Cited by 1,573Computational Drug Discovery MethodsMachine Learning in Materials ScienceProtein Degradation and Inhibitors

The DOI matches its Crossref record; 49 of the 57 references it cites were found in a registry or an index.

Deep learning enables rapid identification of potent DDR1 kinase inhibitors

Zhavoronkov, Alex, Ivanenkov, Yan A., Aliper, Alex, Veselov, Mark S., Aladinskiy, Vladimir A., Aladinskaya, Anastasiya V., Terentiev, Victor A., Polykovskiy, Daniil A., Kuznetsov, Maksim D., Asadulaev, Arip, Volkov, Yury, Zholus, Artem, Shayakhmetov, Rim R., Zhebrak, Alexander, Minaeva, Lidiya I., Zagribelnyy, Bogdan A., Lee, Lennart H., Soll, Richard, Madge, David, Xing, Li, Guo, Tao, Aspuru-Guzik, Alán·2019·Nature Biotechnology

DOI 10.1038/s41587-019-0224-x

Open this paper in CoChat — free account

Abstract

We have developed a deep generative model, generative tensorial reinforcement learning (GENTRL), for de novo small-molecule design. GENTRL optimizes synthetic feasibility, novelty, and biological activity. We used GENTRL to discover potent inhibitors of discoidin domain receptor 1 (DDR1), a kinase target implicated in fibrosis and other diseases, in 21 days. Four compounds were active in biochemical assays, and two were validated in cell-based assays. One lead candidate was tested and demonstrated favorable pharmacokinetics in mice.

Integrity signals

Registry

Crossref · 10.1038/s41587-019-0224-x

Cites

49 of 49 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1179 · Cited by 25,545
GAN（Generative Adversarial Nets）
淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 20,507
UFF, a full periodic table force field for molecular mechanics and molecular dynamics simulations
Rappé, A. K., Casewit, C. J., Colwell, K. S., Goddard, W. A., & Skiff, W. M. (1992) · Journal of the American Chemical Society · doi:10.1021/ja00051a040 · Cited by 10,603
Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
Williams, R. J. (1992) · Machine Learning · doi:10.1007/bf00992696 · Cited by 8,608
How to improve R&D productivity: the pharmaceutical industry's grand challenge
Paul, S. M., Mytelka, D. S., Dunwiddie, C. T., Persinger, C. C., Munos, B. H., Lindborg, S. R., & Schacht, A. L. (2010) · Nature Reviews Drug Discovery · doi:10.1038/nrd3078 · Cited by 3,566
A Nonlinear Mapping for Data Structure Analysis
Sammon, J. W. (1969) · IEEE Transactions on Computers · doi:10.1109/t-c.1969.222678 · Cited by 3,364
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
Self-citation
Tensor-Train Decomposition
Oseledets, I. V. (2011) · SIAM Journal on Scientific Computing · doi:10.1137/090752286 · Cited by 2,860
ZINC: A Free Tool to Discover Chemistry for Biology
Irwin, J. J., Sterling, T., Mysinger, M. M., Bolstad, E. S. D., & Coleman, R. G. (2012) · Journal of Chemical Information and Modeling · doi:10.1021/ci3001277 · Cited by 2,611

Show all 57 references

Research works printed but not linked: 2

Goodfellow, I. et al. Generative adversarial nets. in Advances in Neural Information Processing Systems 2672–2680 (2014).
Williams, R. J. Mach. Learn. 8, 229–256 (1992).

