Assessing the impact of generative AI on medicinal chemistry
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Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612 · see citing works
Weininger, D. (1988) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci00057a005 · Cited by 7,436 · see citing works
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 · see citing works
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 · see citing works
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) · Cambridge University Engineering Department Publications Database · doi:10.17863/cam.34134 · Cited by 1,813 · see citing works
Deep learning enables rapid identification of potent DDR1 kinase inhibitors
Zhavoronkov, A., Ivanenkov, Y. A., Aliper, A., Veselov, M. S., Aladinskiy, V. A., Aladinskaya, A. V., Terentiev, V. A., Polykovskiy, D. A., Kuznetsov, M. D., Asadulaev, A., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0224-x · Cited by 1,573 · see citing works
Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks
Segler, M. H. S., Kogej, T., Tyrchan, C., & Waller, M. P. (2018) · ACS Central Science · doi:10.1021/acscentsci.7b00512 · Cited by 1,537 · see citing works
Computer-based de novo design of drug-like molecules
Schneider, G., & Fechner, U. (2005) · Nature Reviews Drug Discovery · doi:10.1038/nrd1799 · Cited by 956 · see citing works
GuacaMol: Benchmarking Models for de Novo Molecular Design
Brown, N., Fiscato, M., Segler, M. H. S., & Vaucher, A. C. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00839 · Cited by 895 · see citing works
Kadurin, A., Nikolenko, S., Khrabrov, K., Aliper, A., & Zhavoronkov, A. (2017) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.7b00346 · Cited by 587 · see citing works
<i>De Novo</i> Design of Bioactive Small Molecules by Artificial Intelligence
Merk, D., Friedrich, L., Grisoni, F., & Schneider, G. (2018) · Molecular Informatics · doi:10.1002/minf.201700153 · Cited by 373 · see citing works
Adversarial Threshold Neural Computer for Molecular de Novo Design
Lane, E., Asadulaev, A., Vanhaelen, Q., Ivanenkov, Y. A., Aladinskaya, A. V., Aliper, A., & Zhavoronkov, A. A. (2018) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.7b01137 · Cited by 219 · see citing works
Ponatinib: a novel multi-tyrosine kinase inhibitor against human malignancies
Tan, F. H., Putoczki, T. L., Stylli, S. S., & Luwor, R. B. (2019) · OncoTargets and Therapy · doi:10.2147/ott.s189391 · Cited by 206 · see citing works
Gao, M., Duan, L., Luo, J., Zhang, L., Lu, X., Zhang, Y., Zhang, Z., Tu, Z., Xu, Y., Ren, X., et al. (2013) · Journal of Medicinal Chemistry · doi:10.1021/jm301824k · Cited by 168 · see citing works
Canning, P., Tan, L., Chu, K., Lee, S. W., Gray, N. S., & Bullock, A. N. (2014) · Journal of Molecular Biology · doi:10.1016/j.jmb.2014.04.014 · Cited by 103 · see citing works
Drug Guru: A computer software program for drug design using medicinal chemistry rules
Stewart, K. D., Shiroda, M., & James, C. A. (2006) · Bioorganic & Medicinal Chemistry · doi:10.1016/j.bmc.2006.06.024 · Cited by 103 · see citing works
Autonomous Molecular Design: Then and Now
Dimitrov, T., Kreisbeck, C., Becker, J. S., Aspuru-Guzik, A., & Saikin, S. K. (2019) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.9b01226 · Cited by 103 · see citing works
