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

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

Gupta, R., Srivastava, D., Sahu, M., Tiwari, S., Ambasta, R. K., & Kumar, P. (2021) · Molecular Diversity · doi:10.1007/s11030-021-10217-3 · Cited by 1,473

Critical assessment of methods of protein structure prediction (CASP)—Round XIV

Kryshtafovych, A., Schwede, T., Topf, M., Fidelis, K., & Moult, J. (2021) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26237 · Cited by 503

Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 1: Ways to make an impact, and why we are not there yet

Bender, A., & Cortés-Ciriano, I. (2021) · Drug Discovery Today · doi:10.1016/j.drudis.2020.12.009 · Cited by 405

Application of Artificial Intelligence in Medicine: An Overview

Liu, P.-R., Lu, L., Zhang, J.-Y., Huo, T.-T., Liu, S.-X., & Ye, Z.-W. (2021) · Current Medical Science · doi:10.1007/s11596-021-2474-3 · Cited by 258

Natural product drug discovery in the artificial intelligence era

Saldívar‐González, F. I., Aldas-Bulos, V. D., Medina‐Franco, J. L., & Plisson, F. (2021) · Chemical Science · doi:10.1039/d1sc04471k · Cited by 235

Generating Multibillion Chemical Space of Readily Accessible Screening Compounds

Grygorenko, O. O., Radchenko, D. S., Dziuba, I., Chuprina, A., Gubina, K. E., & Moroz, Y. S. (2020) · iScience · doi:10.1016/j.isci.2020.101681 · Cited by 238

Machine Learning for Toxicity Prediction Using Chemical Structures: Pillars for Success in the Real World

Seal, S., Mahale, M., García-Ortegón, M., Joshi, C. K., Hosseini-Gerami, L., Beatson, A., Greenig, M., Shekhar, M., Patra, A., Weis, C., et al. (2025) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.5c00033 · Cited by 168

Role of artificial intelligence in revolutionizing drug discovery

Rehman, A. U., Li, M., Wu, B., Ali, Y., Rasheed, S., Shaheen, S., Liu, X., Luo, R., & Zhang, J. (2024) · Fundamental Research · doi:10.1016/j.fmre.2024.04.021 · Cited by 160

New avenues in artificial-intelligence-assisted drug discovery

Cerchia, C., & Lavecchia, A. (2023) · Drug Discovery Today · doi:10.1016/j.drudis.2023.103516 · Cited by 117

The New Era of Drug Discovery: The Power of Computer-aided Drug Design (CADD)

Nascimento, I. J. D. S., de Aquino, T. M., & da Silva‐Júnior, E. F. (2022) · Letters in Drug Design & Discovery · doi:10.2174/1570180819666220405225817 · Cited by 112

Chemistry in Times of Artificial Intelligence

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

Practical considerations for active machine learning in drug discovery

Reker, D. (2019) · Drug Discovery Today Technologies · doi:10.1016/j.ddtec.2020.06.001 · Cited by 103

Introduction to small molecule drug discovery and preclinical development

Southey, M. W. Y., & Brunavs, M. (2023) · Frontiers in Drug Discovery · doi:10.3389/fddsv.2023.1314077 · Cited by 106

Effect of gem ‐Difluorination on the Key Physicochemical Properties Relevant to Medicinal Chemistry: The Case of Functionalized Cycloalkanes

Holovach, S., Melnykov, K. P., Skreminskiy, A., Herasymchuk, M. V., Tavlui, O., Aloshyn, D., Borysko, P., Rozhenko, A. B., Ryabukhin, S. V., Volochnyuk, D. M., et al. (2022) · Chemistry - A European Journal · doi:10.1002/chem.202200331 · Cited by 100

A comprehensive review of artificial intelligence and network based approaches to drug repurposing in Covid-19

Ahmed, F., Soomro, A. M., Salih, A. R. C., Samantasinghar, A., Asif, A., Kang, I. S., & Choi, K. H. (2022) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2022.113350 · Cited by 92