Sorted by citations

Items 101–200 of 999

On failure modes in molecule generation and optimization
Renz, P., Rompaey, D. V., Wegner, J. K., Hochreiter, S., & Klambauer, G. (2019) · Drug Discovery Today Technologies · doi:10.1016/j.ddtec.2020.09.003 · Cited by 168
DeepMAge: A Methylation Aging Clock Developed with Deep Learning
Galkin, F., Mamoshina, P., Kochetov, K., Sidorenko, D., & Zhavoronkov, A. A. (2021) · Aging and Disease · doi:10.14336/ad.2020.1202 · Cited by 156
Self-citation
Deep learning approaches for de novo drug design: An overview
Wang, M., Wang, Z., Sun, H., Wang, J., Shen, C., Weng, G., Chai, X., Li, H., Cao, D., & Hou, T. (2021) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2021.10.001 · Cited by 159
Application of Computational Biology and Artificial Intelligence in Drug Design
Zhang, Y., Luo, M., Wu, P., Wu, S., Lee, T.-Y., & Bai, C. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms232113568 · Cited by 167
Application of artificial intelligence and machine learning in early detection of adverse drug reactions (ADRs) and drug-induced toxicity
Yang, S., & Kar, S. (2023) · Artificial Intelligence Chemistry · doi:10.1016/j.aichem.2023.100011 · Cited by 162
The future of pharmaceuticals: Artificial intelligence in drug discovery and development
Fu, C., & Chen, Q. (2025) · Journal of Pharmaceutical Analysis · doi:10.1016/j.jpha.2025.101248 · Cited by 151
Generating Ampicillin-Level Antimicrobial Peptides with Activity-Aware Generative Adversarial Networks
Tučs, A., Tran, D. P., Yumoto, A., Ito, Y., Uzawa, T., & Tsuda, K. (2020) · ACS Omega · doi:10.1021/acsomega.0c02088 · Cited by 155
Accelerated rational PROTAC design via deep learning and molecular simulations
Zheng, S., Tan, Y., Wang, Z., Li, C., Zhang, Z., Sang, X., Chen, H., & Yang, Y. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00527-y · Cited by 152
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
Self-citation
Generative Artificial Intelligence Use in Healthcare: Opportunities for Clinical Excellence and Administrative Efficiency
Bhuyan, S. S., Sateesh, V., Mukul, N., Galvankar, A., Mahmood, A., Nauman, M., Rai, A., Bordoloi, K., Basu, U., & Samuel, J. (2025) · Journal of Medical Systems · doi:10.1007/s10916-024-02136-1 · Cited by 145
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 148
Comprehensive Survey of Recent Drug Discovery Using Deep Learning
Kim, J., Park, S., Min, D., & Kim, W. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22189983 · Cited by 147
Nobel Turing Challenge: creating the engine for scientific discovery
Kitano, H. (2021) · npj Systems Biology and Applications · doi:10.1038/s41540-021-00189-3 · Cited by 149
In silico Methods for Design of Kinase Inhibitors as Anticancer Drugs
Gagić, Ž., Ružić, D., Djoković, N., Djikić, T., & Nikolic, K. M. (2020) · Frontiers in Chemistry · doi:10.3389/fchem.2019.00873 · Cited by 148
Large language models surpass human experts in predicting neuroscience results
Luo, X., Rechardt, A., Sun, G., Nejad, K. K., Yáñez, F., Yilmaz, B., Lee, K., Cohen, A. O., Borghesani, V., Pashkov, A. A., et al. (2024) · Nature Human Behaviour · doi:10.1038/s41562-024-02046-9 · Cited by 204
Receptor tyrosine kinase inhibitors in cancer
Ebrahimi, N., Fardi, E., Ghaderi, H., Palizdar, S., Khorram, R., Vafadar, R., Ghanaatian, M., Rezaei‐Tazangi, F., Baziyar, P., Ahmadi, A., et al. (2023) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-023-04729-4 · Cited by 150
Artificial intelligence in early drug discovery enabling precision medicine
Boniolo, F., Dorigatti, E., Ohnmacht, A. J., Saur, D., Schubert, B., & Menden, M. P. (2021) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2021.1918096 · Cited by 147