Deep Generative Models for Molecular Science
Jørgensen, P. B., Schmidt, M. N., & Winther, O. (2018) · Molecular Informatics · doi:10.1002/minf.201700133 · Cited by 86 · see citing works
The scaffold hopping potential of pharmacophores
Heßler, G., & Baringhaus, K. (2010) · Drug Discovery Today Technologies · doi:10.1016/j.ddtec.2010.09.001 · Cited by 72 · see citing works
Generative Models for Artificially‐intelligent Molecular Design
Schneider, G. (2018) · Molecular Informatics · doi:10.1002/minf.201880131 · Cited by 37 · see citing works
A ring‐bracing approach to computer‐assisted ligand design
Leach, A. R., & Lewis, R. A. (1994) · Journal of Computational Chemistry · doi:10.1002/jcc.540150213 · Cited by 16 · see citing works
Computational Medicinal Chemistry
Ståhl, M., & Bajorath, J. (2010) · Journal of Medicinal Chemistry · doi:10.1021/jm1013055 · Cited by 10 · see citing works
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Generative artificial intelligence
Banh, L., & Strobel, G. (2023) · Electronic Markets · doi:10.1007/s12525-023-00680-1 · Cited by 722 · see citing works
Vatansever, S., Schlessinger, A., Wacker, D., Kaniskan, H. Ü., Jin, J., Zhou, M., & Zhang, B. (2021) · Medicinal Research Reviews · doi:10.1002/med.21764 · Cited by 429 · see citing works
Applications of Deep Learning in Molecule Generation and Molecular Property Prediction
Walters, W. P., & Barzilay, R. (2020) · Accounts of Chemical Research · doi:10.1021/acs.accounts.0c00699 · Cited by 393 · see citing works
Self-citationBengesi, S., El-Sayed, H., Sarker, M. K., Houkpati, Y., Irungu, J., & Oladunni, T. (2023) · IEEE Access · doi:10.1109/access.2024.3397775 · Cited by 286 · see citing works
Computer-Aided Drug Design and Drug Discovery: A Prospective Analysis
Niazi, S. K., & Mariam, Z. (2023) · Pharmaceuticals · doi:10.3390/ph17010022 · Cited by 361 · see citing works
Generative models for molecular discovery: Recent advances and challenges
Bilodeau, C., Jin, W., Jaakkola, T., Barzilay, R., & Jensen, K. F. (2022) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1608 · Cited by 333 · see citing works
AI in drug discovery and its clinical relevance
Qureshi, R., Irfan, M., Gondal, T. M., Khan, S., Wu, J., Hadi, M. U., Heymach, J., Le, X., Yan, H., & Alam, T. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e17575 · Cited by 319 · see citing works
De novo molecular design and generative models
Meyers, J., Fabian, B., & Brown, N. (2021) · Drug Discovery Today · doi:10.1016/j.drudis.2021.05.019 · Cited by 300 · see citing works
BindingDB in 2024: a FAIR knowledgebase of protein-small molecule binding data
Liu, T., Hwang, L., Burley, S. K., Nitsche, C. I., Southan, C., Walters, W. P., & Gilson, M. K. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae1075 · Cited by 246 · see citing works
Self-citationGenerative Models for De Novo Drug Design
Tong, X., Liu, X., Tan, X., Li, X., Jiang, J., Xiong, Z., Xu, T., Jiang, H., Qiao, N., & Zheng, M. (2021) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.1c00927 · Cited by 242 · see citing works
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 · see citing works
“Page | 14 Compound 1 (Fig 2) has been criticized for its similarity to the approved kinase inhibitor, ponatinib (136).”
