Chemistry42: An AI-Driven Platform for Molecular Design and Optimization

Highly accurate protein structure prediction with AlphaFold

Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · see citing works

Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings 1PII of original article: S0169-409X(96)00423-1. The article was originally published in Advanced Drug Delivery Reviews 23 (1997) 3–25. 1

Lipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J. (2001) · Advanced Drug Delivery Reviews · doi:10.1016/s0169-409x(00)00129-0 · Cited by 18,139 · see citing works

Self-Organizing Maps

Kohonen, T. (1995) · Springer series in information sciences · doi:10.1007/978-3-642-56927-2 · Cited by 16,032 · see citing works

New Substructure Filters for Removal of Pan Assay Interference Compounds (PAINS) from Screening Libraries and for Their Exclusion in Bioassays

Baell, J. B., & Holloway, G. A. (2010) · Journal of Medicinal Chemistry · doi:10.1021/jm901137j · Cited by 3,963 · see citing works

The rise of deep learning in drug discovery

Chen, H., Engkvist, O., Wang, Y., Olivecrona, M., & Blaschke, T. (2018) · Drug Discovery Today · doi:10.1016/j.drudis.2018.01.039 · Cited by 1,770 · 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

Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery

Yang, X. Y., Wang, Y., Byrne, R., Schneider, G., & Yang, S. (2019) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00728 · Cited by 1,045 · see citing works

Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models

Polykovskiy, D., Zhebrak, A., Sanchez-Lengeling, B., Golovanov, S., Tatanov, O., Belyaev, S., Kurbanov, R., Artamonov, A., Aladinskiy, V., Veselov, M., et al. (2020) · Frontiers in Pharmacology · doi:10.3389/fphar.2020.565644 · Cited by 849 · see citing works

PDB-wide collection of binding data: current status of the PDBbind database

Liu, Z., Li, Y., Han, L., Li, J., Liu, J., Zhao, Z., Nie, W., Liu, Y., & Wang, R. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu626 · Cited by 650 · see citing works

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 · see citing works

Predicting drug metabolism: experiment and/or computation?

Kirchmair, J., Göller, A. H., Lang, D. H., Kunze, J., Testa, B., Wilson, I. D., Glen, R. C., & Schneider, G. (2015) · Nature Reviews Drug Discovery · doi:10.1038/nrd4581 · Cited by 507 · see citing works

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 381 · 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

AlphaFold accelerates artificial intelligence powered drug discovery: efficient discovery of a novel CDK20 small molecule inhibitor

Ren, F., Ding, X., Zheng, M., Korzinkin, M., Cai, X., Zhu, W., Mantsyzov, A., Aliper, A., Aladinskiy, V., Cao, Z., et al. (2023) · Chemical Science · doi:10.1039/d2sc05709c · Cited by 365 · see citing works

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 · see citing works

Bifunctional immune checkpoint-targeted antibody-ligand traps that simultaneously disable TGFβ enhance the efficacy of cancer immunotherapy

Ravi, R., Noonan, K. A., Pham, V., Bedi, R., Zhavoronkov, A., Ozerov, I. V., Makarev, E., V. Artemov, A., Wysocki, P. T., Mehra, R., et al. (2018) · Nature Communications · doi:10.1038/s41467-017-02696-6 · Cited by 325 · see citing works

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 · see citing works

Evolutionary algorithms for de novo drug design – A survey

Devi, R. V., Sathya, S. S., & Coumar, M. S. (2014) · Applied Soft Computing · doi:10.1016/j.asoc.2014.09.042 · Cited by 151 · 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

Bioisosteres in Medicinal Chemistry

(2012) · Wiley eBooks · doi:10.1002/9783527654307 · Cited by 147 · see citing works

In silico Pathway Activation Network Decomposition Analysis (iPANDA) as a method for biomarker development

Ozerov, I. V., Lezhnina, K. V., Izumchenko, E., Artemov, A. V., Medintsev, S., Vanhaelen, Q., Aliper, A. M., Vijg, J., Osipov, A. N., Labat, I., et al. (2016) · Nature Communications · doi:10.1038/ncomms13427 · Cited by 136 · see citing works

Are We Opening the Door to a New Era of Medicinal Chemistry or Being Collapsed to a Chemical Singularity?

