Deep learning enables rapid identification of potent DDR1 kinase inhibitors

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

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

Quantifying the chemical beauty of drugs

Bickerton, G. R., Paolini, G. V., Besnard, J., Mureşan, S., & Hopkins, A. L. (2012) · Nature Chemistry · doi:10.1038/nchem.1243 · Cited by 2,397

Inverse molecular design using machine learning: Generative models for matter engineering

Sánchez-Lengeling, B., & Aspuru‐Guzik, A. (2018) · Science · doi:10.1126/science.aat2663 · Cited by 2,177

Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions

Ertl, P., & Schuffenhauer, A. (2009) · Journal of Cheminformatics · doi:10.1186/1758-2946-1-8 · Cited by 1,939

Planning chemical syntheses with deep neural networks and symbolic AI

Segler, M. H. S., Preuss, M., & Waller, M. P. (2018) · Nature · doi:10.1038/nature25978 · Cited by 1,847

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

Prediction of Physicochemical Parameters by Atomic Contributions

Wildman, S. A., & Crippen, G. M. (1999) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci990307l · Cited by 1,491

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

Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation ModelsPreprint

Guimaraes, G. L., Sánchez-Lengeling, B., Outeiral, C., Farias, P. L. C., & Aspuru‐Guzik, A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.10843 · Cited by 702

Applications of Deep Learning in Biomedicine

Mamoshina, P., Vieira, A., Lane, E., & Zhavoronkov, A. (2016) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.5b00982 · Cited by 701

Deep learning for molecular design—a review of the state of the art

Elton, D. C., Boukouvalas, Z., Fuge, M. D., & Chung, P. W. (2019) · Molecular Systems Design & Engineering · doi:10.1039/c9me00039a · Cited by 683

Self-organizing semantic maps

Ritter, H. J., & Kohonen, T. (1989) · Biological Cybernetics · doi:10.1007/bf00203171 · Cited by 626

druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico

Kadurin, A., Nikolenko, S., Khrabrov, K., Aliper, A., & Zhavoronkov, A. (2017) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.7b00346 · Cited by 587

Reinforced Adversarial Neural Computer for de Novo Molecular Design

Lane, E., Asadulaev, A., Ivanenkov, Y. A., Aladinskiy, V., Sánchez-Lengeling, B., Aspuru‐Guzik, A., & Zhavoronkov, A. A. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00690 · Cited by 388

De Novo 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

The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology

Kadurin, A., Aliper, A., Kazennov, A., Mamoshina, P., Vanhaelen, Q., Khrabrov, K., & Zhavoronkov, A. (2017) · Oncotarget · doi:10.18632/oncotarget.14073 · Cited by 350

The $2.6 Billion Pill — Methodologic and Policy Considerations

Avorn, J. (2015) · New England Journal of Medicine · doi:10.1056/nejmp1500848 · Cited by 322

Entangled Conditional Adversarial Autoencoder for de Novo Drug Discovery

Polykovskiy, D., Zhebrak, A., Vetrov, D., Ivanenkov, Y., Aladinskiy, V., Mamoshina, P., Bozdaganyan, M., Aliper, A., Zhavoronkov, A., & Kadurin, A. (2018) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.8b00839 · Cited by 278

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 226

Discovery and Optimization of 3-(2-(Pyrazolo[1,5-a]pyrimidin-6-yl)ethynyl)benzamides as Novel Selective and Orally Bioavailable Discoidin Domain Receptor 1 (DDR1) Inhibitors

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

Discovery of a Potent and Selective DDR1 Receptor Tyrosine Kinase Inhibitor

Kim, H.-G., Tan, L., Weisberg, E., Liu, F., Canning, P., Choi, H. G., Ezell, S. A., Wu, H., Zhao, Z., Wang, J., et al. (2013) · ACS Chemical Biology · doi:10.1021/cb400430t · Cited by 157

Optimizing distributions over molecular space. An Objective-Reinforced Generative Adversarial Network for Inverse-design Chemistry (ORGANIC)Preprint

Sánchez-Lengeling, B., Outeiral, C., Guimaraes, G. L., & Aspuru‐Guzik, A. (2017) · ChemRxiv · doi:10.26434/chemrxiv.5309668.v2 · Cited by 156