BackgroundAdvanced machine-learning techniques in drug discovery
Elbadawi, M., Gaisford, S., & Basit, A. W. (2020) · Drug Discovery Today · doi:10.1016/j.drudis.2020.12.003 · Cited by 229 · see citing works
Artificial intelligence in drug discovery: applications and techniques
Deng, J., Yang, Z., Ojima, I., Samaras, D., & Wang, F. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab430 · Cited by 236 · see citing works
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 224 · see citing works
Learning Molecular Representations for Medicinal Chemistry
Chuang, K. V., Gunsalus, L. M., & Keiser, M. J. (2020) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.0c00385 · Cited by 227 · see citing works
A systematic study of key elements underlying molecular property prediction
Deng, J., Yang, Z., Wang, H., Ojima, I., Samaras, D., & Wang, F. (2023) · Nature Communications · doi:10.1038/s41467-023-41948-6 · Cited by 233 · see citing works
Machine learning in preclinical drug discovery
Catacutan, D. B., Alexander, J., Arnold, A., & Stokes, J. M. (2024) · Nature Chemical Biology · doi:10.1038/s41589-024-01679-1 · Cited by 219 · see citing works
Artificial intelligence foundation for therapeutic science
Huang, K., Fu, T., Gao, W., Zhao, Y., Roohani, Y., Leskovec, J., Coley, C. W., Xiao, C., Sun, J., & Zitnik, M. (2022) · Nature Chemical Biology · doi:10.1038/s41589-022-01131-2 · Cited by 207 · see citing works
Generative AI for designing and validating easily synthesizable and structurally novel antibiotics
Swanson, K., Liu, G., Catacutan, D. B., Arnold, A., Zou, J., & Stokes, J. M. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00809-7 · Cited by 190 · see citing works
Graph networks for molecular design
Mercado, R., Rastemo, T., Lindelöf, E., Klambauer, G., Engkvist, O., Chen, H., & Bjerrum, E. J. (2020) · Machine Learning Science and Technology · doi:10.1088/2632-2153/abcf91 · Cited by 186 · see citing works
Evaluation guidelines for machine learning tools in the chemical sciences
Bender, A., Schneider, N., Segler, M., Walters, W. P., Engkvist, O., & Rodrigues, T. (2022) · Nature Reviews Chemistry · doi:10.1038/s41570-022-00391-9 · Cited by 186 · see citing works
Self-citationEnhancing preclinical drug discovery with artificial intelligence
Vijayan, R., Kihlberg, J. O., Cross, J. B., & Poongavanam, V. (2021) · Drug Discovery Today · doi:10.1016/j.drudis.2021.11.023 · Cited by 184 · see citing works
Deep learning tools for advancing drug discovery and development
Nag, S., Baidya, A. T. K., Mandal, A., Mathew, A. T., Das, B., Devi, B., & Kumar, R. (2022) · 3 Biotech · doi:10.1007/s13205-022-03165-8 · Cited by 182 · see citing works
Machine‐learning scoring functions for structure‐based virtual screening
Li, H., Sze, K., Lü, G., & Ballester, P. J. (2020) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1478 · Cited by 167 · see citing works
Artificial intelligence in chemistry and drug design
Brown, N., Ertl, P., Lewis, R., Luksch, T., Reker, D., & Schneider, N. (2020) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-020-00317-x · Cited by 165 · see citing works
Machine learning and deep learning methods that use omics data for metastasis prediction
Albaradei, S., Thafar, M. A., Alsaedi, A., Neste, C. V., Gojobori, T., Essack, M., & Gao, X. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.09.001 · Cited by 165 · see citing works
Will Artificial Intelligence for Drug Discovery Impact Clinical Pharmacology?