Machine learning-accelerated quantum mechanics-based atomistic simulations for industrial applications

Morawietz, T., & Artrith, N. (2020) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-020-00346-6 · Cited by 84

Photochemically Activated 3D Printing Inks: Current Status, Challenges, and Opportunities

Gauci, S. C., Vranic, A., Blasco, E., Bräse, S., Wegener, M., & Barner‐Kowollik, C. (2023) · Advanced Materials · doi:10.1002/adma.202306468 · Cited by 82

Knowledge, attitude and practice among pharmacy students and faculty members towards artificial intelligence in pharmacy practice: A multinational cross-sectional study

Hasan, H. E., Jaber, D., Al Tabbah, S., Lawand, N., Habib, H. A., & Farahat, N. M. (2024) · PLOS ONE · doi:10.1371/journal.pone.0296884 · Cited by 76

A review of the current trends in computational approaches in drug design and metabolism

Ouma, R. B. O., Ngari, S. M., & Kibet, J. K. (2024) · Discover Public Health · doi:10.1186/s12982-024-00229-3 · Cited by 80

Chemical-informatics approach to COVID-19 drug discovery: Exploration of important fragments and data mining based prediction of some hits from natural origins as main protease (Mpro) inhibitors

Ghosh, K., Amin, S. A., Gayen, S., & Jha, T. (2020) · Journal of Molecular Structure · doi:10.1016/j.molstruc.2020.129026 · Cited by 65

Mastering the Gram-negative bacterial barrier – Chemical approaches to increase bacterial bioavailability of antibiotics

Ropponen, H., Richter, R., Hirsch, A. K. H., & Lehr, C. (2021) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2021.02.014 · Cited by 66

Machine learning, artificial intelligence, and data science breaking into drug design and neglected diseases

Guerrero, J. P., Nguewa, P., & García‐Sosa, A. T. (2021) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1513 · Cited by 63

Machine Learning in Chemical Product Engineering: The State of the Art and a Guide for Newcomers

Trinh, C., Meimaroglou, D., & Hoppe, S. (2021) · Processes · doi:10.3390/pr9081456 · Cited by 65

DeepDelta: predicting ADMET improvements of molecular derivatives with deep learning

Fralish, Z., Chen, A., Skaluba, P., & Reker, D. (2023) · Journal of Cheminformatics · doi:10.1186/s13321-023-00769-x · Cited by 65

Fundamentals of molecular modeling in drug design

Tripathi, M. K., Ahmad, S., Tyagi, R., Dahiya, V., & Yadav, M. K. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-90608-1.00001-0 · Cited by 54

Recent Advances in Pharmaceutical Design: Unleashing the Potential of Novel Therapeutics

Prajapati, R. N., Bhushan, B., Singh, K., Chopra, H., Kumar, S., Agrawal, M., Pathak, D., Chanchal, D. K., & Kant, L. (2024) · Current Pharmaceutical Biotechnology · doi:10.2174/0113892010275850240102105033 · Cited by 62

Machine learning for small molecule drug discovery in academia and industry

Volkamer, A., Riniker, S., Nittinger, E., Lanini, J., Grisoni, F., Evertsson, E., Rodríguez-Pérez, R., & Schneider, N. (2023) · Artificial Intelligence in the Life Sciences · doi:10.1016/j.ailsci.2022.100056 · Cited by 48

67 million natural product-like compound database generated via molecular language processing

Tay, D. W. P., Yeo, N. Z. X., Adaikkappan, K., Lim, Y. H., & Ang, S. J. (2023) · Scientific Data · doi:10.1038/s41597-023-02207-x · Cited by 49

Molecular Screening and Toxicity Estimation of 260,000 Perfluoroalkyl and Polyfluoroalkyl Substances (PFASs) through Machine Learning

Lai, T. T., Kuntz, D., & Wilson, A. K. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c00374 · Cited by 41