Is Target-Based Drug Discovery Efficient? Discovery and “Off-Target” Mechanisms of All Drugs
Sadri, A. (2023) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.2c01737 · Cited by 149
tmQM Dataset─Quantum Geometries and Properties of 86k Transition Metal Complexes
Balcells, D., & Skjelstad, B. B. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c01041 · Cited by 143
Evolving scenario of big data and Artificial Intelligence (AI) in drug discovery
Tripathi, M. K., Nath, A., Singh, T. P., Ethayathulla, A. S., & Kaur, P. (2021) · Molecular Diversity · doi:10.1007/s11030-021-10256-w · Cited by 151
Artificial Intelligence in Pharmaceutical Sciences
Lu, M., Yin, J., Zhu, Q., Lin, G., Mou, M., Liu, F., Pan, Z., You, N., Lian, X., Li, F., et al. (2023) · Engineering · doi:10.1016/j.eng.2023.01.014 · Cited by 145
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 144
Precision Hypertension
Dzau, V. J., & Hodgkinson, C. P. (2023) · Hypertension · doi:10.1161/hypertensionaha.123.21710 · Cited by 155
Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches
Kim, H., Kim, E., Lee, I., Bae, B., Park, M., & Nam, H. (2020) · Biotechnology and Bioprocess Engineering · doi:10.1007/s12257-020-0049-y · Cited by 146
Memory-assisted reinforcement learning for diverse molecular de novo design
Blaschke, T., Engkvist, O., Bajorath, J., & Chen, H. (2020) · Journal of Cheminformatics · doi:10.1186/s13321-020-00473-0 · Cited by 135
Artificial Intelligence‐Based Data‐Driven Strategy to Accelerate Research, Development, and Clinical Trials of COVID Vaccine
Sharma, A., Virmani, T., Pathak, V., Sharma, A., Pathak, K., Kumar, G., & Pathak, D. (2022) · BioMed Research International · doi:10.1155/2022/7205241 · Cited by 146
Defining and Exploring Chemical Spaces
Coley, C. W. (2020) · Trends in Chemistry · doi:10.1016/j.trechm.2020.11.004 · Cited by 134
Predicting or Pretending: Artificial Intelligence for Protein-Ligand Interactions Lack of Sufficiently Large and Unbiased Datasets
Yang, J., Shen, C., & Huang, N. (2020) · Frontiers in Pharmacology · doi:10.3389/fphar.2020.00069 · Cited by 128
De Novo Design of New Chemical Entities for SARS-CoV-2 Using Artificial Intelligence
Bung, N., Krishnan, S. R., Bulusu, G., & Roy, A. (2021) · Future Medicinal Chemistry · doi:10.4155/fmc-2020-0262 · Cited by 118
A review on compound-protein interaction prediction methods: Data, format, representation and model
Lim, S., Lu, Y., Cho, C. Y., Sung, I., Kim, J.-W., Kim, Y., Park, S., & Kim, S. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.03.004 · Cited by 120
Challenges and applications of artificial intelligence in infectious diseases and antimicrobial resistance
Cesaro, A., Hoffman, S. C., Das, P., & de la Fuente‐Núñez, C. (2025) · npj Antimicrobials and Resistance · doi:10.1038/s44259-024-00068-x · Cited by 145
Molecular docking-based computational platform for high-throughput virtual screening
Zhang, B., Li, H., Yu, K., & Jin, Z. (2022) · CCF Transactions on High Performance Computing · doi:10.1007/s42514-021-00086-5 · Cited by 132
Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations
Sharma, B., Chenthamarakshan, V., Dhurandhar, A., Pereira, S., Hendler, J., Dordick, J. S., & Das, P. (2023) · Scientific Reports · doi:10.1038/s41598-023-31169-8 · Cited by 125
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
Deep generative design with 3D pharmacophoric constraints
Imrie, F., Hadfield, T. E., Bradley, A. R., & Deane, C. M. (2021) · Chemical Science · doi:10.1039/d1sc02436a · Cited by 122
De Novo Drug Design Using Reinforcement Learning with Graph-Based Deep Generative Models
Atance, S. R., Diez, J. V., Engkvist, O., Olsson, S., & Mercado, R. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c00838 · Cited by 117