Tuning artificial intelligence on the de novo design of natural-product-inspired retinoid X receptor modulators

Merk, D., Grisoni, F., Friedrich, L., & Schneider, G. (2018) · Communications Chemistry · doi:10.1038/s42004-018-0068-1 · Cited by 136

DNA-Encoded Library-Derived DDR1 Inhibitor Prevents Fibrosis and Renal Function Loss in a Genetic Mouse Model of Alport Syndrome

Richter, H. G., Satz, A. L., Bedoucha, M., Buettelmann, B., Petersen, A. C., Harmeier, A., Hermosilla, R., Hochstrasser, R., Burger, D., Gsell, B., et al. (2018) · ACS Chemical Biology · doi:10.1021/acschembio.8b00866 · Cited by 122

Structural Mechanisms Determining Inhibition of the Collagen Receptor DDR1 by Selective and Multi-Targeted Type II Kinase Inhibitors

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 109

3D Molecular Representations Based on the Wave Transform for Convolutional Neural Networks

Kuzminykh, D., Polykovskiy, D. A., Kadurin, A., Zhebrak, A., Baskov, I., Nikolenko, S., Shayakhmetov, R., & Zhavoronkov, A. A. (2018) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.7b01134 · Cited by 107

Synthesis of 1,1‐Diborylalkenes through a Brønsted Base Catalyzed Reaction between Terminal Alkynes and Bis(pinacolato)diboron

Morinaga, A., Nagao, K., Ohmiya, H., & Sawamura, M. (2015) · Angewandte Chemie International Edition · doi:10.1002/anie.201509218 · Cited by 102

DDR1 role in fibrosis and its pharmacological targeting

Moll, S., Desmoulière, A., Moeller, M. J., Pache, J., Badi, L., Arcadu, F., Richter, H. G., Satz, A. L., Uhles, S., Cavalli, A., et al. (2019) · Biochimica et Biophysica Acta (BBA) - Molecular Cell Research · doi:10.1016/j.bbamcr.2019.04.004 · Cited by 90

Synthesis and antitrichinellosis activity of some 2-substituted-[1,3]thiazolo[3,2-a]benzimidazol-3(2H)-ones

Mavrova, A. T., Anichina, K. K., Vuchev, D., Tsenov, J. A., Kondeva, M. S., & Micheva, M. K. (2005) · Bioorganic & Medicinal Chemistry · doi:10.1016/j.bmc.2005.06.046 · Cited by 77

Small Molecule Discoidin Domain Receptor Kinase Inhibitors and Potential Medical Applications

Li, Y., Lu, X., Ren, X., & Ding, K. (2015) · Journal of Medicinal Chemistry · doi:10.1021/jm5012319 · Cited by 72

Structure-Based Design of Tetrahydroisoquinoline-7-carboxamides as Selective Discoidin Domain Receptor 1 (DDR1) Inhibitors

Wang, Z., Bian, H., Bartual, S. G., Du, W., Luo, J., Zhao, H., Zhang, S., Mo, C., Zhou, Y., Xu, Y., et al. (2016) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.6b00140 · Cited by 67

Fragment-Based Discovery of Potent and Selective DDR1/2 Inhibitors

Murray, C. W., Berdini, V., Buck, I. M., Carr, M. E., Cleasby, A., Coyle, J. E., Curry, J. E., Day, J. E. H., Day, P. J., Hearn, K., et al. (2015) · ACS Medicinal Chemistry Letters · doi:10.1021/acsmedchemlett.5b00143 · Cited by 65

Prototype-Based Compound Discovery Using Deep Generative Models

Harel, S., & Radinsky, K. (2018) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.8b00474 · Cited by 48

Tetrahydroisoquinoline-7-carboxamide Derivatives as New Selective Discoidin Domain Receptor 1 (DDR1) Inhibitors

Wang, Z., Zhang, Y., Bartual, S. G., Luo, J., Xu, T., Du, W., Xun, Q., Tu, Z., Brekken, R. A., Ren, X., et al. (2017) · ACS Medicinal Chemistry Letters · doi:10.1021/acsmedchemlett.6b00497 · Cited by 41

Discovery of a broad spectrum antiproliferative agent with selectivity for DDR1 kinase: cell line-based assay, kinase panel, molecular docking, and toxicity studies