Zhavoronkov, A., Vanhaelen, Q., & Oprea, T. I. (2020) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.1795 · Cited by 159 · see citing works
Molecular property prediction: recent trends in the era of artificial intelligence
Shen, J., & Nicolaou, C. A. (2019) · Drug Discovery Today Technologies · doi:10.1016/j.ddtec.2020.05.001 · Cited by 157 · see citing works
Leveraging Uncertainty in Machine Learning Accelerates Biological Discovery and Design
Hie, B., Bryson, B. D., & Berger, B. (2020) · Cell Systems · doi:10.1016/j.cels.2020.09.007 · Cited by 154 · see citing works
The Advent of Generative Chemistry
Vanhaelen, Q., Lin, Y.-C., & Zhavoronkov, A. (2020) · ACS Medicinal Chemistry Letters · doi:10.1021/acsmedchemlett.0c00088 · Cited by 151 · see citing works
AI-Aided Design of Novel Targeted Covalent Inhibitors against SARS-CoV-2
Tang, B., He, F., Liu, D., He, F., Wu, T., Fang, M., Niu, Z., Wu, Z., & Xu, D. H. (2022) · Biomolecules · doi:10.3390/biom12060746 · Cited by 144 · see citing works
AI is a viable alternative to high throughput screening: a 318-target study
Program, T. A. A., Wallach, I., Bernard, D., Nguyen, K. T., Ho, G., Morrison, A., Stecuła, A., Rosnik, A. M., O’Sullivan, A. M., Davtyan, A., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-54655-z · Cited by 137 · see citing works
Storey, V. C., Yue, W. T., Zhao, J. L., & Lukyanenko, R. (2025) · Information Systems Frontiers · doi:10.1007/s10796-025-10581-7 · Cited by 176 · see citing works
Large language models for reticular chemistry
Zheng, Z., Rampal, N., Inizan, T. J., Borgs, C., Chayes, J. T., & Yaghi, O. M. (2025) · Nature Reviews Materials · doi:10.1038/s41578-025-00772-8 · Cited by 134 · see citing works
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 116 · see citing works
Critical assessment of AI in drug discovery
Walters, W. P., & Barzilay, R. (2021) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2021.1915982 · Cited by 108 · see citing works
Self-citationTalat, A., & Khan, A. U. (2023) · Drug Discovery Today · doi:10.1016/j.drudis.2023.103491 · Cited by 96 · see citing works
Jeon, W., & Kim, D. (2020) · Scientific Reports · doi:10.1038/s41598-020-78537-2 · Cited by 91 · see citing works
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 88 · see citing works
LLMs and generative agent-based models for complex systems research
Lu, Y., Aleta, A., Du, C., Shi, L., & Moreno, Y. (2024) · Physics of Life Reviews · doi:10.1016/j.plrev.2024.10.013 · Cited by 82 · see citing works
Khemchandani, Y., O’Hagan, S., Samanta, S., Swainston, N., Roberts, T. J., Bollegala, D., & Kell, D. B. (2020) · Journal of Cheminformatics · doi:10.1186/s13321-020-00454-3 · Cited by 80 · see citing works
Thomas, M., Smith, R. T., O’Boyle, N. M., de Graaf, C., & Bender, A. (2021) · Journal of Cheminformatics · doi:10.1186/s13321-021-00516-0 · Cited by 78 · see citing works
“This lack of novelty observed in current deep generative models has also been commented on in the literature [30].”
BackgroundGeometry-complete diffusion for 3D molecule generation and optimization
Morehead, A., & Cheng, J. (2024) · Communications Chemistry · doi:10.1038/s42004-024-01233-z · Cited by 81 · see citing works
Learning To Navigate The Synthetically Accessible Chemical Space Using Reinforcement LearningPreprint
Gottipati, S. K., Sattarov, B., Niu, S., Pathak, Y., Wei, H., Liu, S., Thomas, K. M. J., Blackburn, S., Coley, C. W., Tang, J., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.12485 · Cited by 82 · see citing works
Woodruff, K. A., Hutson, J., & Arnone, K. (2023) · IntechOpen eBooks · doi:10.5772/intechopen.1002741 · Cited by 76 · see citing works
Xiao, H., Li, R., Shi, X., Chen, Y., Zhu, L., Chen, X., & Wang, L. (2023) · Nature Communications · doi:10.1038/s41467-023-42870-7 · Cited by 72 · see citing works