Ivanenkov, Y. A., Zagribelnyy, B. A., & Aladinskiy, V. (2019) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.9b00004 · Cited by 123 · see citing works

Privileged Structures in Drug Discovery

Yet, L. (2018) · Wiley eBooks · doi:10.1002/9781118686263 · Cited by 108 · see citing works

Enhancing Molecular Shape Comparison by Weighted Gaussian Functions

Yan, X., Li, J., Liu, Z., et al. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci300601q · Cited by 84 · see citing works

Immune profiles in primary squamous cell carcinoma of the head and neck

Saloura, V., Izumchenko, E., Zuo, Z., Bao, R., Korzinkin, M., Ozerov, I. V., Zhavoronkov, A. A., Sidransky, D., Bedi, A., Hoque, M. O., et al. (2019) · Oral Oncology · doi:10.1016/j.oraloncology.2019.06.032 · Cited by 72 · see citing works

MolGrow: A Graph Normalizing Flow for Hierarchical Molecular Generation

Kuznetsov, M., & Polykovskiy, D. A. (2021) · AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) · doi:10.1609/aaai.v35i9.17001 · Cited by 44 · see citing works

Has Drug Design Augmented by Artificial Intelligence Become a Reality?

Chen, H., & Engkvist, O. (2019) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2019.09.004 · Cited by 40 · 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

An analysis of gene expression data involving examination of signaling pathways activation reveals new insights into the mechanism of action of minoxidil topical foam in men with androgenetic alopecia

Stamatas, G. N., Wu, J., Pappas, A., Mirmirani, P., McCormick, T. S., Cooper, K. D., Consolo, M. C., Schastnaya, J., Ozerov, I. V., Aliper, A. M., et al. (2017) · Cell Cycle · doi:10.1080/15384101.2017.1327492 · Cited by 26 · see citing works

PIM1 kinase promotes gallbladder cancer cell proliferation via inhibition of proline-rich Akt substrate of 40 kDa (PRAS40)

Subbannayya, T., Leal, P., Zhavoronkov, A. A., et al. (2019) · Journal of Cell Communication and Signaling · doi:10.1007/s12079-018-00503-5 · Cited by 13 · see citing works

A small-molecule TNIK inhibitor targets fibrosis in preclinical and clinical models

Ren, F., Aliper, A., Chen, J., Zhao, H., Rao, S., Kuppe, C., Ozerov, I. V., Zhang, M., Witte, K., Kruse, C., et al. (2025) · Nature Biotechnology · doi:10.1038/s41587-024-02143-0 · Cited by 348 · see citing works

AI-powered therapeutic target discovery

Pun, F. W., Ozerov, I. V., & Zhavoronkov, A. (2023) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2023.06.010 · Cited by 341 · 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

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 · see citing works

Dynamic Selection of Component Implementations

Xu, M. (2004) · Jisuanji yingyong yanjiu

Legacy component integration by the Fujaba real-time tool suite

Henkler, S., Meyer, J., Schäfer, W., et al. (2010) · doi:10.1145/1810295.1810349 · Cited by 11 · see citing works

A Suite of Java Message-passing Benchmarks to Support the Validation of Testing Models, Criteria and Tools

Dourado, G. G. M., de Souza, P. S. L., Prado, R., et al. (2016) · Procedia Computer Science · doi:10.1016/j.procs.2016.05.387 · Cited by 5 · see citing works

JSBricks: a suite of microbenchmarks for the evaluation of Java as a scientific execution environment

Barisone, A., Bellotti, F., Berta, R., et al. (2001) · Future Generation Computer Systems · doi:10.1016/s0167-739x(00)00097-2 · Cited by 7 · see citing works

A Suite of Precise and Efficient Analyzing Techniques for Application Protocols

Qiao, M. (2010) · Computer Engineering and Science