The challenges of generalizability in artificial intelligence for ADME/Tox endpoint and activity prediction

Huang, D. Z., Baber, J. C., & Bahmanyar, S. S. (2021) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2021.1901685 · Cited by 39

The paradigm of digital health: AI applications and transformative trends

Rashid, Z., Ahmed, H., Nadeem, N., Zafar, S. B., & Yousaf, M. Z. (2025) · Neural Computing and Applications · doi:10.1007/s00521-025-11081-0 · Cited by 45

Superhuman science: How artificial intelligence may impact innovation

Agrawal, A. K., McHale, J., & Oettl, A. (2023) · Journal of Evolutionary Economics · doi:10.1007/s00191-023-00845-3 · Cited by 37

Graph Polish: A Novel Graph Generation Paradigm for Molecular Optimization

Ji, C., Zheng, Y., Wang, R., Cai, Y., & Wu, H. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3106392 · Cited by 27

An efficiency-driven, correlation-based feature elimination strategy for small datasets

Rickert, C. A., Henkel, M., & Lieleg, O. (2023) · APL Machine Learning · doi:10.1063/5.0118207 · Cited by 32

Artifical intelligence: a virtual chemist for natural product drug discovery

Arora, S., Chettri, S., Percha, V., Kumar, D., & Latwal, M. (2023) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2023.2216295 · Cited by 25

DNN-PP: A novel Deep Neural Network approach and its applicability in drug-related property prediction

Wiercioch, M., & Kirchmair, J. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.119055 · Cited by 26

Improving molecular machine learning through adaptive subsampling with active learning

Wen, Y., Li, Z., Xiang, Y., & Reker, D. (2023) · Digital Discovery · doi:10.1039/d3dd00037k · Cited by 24

Can We Quickly Learn to “Translate” Bioactive Molecules with Transformer Models?

Tysinger, E. P., Rai, B. K., & Sinitskiy, A. V. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c01618 · Cited by 23

The Role of Artificial Intelligence in Drug Discovery and Development

Liebman, M. (2022) · Chemistry International · doi:10.1515/ci-2022-0105 · Cited by 22

Activity Prediction of Small Molecule Inhibitors for Antirheumatoid Arthritis Targets Based on Artificial Intelligence

Xing, G., Liang, L., Deng, C., Hua, Y., Chen, X., Yang, Y., Liu, H., Lu, T., Chen, Y., & Zhang, Y. (2020) · ACS Combinatorial Science · doi:10.1021/acscombsci.0c00169 · Cited by 23

Memory augmented recurrent neural networks for de-novo drug design

Suresh, N., Kumar, N. C. A., SUBRAMANIAN, S. K., & Srinivasa, G. (2022) · PLoS ONE · doi:10.1371/journal.pone.0269461 · Cited by 23

Industry 4.0 technologies adoption for digital transition in drug discovery and development: A review

Anthwal, A., Uniyal, A., Gairolla, J., Singh, R., Gehlot, A., Abbas, M. A. E., & Akram, S. V. (2024) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2024.100562 · Cited by 23

Balancing computational chemistry's potential with its environmental impact

Schilter, O. T., Schwaller, P., & Laino, T. (2024) · Green Chemistry · doi:10.1039/d4gc01745e · Cited by 24

Identification of Bioisosteric Substituents by a Deep Neural Network

Ertl, P. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00290 · Cited by 21

Image-based molecular representation learning for drug development: a survey

Li, Y., Liu, B., Deng, J., Guo, Y., & Du, H. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae294 · Cited by 23

A comprehensive review of artificial intelligence for pharmacology research

Li, B., Tan, K., Lao, A. R., Wang, H., Zheng, H., & Zhang, L. (2024) · Frontiers in Genetics · doi:10.3389/fgene.2024.1450529 · Cited by 26

Quantum–mechanical property prediction of solvated drug molecules: what have we learned from a decade of SAMPL blind prediction challenges?

