2410-681x.2026.i2.16362 · Cited by 1

AI-driven computational drug design: tools, workflow and challenges

Dhir, R., Ghosh, P., Sharma, D., & Asati, V. (2026) · RSC Advances · doi:10.1039/d6ra02374f · Cited by 1

Perspectives on the use and implementation of artificial intelligence in pharmacy education and training: Views of faculty, PharmD students, and pharmacists in Saudi Arabia

Alsharif, A. A., Alsaleh, N. A., Alshehri, G. H., AlRasheed, H. A., Daghastani, D. R., Alotaibi, S. S., Alasmari, L. A., AlGhamdi, R. H., & Naser, A. Y. (2026) · Digital Health · doi:10.1177/20552076261476556 · Cited by 1

Advances of AI-driven Drug Design and Discovery in Pharmaceuticals - Review

Pandey, D. R., Dash, S. S., & Mishra, A. (2024) · Journal of Angiotherapy · doi:10.25163/angiotherapy.819488 · Cited by 1

Transformative potential of artificial intelligence in medical microbiology education

Rathod, S. S., Bankar, N. J., Tiwade, Y. R., Bandre, G. R., Mishra, V. H., & Badge, A. K. (2024) · Journal of Education and Health Promotion · doi:10.4103/jehp.jehp_2112_23 · Cited by 1

Analysis of the Effect of Virtual Reality Technology on Improving Drug Design

Luo, X. (2022) · Advances in Social Science, Education and Humanities Research/Advances in social science, education and humanities research · doi:10.2991/assehr.k.220110.124 · Cited by 1

To Affinity and Beyond: A Personal Reflection on the Design and Discovery of Drugs

Flower, D. (2022) · Molecules · doi:10.3390/molecules27217624

Expanding the Chemical Information Science gateway

Medina‐Franco, J. L. (2021) · F1000Research · doi:10.12688/f1000research.52192.1 · Cited by 1

Revising Message Passing Graph Neural Networks for Catalyst DesignPreprint

Lukin, R. Y., Faleev, M., & Grigorev, R. (2022) · ChemRxiv · doi:10.26434/chemrxiv-2022-jgd2t · Cited by 1

Data science for Chemists: Integrating and evaluating the use of interactive digital python notebooks in a large enrollment undergraduate biochemistry coursePreprint

Brunk, R., Shukla, K., Hutson, B., Wang, Y., Verber, M., Ford, C., Dennis, W., Mehta, A., Hogan, B., Swetnam, T. L., et al. (2023) · ChemRxiv · doi:10.26434/chemrxiv-2023-qqcs9 · Cited by 1

Open Access Databases and Datasets for Computer‐Aided Drug Design. A Short List Used in the Molecular Modelling Group of the SIB

Daina, A., Ojeda, M. J., Bragina, M. E., Cuozzo, A., Röhrig, U. F., Perez, M. A. S., & Zoete, V. (2023) · Methods and principles in medicinal chemistry · doi:10.1002/9783527830497.ch1 · Cited by 1

Real-World Applications of Artificial Intelligence and Blockchain in HealthcarePreprint

Naraghi, A., Nourmohammadi, R., & Behravan, I. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4650201 · Cited by 1

Machine Learning in Computational Chemistry

Kuntz, D. (2022) · University of North Texas Digital Library (University of North Texas) · doi:10.12794/metadc1944346 · Cited by 1

Masthead - Full issue pdf

(2022) · Chemistry International · doi:10.1515/ci-2022-0100

The Brazilian Compound Library (BraCoLi) Database: A Brazilian Repository of Chemical and Biological Information for Drug DesignPreprint

Veríssimo, G. C., dos Santos, V. S., de Almeida, I. A. D. R., Ruas, M. S. M., Coutinho, L. G., de Oliveira, R. B., Maltarollo, V. G., & Alves, R. J. (2021) · doi:10.33774/chemrxiv-2021-lwrcx-v2

The Brazilian Compound Library (BraCoLi) Database: A Brazilian Repository of Chemical and Biological Information for Drug DesignPreprint

Veríssimo, G. C., dos Santos, V. S., de Almeida, I. A. D. R., Ruas, M. S. M., Coutinho, L. G., de Oliveira, R. B., & Maltarollo, V. G. (2021) · ChemRxiv · doi:10.26434/chemrxiv.14569356

Data-driven chemistry

Rampino, S. (2022) · Elsevier eBooks · doi:10.1016/b978-0-32-390865-8.00031-3

Bibliography

(2022) · Elsevier eBooks · doi:10.1016/b978-0-32-390865-8.00034-9

Ai's Assistance in Amplifying the Competence of ChemistsPreprint

Goel, S., Khulbe, M., Sharma, R., & Bargujar, S. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4055501 · Cited by 1