Elkamhawy, A., Park, J., Cho, N., Sim, T., Pae, A. N., & Roh, E. J. (2015) · Journal of Enzyme Inhibition and Medicinal Chemistry · doi:10.3109/14756366.2015.1004057 · Cited by 34

Preparation of diamines by lithiation–substitution of imidazolidines and pyrimidines

Ashweek, N. J., Coldham, I., Haxell, T. F. N., & Howard, S. (2003) · Organic & Biomolecular Chemistry · doi:10.1039/b301502e · Cited by 33

Synthesis and biological evaluation of novel dasatinib analogues as potent DDR 1 and DDR 2 kinase inhibitors

Liu, L., Hussain, M., Luo, J., Duan, A., Chen, C., Tu, Z., & Zhang, J. (2016) · Chemical Biology & Drug Design · doi:10.1111/cbdd.12863 · Cited by 33

eCF309 : a potent, selective and cell-permeable mTOR inhibitor

Fraser, C., Carragher, N. O., & Unciti‐Broceta, A. (2015) · MedChemComm · doi:10.1039/c5md00493d · Cited by 23

Discovery of N-(3-((1-Isonicotinoylpiperidin-4-yl)oxy)-4-methylphenyl)-3-(trifluoromethyl)benzamide (CHMFL-KIT-110) as a Selective, Potent, and Orally Available Type II c-KIT Kinase Inhibitor for Gastrointestinal Stromal Tumors (GISTs)

Wang, Q., Liu, F., Wang, B., Zou, F., Chen, C., Liu, X., Wang, A., Qi, S., Wang, W., Qi, Z., et al. (2016) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.6b00200 · Cited by 15

Crystal Packing: An Examination of the Packing of Molecules Approximately Isosteric with 4,5‐Dichlorophthalic Anhydride

Britton, D., Noland, W. E., Pinnow, M. J., & Young, V. G. (2003) · Helvetica Chimica Acta · doi:10.1002/hlca.200390102 · Cited by 8

AI in health and medicine

Rajpurkar, P., Chen, E., Banerjee, O., & Topol, E. J. (2022) · Nature Medicine · doi:10.1038/s41591-021-01614-0 · Cited by 3,212

A guide to machine learning for biologists

Greener, J. G., Kandathil, S. M., Moffat, L., & Jones, D. T. (2022) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-021-00407-0 · Cited by 2,127

Scientific discovery in the age of artificial intelligence.

Wang, H., Fu, T., Du, Y., Gao, W., Huang, K., Liu, Z., Chandak, P., Liu, S., Van Katwyk, P., Deac, A., et al. (2023) · Nature · doi:10.1038/s41586-023-06221-2 · Cited by 1,823

Artificial intelligence: A powerful paradigm for scientific research

Xu, Y., Liu, X., Cao, X., Huang, C., Liu, E., Qian, S., Liu, X., Wu, Y., Dong, F., Qiu, C.-W., et al. (2021) · The Innovation · doi:10.1016/j.xinn.2021.100179 · Cited by 1,726

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

Deep learning identifies DDR1 kinase inhibitors

Crunkhorn, S. (2019) · Nature Reviews Drug Discovery · doi:10.1038/d41573-019-00170-0 · Cited by 3

Design and Synthesis of DDR1 Inhibitors with a Desired Pharmacophore Using Deep Generative Models

Yoshimori, A., Asawa, Y., Kawasaki, E., Tasaka, T., Matsuda, S., Sekikawa, T., Tanabe, S., Neya, M., Natsugari, H., & Kanai, C. (2020) · ChemMedChem · doi:10.1002/cmdc.202000786 · Cited by 48

Discovery of Pyrazolo[3,4- d ]pyridazinone Derivatives as Selective DDR1 Inhibitors via Deep Learning Based Design, Synthesis, and Biological Evaluation

Tan, X., Li, C., Yang, R., Zhao, S., Li, F., Li, X., Chen, L., Wan, X., Liu, X., Yang, T., et al. (2021) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.1c01205 · Cited by 68

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

Machine Learning-Driven De Novo Design of DDP-4 Targeted Drug Molecules Using RNN-Based Generative Models and Reinforcement Learning

Lou, J. (2025) · Theoretical and Natural Science · doi:10.54254/2753-8818/2025.au28436