Transferable Multilevel Attention Neural Network for Accurate Prediction of Quantum Chemistry Properties via Multitask Learning
Liu, Z., Lin, L., Jia, Q., Cheng, Z., Jiang, Y., Guo, Y., & Ma, J. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c01224 · Cited by 115
OpenChem: A Deep Learning Toolkit for Computational Chemistry and Drug Design
Korshunova, M. V., Ginsburg, B., Tropsha, A., & Isayev, O. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00971 · Cited by 113
Foundation models for materials discovery – current state and future directions
Pyzer‐Knapp, E. O., Manica, M., Staar, P., Morin, L., Ruch, P., Laino, T., Smith, J. R., & Curioni, A. (2025) · npj Computational Materials · doi:10.1038/s41524-025-01538-0 · Cited by 144
Machine learning and AI-based approaches for bioactive ligand discovery and GPCR-ligand recognition
Raschka, S., & Kaufman, B. (2020) · Methods · doi:10.1016/j.ymeth.2020.06.016 · Cited by 115
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 113
Generating property-matched decoy molecules using deep learning
Imrie, F., Bradley, A. R., & Deane, C. M. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab080 · Cited by 113
Generative deep learning enables the discovery of a potent and selective RIPK1 inhibitor
Li, Y., Zhang, L., Wang, Y., Zou, J., Yang, R., Luo, X., Wu, C., Yang, W. M., Tian, C., Xu, H., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-34692-w · Cited by 113
Deep Generative Models in De Novo Drug Molecule Generation
Pang, C., Qiao, J., Zeng, X., Zou, Q., & Wei, L. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01496 · Cited by 113
Comparative study between deep learning and QSAR classifications for TNBC inhibitors and novel GPCR agonist discovery
Tsou, L. K., Yeh, S.-H., Ueng, S., Chang, C., Song, J., Wu, M.-H., Chang, H.-F., Chen, S.-R., Shih, C., Chen, C., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-73681-1 · Cited by 108
Machine Learning and Big Data Provide Crucial Insight for Future Biomaterials Discovery and Research
Kerner, J., Dogan, A. B., & von Recum, H. A. (2020) · Acta Biomaterialia · doi:10.1016/j.actbio.2021.05.053 · Cited by 106
The Journey of DDR1 and DDR2 Kinase Inhibitors as Rising Stars in the Fight Against Cancer
Elkamhawy, A., Lu, Q., Nada, H., Woo, J., Quan, G., & Lee, K. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22126535 · Cited by 104
Comparative analysis of molecular fingerprints in prediction of drug combination effects
Zagidullin, B., Wang, Z., Guan, Y., Pitkänen, E., & Tang, J. P. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab291 · Cited by 110
Bridging Nanomanufacturing and Artificial Intelligence—A Comprehensive Review
Nandipati, M., Fatoki, O., & Desai, S. S. (2024) · Materials · doi:10.3390/ma17071621 · Cited by 114
Deep learning in electron microscopy
Ede, J. M. (2020) · Machine Learning Science and Technology · doi:10.1088/2632-2153/abd614 · Cited by 118
Generative and reinforcement learning approaches for the automated de novo design of bioactive compounds
Korshunova, M. V., Huang, N., Capuzzi, S. J., Radchenko, D. S., Savych, O., Moroz, Y. S., Wells, C. I., Willson, T. M., Tropsha, A., & Isayev, O. (2022) · Communications Chemistry · doi:10.1038/s42004-022-00733-0 · Cited by 115
ResGen is a pocket-aware 3D molecular generation model based on parallel multiscale modelling
Zhang, O., Zhang, J., Jin, J., Zhang, X., Hu, R., Shen, C., Cao, H., Du, H., Kang, Y., Deng, Y., et al. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00712-7 · Cited by 103
A generative deep learning approach to de novo antibiotic design
Krishnan, A., Anahtar, M. N., Valeri, J. A., Jin, W., Donghia, N. M., Sieben, L., Luttens, A., Zhang, Y., Modaresi, S. M., Hennes, A. D., et al. (2025) · Cell · doi:10.1016/j.cell.2025.07.033 · Cited by 123