Tielker, N., Eberlein, L., Heßler, G., Schmidt, F., Güssregen, S., & Kast, S. M. (2020) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-020-00347-5 · Cited by 24

From pilots to decision systems: embedding generative AI into strategic decision-making through a socio-technical and governance lens

Saup, T.-O., Asghar, J., Kanbach, D. K., & Kraus, S. (2026) · Journal of Decision System · doi:10.1080/12460125.2025.2597835 · Cited by 29

Strategies in Medicinal Chemistry to Discover New Hit Compounds against Ebola Virus: Challenges and Perspectives in Drug Discovery

Nascimento, I. J. D. S., Santos-Júnior, P. F. D. S., de Araújo‐Júnior, J. X., & da Silva‐Júnior, E. F. (2022) · Mini-Reviews in Medicinal Chemistry · doi:10.2174/1389557522666220404085858 · Cited by 20

Introducing artificial MOFs for improved machine learning predictions: Identification of top-performing materials for methane storage

Fanourgakis, G. S., Gkagkas, K., & Froudakis, G. E. (2022) · The Journal of Chemical Physics · doi:10.1063/5.0075994 · Cited by 21

Contaminants in the Water Environment: Significance from the Perspective of the Global Environment and Health

Jiang, X., Kirsten, K. L., & Qadeer, A. (2025) · Water · doi:10.3390/w17091257 · Cited by 18

Prediction of Chromatography Conditions for Purification in Organic Synthesis Using Deep Learning

Vaškevičius, M., Kapočiūtė-Dzikienė, J., & Šlepikas, L. (2021) · Molecules · doi:10.3390/molecules26092474 · Cited by 18

Text‐based representations with interpretable machine learning reveal structure–property relationships of polybenzenoid hydrocarbons

Fite, S., Wahab, A., Paenurk, E., Gross, Z., & Gershoni‐Poranne, R. (2022) · Journal of Physical Organic Chemistry · doi:10.1002/poc.4458 · Cited by 17

Crystal structure prediction of materials with high symmetry using differential evolution

Yang, W., Siriwardane, E. M. D., Dong, R., Li, Y., & Hu, J. (2021) · Journal of Physics Condensed Matter · doi:10.1088/1361-648x/ac1d6c · Cited by 17

Radical Scavenging Activity and Pharmacokinetic Properties of Coumarin–Hydroxybenzohydrazide Hybrids

Antonijević, M. R., Avdović, E., Simijonović, D. M., Milanović, Ž., Amić, A., & Marković, Z. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23010490 · Cited by 17

The impact of artificial intelligence on the knowledge, attitude, and practice of pharmacists across diverse settings: A cross-sectional study

Jaber, D., Hasan, H. E., Abutaima, R., Sawan, H. M., & Tabbah, S. A. A. A. (2024) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2024.105656 · Cited by 17

From Pipeline to Plant Protection Products: Using New Approach Methodologies (NAMs) in Agrochemical Safety Assessment

Henriquez, J. E., Badwaik, V. D., Bianchi, E., Chen, W., Corvaro, M., LaRocca, J. L., Lunsman, T. D., Zu, C., & Johnson, K. J. (2024) · Journal of Agricultural and Food Chemistry · doi:10.1021/acs.jafc.4c00958 · Cited by 16

Transmol: repurposing a language model for molecular generation

Zhumagambetov, R., Molnár, F., Peshkov, V. A., & Fazli, S. (2021) · RSC Advances · doi:10.1039/d1ra03086h · Cited by 16

Development and Evaluation of Geometry Optimization Algorithms in Conjunction with ANI Potentials

Hao, D., He, X., Roitberg, A. E., Zhang, S., & Wang, J. (2022) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.1c01043 · Cited by 18

The Brazilian compound library (BraCoLi) database: a repository of chemical and biological information for drug design