Santana, M. V., & Silva, F. P. (2021) · BMC Chemistry · doi:10.1186/s13065-021-00737-2 · Cited by 69 · see citing works
Artificial intelligence in the early stages of drug discovery
Cavasotto, C. N., & Filippo, J. I. D. (2020) · Archives of Biochemistry and Biophysics · doi:10.1016/j.abb.2020.108730 · Cited by 67 · see citing works
Artificial intelligence revolution in drug discovery: A paradigm shift in pharmaceutical innovation
Jarallah, S. J., Almughem, F. A., Alhumaid, N. K., Fayez, N. A., Alradwan, I. A., Alsulami, K. A., Tawfik, E. A., & Alshehri, A. A. (2025) · International Journal of Pharmaceutics · doi:10.1016/j.ijpharm.2025.125789 · Cited by 70 · see citing works
Guerrero, J. P., Nguewa, P., & García‐Sosa, A. T. (2021) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1513 · Cited by 61 · see citing works
Kell, D. B., Samanta, S., & Swainston, N. (2020) · Biochemical Journal · doi:10.1042/bcj20200781 · Cited by 56 · see citing works
Recent progress on cheminformatics approaches to epigenetic drug discovery
Sessions, Z. L., Sánchez‐Cruz, N., Prieto-Martínez, F. D., Alves, V. M., Junior, H. P. D. O. S., Muratov, E. N., Tropsha, A., & Medina‐Franco, J. L. (2020) · Drug Discovery Today · doi:10.1016/j.drudis.2020.09.021 · Cited by 54 · see citing works
Stecuła, A., Hussain, M. S., & Viola, R. E. (2020) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.0c00473 · Cited by 52 · see citing works
Stanley, M., & Segler, M. (2023) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2023.102658 · Cited by 48 · see citing works
AI-enhanced chemical paradigm: From molecular graphs to accurate prediction and mechanism
Huang, Z. Z., Jiang, Y. P., He, W., Yu, J., Deng, S., Yang, C., Zhu, W., & Shao, X. (2023) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2023.133355 · Cited by 47 · see citing works
AI-driven drug discovery: A boon against COVID-19?
Kaushik, A. C., & Raj, U. (2020) · AI Open · doi:10.1016/j.aiopen.2020.07.001 · Cited by 45 · see citing works
VAE-Sim: A Novel Molecular Similarity Measure Based on a Variational Autoencoder
Samanta, S., O’Hagan, S., Swainston, N., Roberts, T. J., & Kell, D. B. (2020) · Molecules · doi:10.3390/molecules25153446 · Cited by 44 · see citing works
On the Value of Using 3D Shape and Electrostatic Similarities in Deep Generative Methods
Bolcato, G., Heid, E., & Boström, J. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c01535 · Cited by 43 · see citing works
Applications of Artificial Intelligence in Drug Design: Opportunities and Challenges
Thomas, M. C., Boardman, A., García-Ortegón, M., Yang, H., de Graaf, C., & Bender, A. (2021) · Methods in molecular biology · doi:10.1007/978-1-0716-1787-8_1 · Cited by 42 · see citing works
Analyzing behavioral intentions toward Generative Artificial Intelligence: the case of ChatGPT
Nan, D., Sun, S., Zhang, S., Zhao, X., & Kim, J. H. (2024) · Universal Access in the Information Society · doi:10.1007/s10209-024-01116-z · Cited by 40 · see citing works
Molecular set representation learning
Boulougouri, M., Vandergheynst, P., & Probst, D. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00856-0 · Cited by 40 · see citing works
Duan, C., Nandy, A., Terrones, G., Kastner, D. W., & Kulik, H. J. (2022) · JACS Au · doi:10.1021/jacsau.2c00547 · Cited by 39 · see citing works
Impact of Artificial Intelligence on Compound Discovery, Design, and Synthesis
Miljković, F. N., Rodríguez-Pérez, R., & Bajorath, J. (2021) · ACS Omega · doi:10.1021/acsomega.1c05512 · Cited by 39 · see citing works
Keeping pace with the explosive growth of chemical libraries with structure‐based virtual screening
Kuan, J., Radaeva, M., Avenido, A., Cherkasov, A. R., & Gentile, F. (2023) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1678 · Cited by 43 · see citing works
Guan, S., & Wang, G. (2024) · Artificial Intelligence Chemistry · doi:10.1016/j.aichem.2024.100070 · Cited by 38 · see citing works