Quantum computing for near-term applications in generative chemistry and drug discovery.
Pyrkov, A., Aliper, A., Bezrukov, D., Lin, Y.-C., Polykovskiy, D., Kamya, P., Ren, F., & Zhavoronkov, A. (2023) · Drug Discovery Today · doi:10.1016/j.drudis.2023.103675 · Cited by 100
Self-citation
Comprehensive Survey of Consensus Docking for High-Throughput Virtual Screening
Blanes‐Mira, C., Fernández-Aguado, P., de Andrés-López, J., Fernández-Carvajal, A. M., Ferrer-Montiel, A. V., & Fernandez-Ballester, G. J. (2022) · Molecules · doi:10.3390/molecules28010175 · Cited by 113
Potential 2019-nCoV 3C-like Protease Inhibitors Designed Using Generative Deep Learning ApproachesPreprint
Zhavoronkov, A., Aladinskiy, V., Zhebrak, A., Zagribelnyy, B. A., Terentiev, V. A., Bezrukov, D., Polykovskiy, D. A., Shayakhmetov, R., Filimonov, A., Orekhov, P., et al. (2020) · ChemRxiv · doi:10.26434/chemrxiv.11829102.v1 · Cited by 108
Self-citation
RELATION: A Deep Generative Model for Structure-Based De Novo Drug Design
Wang, M., Hsieh, C.-Y., Wang, J., Wang, D., Weng, G., Shen, C., Yao, X., Bing, Z., Li, H., Cao, D., et al. (2022) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.2c00732 · Cited by 99
PaccMannRL: De novo generation of hit-like anticancer molecules from transcriptomic data via reinforcement learning
Born, J., Manica, M., Oskooei, A., Cadow, J., Markert, G., & Martínez, M. R. (2021) · iScience · doi:10.1016/j.isci.2021.102269 · Cited by 102
De novo design of novel protease inhibitor candidates in the treatment of SARS-CoV-2 using deep learning, docking, and molecular dynamic simulations
Arshia, A. H., Shadravan, S., Solhjoo, A., Sakhteman, A., & Sami, A. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104967 · Cited by 97
Differentiable biology: using deep learning for biophysics-based and data-driven modeling of molecular mechanisms
AlQuraishi, M., & Sorger, P. K. (2021) · Nature Methods · doi:10.1038/s41592-021-01283-4 · Cited by 96
De novo generation of multi-target compounds using deep generative chemistry
Munson, B. P., Chen, M., Bogosian, A., Kreisberg, J. F., Licon, K., Abagyan, R. A., Kuenzi, B. M., & Ideker, T. (2024) · Nature Communications · doi:10.1038/s41467-024-47120-y · Cited by 109
Geroprotective and senoremediative strategies to reduce the comorbidity, infection rates, severity, and lethality in gerophilic and gerolavic infections
Zhavoronkov, A. A. (2020) · Aging · doi:10.18632/aging.102988 · Cited by 93
Self-citation
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 advancements in organic synthesis: A focused exploration of artificial intelligence applications in chemistry
Ali, R. S. A. E., Meng, J., Khan, M. E. I., & Jiang, X. (2024) · Artificial Intelligence Chemistry · doi:10.1016/j.aichem.2024.100049 · Cited by 103
Exploring the Advantages of Quantum Generative Adversarial Networks in Generative Chemistry
Kao, P.-Y., Yang, Y.-C., Chiang, W.-Y., Hsiao, J.-Y., Cao, Y., Aliper, A., Ren, F., Aspuru‐Guzik, A., Zhavoronkov, A. A., Hsieh, M.-H., et al. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00562 · Cited by 109
Self-citation
Artificial intelligence in food science and nutrition: A narrative review
Miyazawa, T., Hiratsuka, Y., Toda, M., Hatakeyama, N., Ozawa, H., Abe, C., Cheng, T.-Y., Matsushima, Y., Miyawaki, Y., Ashida, K., et al. (2022) · Nutrition Reviews · doi:10.1093/nutrit/nuac033 · Cited by 91
Autonomous molecule generation using reinforcement learning and docking to develop potential novel inhibitors
Jeon, W., & Kim, D. (2020) · Scientific Reports · doi:10.1038/s41598-020-78537-2 · Cited by 93
Modeling the Solid Electrolyte Interphase: Machine Learning as a Game Changer?