APLICAÇÕES EM INTELIGÊNCIA ARTIFICIAL NA EDUCAÇÃO, NA QUÍMICA E NO ENSINO DE QUÍMICA: uma Revisão Sistemática de Literatura

PONTES, V. M. C. (2021) · doi:10.29327/4177535

AI in Translational Bioinformatics and Precision Medicine

Nguyen, T. M., & Chen, J. Y. (2022) · Cognitive informatics in biomedicine and healthcare · doi:10.1007/978-3-031-09108-7_14

68 million natural product-like compound database generated via molecular language processingPreprint

Tay, D. W. P., Xi, N. Y. Z., Adaikkappan, K., Lim, Y. H., & Ang, S. J. (2023) · ChemRxiv · doi:10.26434/chemrxiv-2023-wmgwn

Constructive Machine Learning and Hierarchical Multi-label Classification for Molecules Design

Silva, R. R. D. S., & Cerri, R. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-45389-2_19

DEEP LEARNING IN DRUG DISCOVERY

Preuer, K. (2023) · International Journal of Biology Pharmacy and Allied Sciences · doi:10.31032/ijbpas/2023/12.10.1013

n-Tuples on Scaffold Diversity Inspired by Drug Hybridisation to Enhance Drugability: Application to Cytarabine

García-Castro, M., Fuentes‐Ríos, D., López‐Romero, J. M., Romero‐Carrasco, A., Moya‐Utrera, F., Díaz, A., & Sarabia, F. (2023) · Marine Drugs · doi:10.3390/md21120637

Descoberta de novos inibidores para doenças infecciosas: estudos integrados de modelagem molecular e aprendizado de máquina para a malária e o COVID-19

Nogueira, V. H. R. (2024) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/t.76.2024.tde-23042024-120251

AI: Catalyst for Drug Discovery and Development

Nailwal, K., Durgapal, S., Dasauni, K., & Nailwal, T. K. (2024) · Interdisciplinary biotechnological advances · doi:10.1007/978-981-97-1148-2_18

Introduction

Carpentieri, B., & Lecca, P. (2024) · Wiley eBooks · doi:10.1002/9781119846567.ch1

Application of Artificial Intelligence for Predicting of New Potential Inhibitors of Vitamin K Epoxide Reductase

Antonijević, M. R., Milenković, D., Avdović, E., & Marković, Z. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-60840-7_21

Digital Transformation in Healthcare - A Recent Progress and Applications of Artificial Intelligence in Healthcare (Preprint)Preprint

Nadeem, N., Rashid, Z., Zafar, S. B., & Yousaf, M. Z. (2024) · doi:10.2196/preprints.63885 · Cited by 1

Tentative exploration of artificial intelligence in developing and optimizing fuel cell catalysts

Qiuru, H., Gu, Y., Guo, X., Wu, C., Li, G., Zhang, Y., & Wang, S. (2024) · Scientia Sinica Chimica · doi:10.1360/ssc-2024-0128

AI/ML Approaches in Drug Design

Kırboğa, K. K. (2024) · Wiley eBooks · doi:10.1002/9781394249190.ch11

The Role of Green Chemistry in the Management of Self-harm and Suicides by Hazardous Chemicals

Utembe, W., & Tlotleng, N. (2024) · Integrated science · doi:10.1007/978-3-031-64288-3_6

Trust in Artificial Intelligence - the Role of Occupation and ExplanationsPreprint

Prinz, S. G., Weißenberger, B. E., & Kotzian, P. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5008350

Cognizing Nature

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

AI Algorithms in Sustainable Healthcare

Vișan, A. I., & Neguț, I. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-9725-1.ch001 · Cited by 1

1Chapter 1 Drug development in virtual world: metaverse and digital twins in healthcare technology

Singh, B., & Malviya, R. (2025) · De Gruyter eBooks · doi:10.1515/9783111646893-001

Navigating the Challenges of Large Language Models in Construction Education in South Africa: Balancing Innovation and Adoption

Gyadu-Asiedu, N. A. A., Aigbavboa, C. O., & David, M. M. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-032-07992-3_16

Artificial intelligence driven discovery of anticancer phytochemicals: from plants to lead compounds

Gholap, A. D., Khuspe, P. R., & Mane, D. V. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-40511-2.00001-2

Additional file 1 of DeepDelta: predicting ADMET improvements of molecular derivatives with deep learning