Veríssimo, G. C., dos Santos, V. S., de Almeida, I. A. D. R., Ruas, M. S. M., Coutinho, L. G., de Oliveira, R. B., Alves, R. J., & Maltarollo, V. G. (2022) · Molecular Diversity · doi:10.1007/s11030-022-10386-9 · Cited by 15

Highly selective and AI-predictable Se–N exchange chemistry between benzoselenazolones and boronic acids for programmable, parallel, and DNA-encoded library synthesis

Zhou, W., Wang, Y., Zhang, S., Zhang, C., Pang, J., Hou, S., Li, J., Yao, Y., Su, A., Ma, P., et al. (2025) · Chemical Science · doi:10.1039/d5sc05512a · Cited by 13

Characterizing emerging companies in computational drug development

Markey, C., Croset, S., Woolley-Meza, O., Buldun, C. M., Koch, C. P., Koller, D., & Reker, D. (2024) · Nature Computational Science · doi:10.1038/s43588-024-00594-8 · Cited by 13

Models of necessity

Clark, T., & Hicks, M. G. (2020) · Beilstein Journal of Organic Chemistry · doi:10.3762/bjoc.16.137 · Cited by 13

Bio-inspired microfluidics: A review

Kiran Raj, M., Priyadarshani, J., Karan, P., Bandyopadhyay, S., Bhattacharya, S., & Chakraborty, S. K. (2023) · Biomicrofluidics · doi:10.1063/5.0161809 · Cited by 13

Artificial Intelligence and Its Applications in Drug Discovery, Formulation Development, and Healthcare

Banerjee, D., Rajput, D., Banerjee, S., & Saharan, V. A. (2022) · Springer eBooks · doi:10.1007/978-981-16-5180-9_11 · Cited by 12

Computational anti-COVID-19 drug design: progress and challenges

Wang, J., Zhang, Y., Nie, W., Luo, Y., & Deng, L. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab484 · Cited by 12

HS-DTI: Drug-target Interaction Prediction Based on Hierarchical Networks and Multi-order Sequence Effect

Gong, X., Liu, M., Sun, H., Li, M., & Liu, Q. (2022) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm55620.2022.9994908 · Cited by 11

AI-Driven Drug Discovery and Development

Afrose, N., Chakraborty, R., Hazra, A., Bhowmick, P., & Bhowmick, M. (2024) · Advances in medical diagnosis, treatment, and care (AMDTC) book series · doi:10.4018/979-8-3693-3629-8.ch013 · Cited by 13

Drug discovery in the context of precision medicine and artificial intelligence

Hasanzad, M., Nosrati, M., Khatami, F., Rahmani, P., Sarhangi, N., Nikfar, S., & Abdollahi, M. (2024) · Expert Review of Precision Medicine and Drug Development · doi:10.1080/23808993.2024.2393089 · Cited by 13

Aiming to Enhance Higher Education Practice Through Generative AI Integration: A Theoretical Exploration of Critical Success Factors

Wang, J., & Chuanbo, Z. (2025) · SAGE Open · doi:10.1177/21582440251342464 · Cited by 15

Interpretable Molecular Property Predictions Using Marginalized Graph Kernels

Xiang, Y., Tang, Y., Lin, G., & Reker, D. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00396 · Cited by 13

cheML.io: an online database of ML-generated molecules

Zhumagambetov, R., Kazbek, D., Shakipov, M., Maksut, D., Peshkov, V. A., & Fazli, S. (2020) · RSC Advances · doi:10.1039/d0ra07820d · Cited by 10

AI-driven design and applications of quantum dots

Soni, P. K., Satnami, M. L., Nagwanshi, R., Chawre, Y., Kujur, A. B., Sinha, A., Miri, P., Karbhal, I., & Ghosh, K. K. (2026) · Nano Today · doi:10.1016/j.nantod.2026.102974 · Cited by 12

Predicting partition coefficients for the SAMPL7 physical property challenge using the ClassicalGSG method

Donyapour, N., & Dickson, A. (2021) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-021-00400-x · Cited by 10

Examining the Reliability of ChatGPT: Identifying Retracted Scientific Literature and Ensuring Accurate Citations and References

Jan, R. (2024) · Advances in Educational Technologies and Instructional Design · doi:10.4018/979-8-3693-0884-4.ch014 · Cited by 9

Finding the most potent compounds using active learning on molecular pairs

Fralish, Z., & Reker, D. (2024) · Beilstein Journal of Organic Chemistry · doi:10.3762/bjoc.20.185 · Cited by 9

A comprehensive review on the application of artificial intelligence in drug discovery.