Diddens, D., Appiah, W. A., Mabrouk, Y., Heuer, A., Vegge, T., & Bhowmik, A. (2022) · Advanced Materials Interfaces · doi:10.1002/admi.202101734 · Cited by 90
Implications of Artificial Intelligence in Addressing Antimicrobial Resistance: Innovations, Global Challenges, and Healthcare’s Future
Branda, F., & Scarpa, F. (2024) · Antibiotics · doi:10.3390/antibiotics13060502 · Cited by 104
Overcoming barriers to data sharing with medical image generation: a comprehensive evaluation
Schütte, A. D., Hetzel, J., Gatidis, S., Hepp, T., Dietz, B., Bauer, S., & Schwab, P. (2021) · Europe PMC (PubMed Central) · doi:10.3929/ethz-b-000507963 · Cited by 90
Machine Learning for Scent: Learning Generalizable Perceptual Representations of Small MoleculesPreprint
Sánchez-Lengeling, B., Wei, J. N., Lee, B. K., Gerkin, R. C., Aspuru‐Guzik, A., & Wiltschko, A. B. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.10685 · Cited by 164
Self-citation
Molecular Generative Model Based on an Adversarially Regularized Autoencoder
Hong, S. H., Ryu, S., Lim, J., & Kim, W. Y. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00694 · Cited by 92
De Novo Drug Design of Targeted Chemical Libraries Based on Artificial Intelligence and Pair-Based Multiobjective Optimization
Alberga, D., Gambacorta, N., Trisciuzzi, D., Ciriaco, F., Amoroso, N., & Nicolotti, O. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00517 · Cited by 94
An overview of kinase downregulators and recent advances in discovery approaches
Wang, B., Wu, H., Hu, C., Wang, H., Liu, J., Wang, W., & Liu, Q. (2021) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-021-00826-7 · Cited by 93
cMolGPT: A Conditional Generative Pre-Trained Transformer for Target-Specific De Novo Molecular Generation
Wang, Y., Zhao, H., Sciabola, S., & Wang, W. (2023) · Molecules · doi:10.3390/molecules28114430 · Cited by 95
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 103
3D molecular generative framework for interaction-guided drug design
(2024) · doi:10.1038/s41467-024-47011-2 · Cited by 88
A deep generative model enables automated structure elucidation of novel psychoactive substances
Skinnider, M. A., Wang, F., Pasin, D., Greiner, R., Foster, L. J., Dalsgaard, P. W., & Wishart, D. S. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00407-x · Cited by 96
Decoding disease: from genomes to networks to phenotypes
Wong, A. K., Sealfon, R. S. G., Theesfeld, C. L., & Troyanskaya, O. G. (2021) · Nature Reviews Genetics · doi:10.1038/s41576-021-00389-x · Cited by 90
Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation
Yan, X., Yue, T., Winkler, D. A., Yin, Y., Zhu, H., Jiang, G., & Yan, B. (2023) · Chemical Reviews · doi:10.1021/acs.chemrev.3c00070 · Cited by 89
Potential COVID-2019 3C-like Protease Inhibitors Designed Using Generative Deep Learning ApproachesPreprint
Zhavoronkov, A. A., Aladinskiy, V., Zhebrak, A., Zagribelnyy, B. A., Terentiev, V. A., Bezrukov, D. S., Polykovskiy, D. A., Shayakhmetov, R., Filimonov, A., Orekhov, P. S., et al. (2020) · ChemRxiv · doi:10.26434/chemrxiv.11829102 · Cited by 89
Self-citation
Machine Learning and Artificial Intelligence: A Paradigm Shift in Big Data-Driven Drug Design and Discovery
Pasrija, P., Jha, P. K., Upadhyaya, P., Khan, M. S., & Chopra, M. (2022) · Current Topics in Medicinal Chemistry · doi:10.2174/1568026622666220701091339 · Cited by 92
Artificial intelligence and big data facilitated targeted drug discovery
Liu, B., He, H., Luo, H., Zhang, T., & Jiang, J. (2019) · Stroke and Vascular Neurology · doi:10.1136/svn-2019-000290 · Cited by 81
Using GANs with adaptive training data to search for new molecules
Blanchard, A. E., Stanley, C. B., & Bhowmik, D. (2021) · Journal of Cheminformatics · doi:10.1186/s13321-021-00494-3 · Cited by 87