Fralish, Z., Chen, A., Skaluba, P., & Reker, D. (2024) · Figshare · doi:10.6084/m9.figshare.26632926

Exploring the Potential of Large Language Models in Construction Education and Training in South Africa: Advancing Learning and Skill Development

Gyadu-Asiedu, N. A. A., Aigbavboa, C., Aghimien, L. M., & David, M. M. (2025) · Springer eBooks · doi:10.1007/978-3-031-99204-9_7

History of Artificial Intelligence and Drug Discovery

Lavecchia, A. (2026) · Springer eBooks · doi:10.1007/978-3-031-98022-0_1

Performance evaluation of generative pre-trained transformer on the National Veterinary Licensing Examination in Japan

Kako, T., Kato, D., Iguchi, T., Shiyu, Q., Ando, M., KOSEKI, S., SHIBAHARA, H., MOTOI, H., Isaka, R., Ikeda, N., et al. (2026) · Scientific Reports · doi:10.1038/s41598-026-37300-9

Artificial Intelligence in Drug Discovery and Diagnosis: Challenges, Opportunities, and Strategies

Mishra, A. K., Verma, S., Yadav, S., & Chanchal, D. K. (2026) · Current Indian Science · doi:10.2174/012210299x411339260202160840

Artificial Intelligence for predicting drug-likeness and bioavailability

Rajak, B. K., & Rani, P. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-44415-9.00011-9

On the use of Machine Learning modeling tools in chemical process and product engineering

Trinh, C. (2023) · theses.fr (ABES) · doi:10.70675/07975f61z019bz4c08zaae4z2fcda6cc9032

Protein Design Enters the Artificial Intelligence Era: Foundations, Tools, and Emerging Paradigms

Mi, Y., Shukla, A., Tangney, M., Tabirca, S., & Yallapragada, V. V. B. (2026) · Computational and Structural Biotechnology Journal · doi:10.34133/csbj.0105

AI-Enabled Drug Discovery in Pharmaceutical Sciences: A Systematic Review of Molecular Models, Target Prediction, Lead Optimization, and Translational Barriers

Anderson, J., Rossi, M., & Clark, W. (2025) · International Journal of Pharmaceutical and Phytopharmacological Research · doi:10.51847/6jtpi8neii

Natural Products in AI-Driven Drug Discovery: A Bibliometric and Thematic Review of Computational Methods, Disease Areas, and Emerging Frontiers

Silva, J., Costa, P., Beatriz, A., & Mendes, R. (2025) · International Journal of Pharmaceutical and Phytopharmacological Research · doi:10.51847/h3b2b00eye

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

Persuasion in AI-driven science: A case of drug design

Han, H. (2026) · Studies in History and Philosophy of Science Part A · doi:10.1016/j.shpsa.2026.102219

Artificial Intelligence: Shaping Next Generation Oncotherapy

Tejashwini, K., Thota, S., Himabindu, V., Maneesha, N., & Bhukya, B. (2026) · Cancer Bioscience · doi:10.1016/j.cbs.2026.100007

Decoding the antidiabetic potential of Gymnema sylvestre: An in silico network pharmacology and molecular docking analysis

Raut, S., Gawande, S., lanjekar, R., & Mahabhudhe, J. (2026) · In Silico Research in Biomedicine · doi:10.1016/j.insi.2026.100625

Drug-Likeness, Druggability, and Molecular Developability via Chemometric Evaluation: A Review

Yu, B., Zhu, H.-Y., Gu, R., Du, S., Shi, W., & Xu, L. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6c02860

Artificial intelligence in molecular de novo design: Integration with experiment

Janet, J. P., Mervin, L., & Engkvist, O. (2023) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2023.102575 · Cited by 25

Artificial Intelligence Approaches for Rational Drug Design and Discovery

Duch, W., Swaminathan, K., & Meller, J. (2007) · Current Pharmaceutical Design · doi:10.2174/138161207780765954 · Cited by 249

Artificial intelligence in drug design

Zhong, F., Xing, J., Li, X., Liu, X., Fu, Z., Xiong, Z., Lu, D., Wu, X., Zhao, J., Tan, X., et al. (2018) · Science China Life Sciences · doi:10.1007/s11427-018-9342-2 · Cited by 293

Artificial Intelligence in Drug Discovery: Machine Learning for Molecular Design

Mahmoud, A. R. (2026) · doi:10.13140/rg.2.2.12065.57442

Artificial Intelligence for Autonomous Molecular Design: A Perspective

Joshi, R. P., & Kumar, N. (2021) · Molecules · doi:10.3390/molecules26226761 · Cited by 55