Sahoo, A., & Dar, G. M. (2021) · The Applied Biology & Chemistry Journal · doi:10.52679/tabcj.2021.0007 · Cited by 11

ARTIFICIAL INTELLIGENCE IN PHARMACY DRUG DESIGN

Kalayil, N., D'Souza, S. S., Khan, S., & PAUL, P. (2022) · Asian Journal of Pharmaceutical and Clinical Research · doi:10.22159/ajpcr.2022.v15i4.43890 · Cited by 9

Computational Approaches to Designing Antiviral Drugs against COVID-19: A Comprehensive Review

Singh, M. P., Singh, N., Mishra, D., Ehsan, S., Chaturvedi, V. K., Chaudhary, A., Singh, V., & Vamanu, E. (2023) · Current Pharmaceutical Design · doi:10.2174/0113816128259795231023193419 · Cited by 9

Novel Reagent Space: Identifying Unorderable but Readily Synthesizable Building Blocks

Seierstad, M. J., Tichenor, M. S., DesJarlais, R. L., Na, J., Bacani, G. M., Chung, D. M., Mercado-Marin, E. V., Steffens, H. C., & Mirzadegan, T. (2021) · ACS Medicinal Chemistry Letters · doi:10.1021/acsmedchemlett.1c00340 · Cited by 9

Molecular Representations in Machine-Learning-Based Prediction of PK Parameters for Insulin Analogs

Einarson, K. A., Bendtsen, K. M., Li, K., Thomsen, M., Kristensen, N. R., Winther, O., Fulle, S., Clemmensen, L. K. H., & Refsgaard, H. H. F. (2023) · ACS Omega · doi:10.1021/acsomega.3c01218 · Cited by 11

Machine Learning-Assisted Iterative Screening for Efficient Detection of Drug Discovery Starting Points

Müller, A. T., Hierl, M., Heer, D., Westwood, P., Hartz, P., Wörsdörfer, B., Krämer, C., Haap, W. J., Roth, D., & Reutlinger, M. (2025) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.5c00943 · Cited by 10

How AI application in pharmaceutical industries is beneficial to materials science

Suzuki, H., Kurosawa, S., Marcella, S., Kanba, M., Koretaka, Y., Tsuji, A., & Okumura, T. (2021) · Journal of Physics D Applied Physics · doi:10.1088/1361-6463/ac3a48 · Cited by 8

The Application of Artificial Intelligence and Drug Repositioning for the Identification of Fibroblast Growth Factor Receptor Inhibitors: A Review

Zarei, P., & Ghasemi, F. (2024) · Advanced Biomedical Research · doi:10.4103/abr.abr_170_23 · Cited by 8

Practical constraints with machine learning in drug discovery

Baskin, I. I. (2021) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2021.1887133 · Cited by 7

From Plants to Patients: Advancing Cancer Therapy With Bioengineered Exosomes and AI ‐Driven Innovations

Atia, G. A. N., Abdeen, A., Taher, E. S., Alghonemy, W. Y., Nashar, E. M. E., Al-Zahrani, N. S., Elmorsy, E. M., Khanday, S., Alshambky, A., Taymour, N., et al. (2025) · Phytotherapy Research · doi:10.1002/ptr.70040 · Cited by 7

Harnessing artificial intelligence for the analysis of complex chemical combinations, paving the way for novel flavors in food manufacturing: A comprehensive review

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