Quantifying the use and potential benefits of artificial intelligence in scientific research
Gao, J., & Wang, D. (2024) · Nature Human Behaviour · doi:10.1038/s41562-024-02020-5 · Cited by 81
Use of artificial intelligence to enhance phenotypic drug discovery
Malandraki-Miller, S., & Riley, P. R. (2021) · Drug Discovery Today · doi:10.1016/j.drudis.2021.01.013 · Cited by 84
Signaling Pathway and Small-Molecule Drug Discovery of FGFR: A Comprehensive Review
Zheng, J., Zhang, W., Li, L., He, Y., Wei, Y., Dang, Y., Nie, S., & Guo, Z. (2022) · Frontiers in Chemistry · doi:10.3389/fchem.2022.860985 · Cited by 81
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) · Journal of Cheminformatics · doi:10.1186/s13321-021-00516-0 · Cited by 78
“biased training or fine-tuning) [2, 6, 19], or using machine learning models trained on known bioactive molecules to predict de novo molecule bioactivity (e.”
Method
Big data and artificial intelligence in cancer research
Wu, X., Li, W., & Tu, H. (2023) · Trends in cancer · doi:10.1016/j.trecan.2023.10.006 · Cited by 83
Quantum-computing-enhanced algorithm unveils potential KRAS inhibitors
Vakili, M. G., Gorgulla, C., Snider, J. D., Nigam, A., Bezrukov, D. S., Varoli, D., Aliper, A., Polykovskiy, D. A., Das, K. M., Cox, H., et al. (2025) · Nature Biotechnology · doi:10.1038/s41587-024-02526-3 · Cited by 122
Self-citation
Generative AI for drug discovery and protein design: the next frontier in AI-driven molecular science
Das, U. (2025) · Medicine in Drug Discovery · doi:10.1016/j.medidd.2025.100213 · Cited by 91
Deep learning of pharmacogenomics resources: moving towards precision oncology
Chiu, Y., Chen, H.-I. H., Gorthi, A., Mostavi, M., Zheng, S., Huang, Y., & Chen, Y. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz144 · Cited by 80
Discovering small-molecule senolytics with deep neural networks
Wong, F., Omori, S., Donghia, N. M., Zheng, E. J., & Collins, J. J. (2023) · Nature Aging · doi:10.1038/s43587-023-00415-z · Cited by 82
Deep learning in medicine: advancing healthcare with intelligent solutions and the future of holography imaging in early diagnosis
Nazir, A., Hussain, A. B., Singh, M., & Assad, A. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-19694-8 · Cited by 86
Deep Learning Assisted Buildings Energy Consumption Profiling Using Smart Meter Data
Ullah, A., Haydarov, K., Haq, I. U., Muhammad, K., Rho, S., Lee, M. Y., & Baik, S. W. (2020) · Sensors · doi:10.3390/s20030873 · Cited by 77
The power of deep learning to ligand-based novel drug discovery
Baskin, I. I. (2020) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2020.1745183 · Cited by 76
Deep learning to design nuclear-targeting abiotic miniproteins
Schissel, C. K., Mohapatra, S., Wolfe, J. M., Fadzen, C. M., Bellovoda, K., Wu, C., Wood, J. A., Malmberg, A. B., Loas, A., Gómez‐Bombarelli, R., et al. (2021) · Nature Chemistry · doi:10.1038/s41557-021-00766-3 · Cited by 75
Hallmarks of artificial intelligence contributions to precision oncology
Chang, T., Park, S., Schäffer, A. A., Jiang, P., & Ruppin, E. (2025) · Nature Cancer · doi:10.1038/s43018-025-00917-2 · Cited by 83
Applied Artificial Intelligence in Materials Science and Material Design
Chávez‐Ángel, E., Eriksen, M., Castro‐Álvarez, A., García, J. H., Botifoll, M., Ávalos‐Ovando, Ó., Arbiol, J., & Mugarza, A. (2025) · Advanced Intelligent Systems · doi:10.1002/aisy.202400986 · Cited by 84
The role of nanomedicine and artificial intelligence in cancer health care: individual applications and emerging integrations—a narrative review
Prasanthi, S., Kumarachari, R. K., Bukke, S. P. N., Rajasekhar, E. S. K., Ashokkumar, J. A., & Eftekhari, Z. (2025) · Discover Oncology · doi:10.1007/s12672-025-02469-4 · Cited by 83