Thomas, M. C., O’Boyle, N. M., Bender, A., & de Graaf, C. (2024) · Journal of Cheminformatics · doi:10.1186/s13321-024-00861-w · Cited by 39 · see citing works
Artificial Intelligence, Machine Learning, and Deep Learning in Real-Life Drug Design Cases
Müller, C., Rabal, O., & Gonzalez, C. D. (2021) · Methods in molecular biology · doi:10.1007/978-1-0716-1787-8_16 · Cited by 37 · see citing works
Recent applications of artificial intelligence in RNA-targeted small molecule drug discovery
Morishita, E. C., & Nakamura, S. (2024) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2024.2313455 · Cited by 35 · see citing works
Accelerating discovery of bioactive ligands with pharmacophore-informed generative models
Xie, W., Zhang, J., Xie, Q., Gong, C., Ren, Y., Xie, J. H., Sun, Q., Xu, Y., Lai, L., & Pei, J. (2025) · Nature Communications · doi:10.1038/s41467-025-56349-0 · Cited by 35 · see citing works
Trends in Deep Learning for Property-driven Drug Design
Born, J., & Manica, M. (2021) · Current Medicinal Chemistry · doi:10.2174/0929867328666210729115728 · Cited by 35 · see citing works
State-of-the-art of Artificial Intelligence in Medicinal Chemistry
Bajorath, J. (2021) · Future Science OA · doi:10.2144/fsoa-2021-0030 · Cited by 33 · see citing works
Artificial Intelligence (AI) in Pharmaceutical Formulation and Dosage Calculations
Joshi, S., & Sheth, S. (2025) · Pharmaceutics · doi:10.3390/pharmaceutics17111440 · Cited by 37 · see citing works
Artificial Intelligence and Machine Learning for Lead-to-Candidate Decision-Making and Beyond
McNair, D. (2022) · The Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-051921-023255 · Cited by 32 · see citing works
Bian, Y., & Xie, X. (2022) · Cells · doi:10.3390/cells11050915 · Cited by 32 · see citing works
Deep Learning Methods for Small Molecule Drug Discovery: A Survey
Hu, W., Liu, Y., Chen, X., Chai, W., Chen, H., Wang, H., & Wang, G. (2023) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2023.3251977 · Cited by 30 · see citing works
Graph neural networks driven acceleration in drug discovery
Wang, R., & Zhuang, C. (2025) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2025.10.011 · Cited by 33 · see citing works
Schistosomiasis Drug Discovery in the Era of Automation and Artificial Intelligence
Moreira‐Filho, J. T., Silva, A. C., Dantas, R. F., Gomes, B. F., Neto, L. R. S., Brandão-Neto, J., Owens, R. J., Furnham, N., Neves, B. J., Silva, F. P., et al. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.642383 · Cited by 31 · see citing works
Revolutionizing Medical Practice: The Impact of Artificial Intelligence (AI) on Healthcare
(2024) · Open Access Journal of Applied Science and Technology · doi:10.33140/oajast.02.01.07 · Cited by 29 · see citing works
Semi-supervised Hierarchical Drug Embedding in Hyperbolic Space
Yu, K., Visweswaran, S., & Batmanghelich, K. N. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00681 · Cited by 28 · see citing works
Deep Learning Applied to Ligand-Based De Novo Drug Design
Palazzesi, F., & Pozzan, A. (2021) · Methods in molecular biology · doi:10.1007/978-1-0716-1787-8_12 · Cited by 30 · see citing works
Deep Machine Learning for Computer-Aided Drug Design
Bajorath, J. (2022) · Frontiers in Drug Discovery · doi:10.3389/fddsv.2022.829043 · Cited by 27 · see citing works
Cell morphology-guided de novo hit design by conditioning GANs on phenotypic image features
Zapata, P. A. M., Méndez‐Lucio, O., Le, T., Beese, C. J., Wichard, J., Rouquié, D., & Clevert, D.-A. (2022) · Digital Discovery · doi:10.1039/d2dd00081d · Cited by 29 · see citing works
Role of artificial intelligence in chemistry
Choudhary, N., Bharti, R., & Sharma, R. (2021) · Materials Today Proceedings · doi:10.1016/j.matpr.2021.09.428 · Cited by 29 · see citing works