Authors: 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., Volkov, Y., Zholus, A., Shayakhmetov, R. R., Zhebrak, A., Minaeva, L. I., Zagribelnyy, B. A., Lee, L. H., Soll, R., Madge, D., Xing, L., Guo, T., & Aspuru-Guzik, A.
Affiliations: WuXi AppTec (China) (CN)
Corresponding author: Zhavoronkov, Alex
ORCID: Zhavoronkov, Alex
Ivanenkov, Yan A.
Aliper, Alex
Veselov, Mark S.
Aladinskiy, Vladimir A.
Terentiev, Victor A.
Polykovskiy, Daniil A.
Kuznetsov, Maksim D.
Asadulaev, Arip
Zholus, Artem
Shayakhmetov, Rim R.
Zhebrak, Alexander
Minaeva, Lidiya I.
Zagribelnyy, Bogdan A.
Lee, Lennart H.
Guo, Tao
Venue: Nature Biotechnology
Publisher: Springer Science and Business Media LLC
Published: 2019-09
Volume: 37
Issue: 9
Pages: 1038-1040
ISSN: 0733-222X (ISSN-L)
1087-0156
1546-1696
DOI: 10.1038/s41587-019-0224-x
OpenAlex: W2971690404
Cited by: 1,573
References: 57
Citations by year: 2026: 320 · 2025: 306 · 2024: 224 · 2023: 178 · 2022: 169 · 2021: 206 · 2020: 144 · 2019: 22
FWCI: 92.35
Type: journal article
Language: en
Open access: closed
Licence: www.springer.com/tdm (tdm)
www.springer.com/tdm (vor)
Locations: Nature Biotechnology (publishedVersion)
PubMed (publishedVersion)
Funders: Nvidia
Nature
School of Medicine, University of New Mexico
Topics: Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Degradation and Inhibitors
Keywords: DDR1, deep generative models, drug discovery, fibrosis, pharmacokinetics
MeSH terms: Discoidin Domain Receptor 1, Deep Learning, Animals, Dogs, Drug Evaluation, Preclinical, Enzyme Inhibitors, Humans, Microsomes, Liver, Models, Molecular, Protein Conformation, Molecular Structure, Mice, Rats

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Deep learning enables rapid identification of potent DDR1 kinase inhibitors

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Citations

Cites

Cited by

Related

Metadata

Record

About these checks