Maria, J. P. S., Wang, Y., & Camargo, L. M. (2023) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2023.1121591 · Cited by 29 · see citing works
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 · see citing works
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 · see citing works
Generative AI for the Design of Molecules: Advances and Challenges
Sun, Y., Chen, L., Jing, Z., Li, Y. Y., Kim, D., Gao, J.-Y., Noroozi, R., Yi, G. Y., Feugmo, C. G. T., Klinkova, A., et al. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c02234 · Cited by 27 · see citing works
Data-Driven Cheminformatics Models for Predicting Bioactivity of Natural Compounds in Oncology
Atalor, S. I. (2022) · International Journal of Scientific Research and Modern Technology. · doi:10.38124/ijsrmt.v1i1.496 · Cited by 24 · see citing works
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 · see citing works
Hit and Lead Discovery with Explorative RL and Fragment-based Molecule GenerationPreprint
Yang, S., Hwang, D., Lee, S., Ryu, S., & Hwang, S. J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.01219 · Cited by 24 · see citing works
Artificial Intelligence in Toxicology and Pharmacology
Nasnodkar, S. P., Çınar, B., & Ness, S. (2023) · Journal of Engineering Research and Reports · doi:10.9734/jerr/2023/v25i7952 · Cited by 24 · see citing works
Kayode, G. O., & Montemore, M. M. (2023) · JACS Au · doi:10.1021/jacsau.3c00419 · Cited by 23 · see citing works
Ontology in Hybrid Intelligence: A Concise Literature Review
Pileggi, S. F. (2024) · Future Internet · doi:10.3390/fi16080268 · Cited by 28 · see citing works
Lim, S., Lee, S., Piao, Y., Choi, M., Bang, D., Gu, J., & Kim, S. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.07.049 · Cited by 23 · see citing works
Wang, Z., & You, F. (2025) · Nature Computational Science · doi:10.1038/s43588-025-00797-7 · Cited by 21 · see citing works
Bilsland, A. E., McAulay, K., West, R., Pugliese, A., & Bower, J. F. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c01226 · Cited by 21 · see citing works
Jeyaraj, G., Rajendran, A. K., Sathishkumar, K., Almutairi, B. O., Vadivelu, A., Chokkakula, S., Tu, Y., & Xie, W. (2025) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2025.1688455 · Cited by 23 · see citing works
Recent omics-based computational methods for COVID-19 drug discovery and repurposing
Tayara, H., Abdelbaky, I., & Chong, K. T. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab339 · Cited by 20 · see citing works
Sulley, G. A., & Montemore, M. M. (2022) · Current Opinion in Chemical Engineering · doi:10.1016/j.coche.2022.100821 · Cited by 21 · see citing works
Improving de novo Molecule Generation by Embedding LSTM and Attention Mechanism in CycleGAN
Wang, F., Feng, X., Guo, X., Xu, L., Xie, L., & Chang, S. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.709500 · Cited by 20 · see citing works
- Authors
- Walters, W. P., & Murcko, M.
- Affiliations
- Relay Therapeutics (United States) (US)
- Corresponding author
- Walters, W. Patrick
- Venue
- Nature Biotechnology
- Published
- 2020-01-30
- Volume
- 38
- Issue
- 2
- Pages
- 143-145
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- OpenAlex
- W3003906593
- Cited by
- 262
- References
- 23
- Citations by year
- 2026: 60 · 2025: 40 · 2024: 43 · 2023: 35 · 2022: 21 · 2021: 37 · 2020: 25 · 2019: 1
- Type
- dissertation
- Language
- en
- Open access
- closed
- Locations
- Nature Biotechnology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Keywords
- generative AI, medicinal chemistry, drug discovery, structure-based drug design, reinforcement learning, adenosine A2A receptor, AI impact assessment
- MeSH terms
- Artificial Intelligence, Chemistry, Pharmaceutical, Humans, Inhibitory Concentration 50, Quantitative Structure-Activity Relationship, Cell Line, Tumor, Protein Kinase Inhibitors, Drug Discovery
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