Zhavoronkov, Alex

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

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 1,573

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

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 849

Applications of Deep Learning in Biomedicine

Mamoshina, P., Vieira, A., Lane, E., & Zhavoronkov, A. (2016) · Molecular Pharmaceutics

Computational Drug Discovery MethodsMachine Learning in Healthcare

Cited by 701

Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data

Aliper, A., Plis, S., Artemov, A., Ulloa, A., Mamoshina, P., & Zhavoronkov, A. (2016) · Molecular Pharmaceutics

Computational Drug Discovery MethodsBioinformatics and Genomic Networks

Cited by 610

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

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 587

Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare

Mamoshina, P., Ojomoko, L., Yanovich, Y., Ostrovski, A., Botezatu, A., Prikhodko, P., Izumchenko, E., Aliper, A., Romantsov, K., Zhebrak, A., et al. (2018) · Oncotarget

Blockchain Technology Applications and SecurityArtificial Intelligence in Healthcare and Education

Cited by 471

The role of DNA damage and repair in aging through the prism of Koch-like criteria

Moskalev, A. A., Shaposhnikov, M. V., Plyusnina, E. N., Zhavoronkov, A., Budovsky, A., Yanai, H., & Fraifeld, V. E. (2013) · Ageing Research Reviews

Genetics, Aging, and Longevity in Model OrganismsTelomeres, Telomerase, and Senescence

Cited by 387

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

Computational Drug Discovery MethodsProtein Degradation and Inhibitors

Cited by 365

Validation of biomarkers of aging

Moqri, M., Herzog, C., Poganik, J. R., Ying, K., Justice, J. N., Belsky, D. W., Higgins-Chen, A. T., Chen, B. H., Cohen, A. A., Fuellen, G., et al. (2024) · Nature Medicine

Genetics, Aging, and Longevity in Model OrganismsTelomeres, Telomerase, and Senescence

Cited by 361

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

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 350

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

Interstitial Lung Diseases and Idiopathic Pulmonary FibrosisComputational Drug Discovery Methods

Cited by 348

AI-powered therapeutic target discovery

Pun, F. W., Ozerov, I. V., & Zhavoronkov, A. (2023) · Trends in Pharmacological Sciences

Computational Drug Discovery MethodsProtein Degradation and Inhibitors

Cited by 341

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

Cancer Immunotherapy and BiomarkersTGF-β signaling in diseases

Cited by 325

Human gut microbiome aging clock based on taxonomic profiling and deep learning

Galkin, F., Mamoshina, P., Aliper, A., Lane, E., Moskalev, V., Gladyshev, V. N., & Zhavoronkov, A. (2020) · iScience

Gut microbiota and healthOral microbiology and periodontitis research

Cited by 292

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

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 278

Artificial intelligence for aging and longevity research: Recent advances and perspectives

Zhavoronkov, A., Mamoshina, P., Vanhaelen, Q., Scheibye-Knudsen, M., Moskalev, A., & Aliper, A. (2019) · Ageing Research Reviews

Genetics, Aging, and Longevity in Model OrganismsComputational Drug Discovery Methods

Cited by 246

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

Ivanenkov, Y. A., Polykovskiy, D., Bezrukov, D., Zagribelnyy, B., Aladinskiy, V., Kamya, P., Aliper, A., Ren, F., & Zhavoronkov, A. (2023) · Journal of Chemical Information and Modeling

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 231

Rapamycin in the context of Pascal's Wager: generative pre-trained transformer perspective

Generative Pre-trained Transformer, C., & Zhavoronkov, A. (2022) · Oncoscience

Genetics, Aging, and Longevity in Model OrganismsArtificial Intelligence in Healthcare and Education

Cited by 203

Biohorology and biomarkers of aging: Current state-of-the-art, challenges and opportunities

Galkin, F., Mamoshina, P., Aliper, A., de Magalhães, J. P., Gladyshev, V. N., & Zhavoronkov, A. (2020) · Ageing Research Reviews

Genetics, Aging, and Longevity in Model OrganismsTelomeres, Telomerase, and Senescence

Cited by 189

A generative AI-discovered TNIK inhibitor for idiopathic pulmonary fibrosis: a randomized phase 2a trial

Xu, Z., Ren, F., Wang, P., Cao, J., Tan, C., Ma, D., Zhao, L., Dai, J., Ding, Y., Fang, H., et al. (2025) · Nature Medicine

Interstitial Lung Diseases and Idiopathic Pulmonary FibrosisComputational Drug Discovery Methods

Cited by 180

Will Artificial Intelligence for Drug Discovery Impact Clinical Pharmacology?

Zhavoronkov, A., Vanhaelen, Q., & Oprea, T. I. (2020) · Clinical Pharmacology & Therapeutics

Computational Drug Discovery MethodsArtificial Intelligence in Healthcare and Education

Cited by 159

The Advent of Generative Chemistry

Vanhaelen, Q., Lin, Y.-C., & Zhavoronkov, A. (2020) · ACS Medicinal Chemistry Letters

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 151

Design of efficient computational workflows for in silico drug repurposing

Vanhaelen, Q., Mamoshina, P., Aliper, A. M., Artemov, A., Lezhnina, K., Ozerov, I., Labat, I., & Zhavoronkov, A. (2017) · Drug Discovery Today

Computational Drug Discovery Methodsvaccines and immunoinformatics approaches

Cited by 150

Identification of Therapeutic Targets for Amyotrophic Lateral Sclerosis Using PandaOmics – An AI-Enabled Biological Target Discovery Platform

Pun, F. W., Liu, B. H. M., Long, X., Leung, H. W., Leung, G. H. D., Mewborne, Q. T., Gao, J., Shneyderman, A., Ozerov, I. V., Wang, J., et al. (2022) · Frontiers in Aging Neuroscience

Amyotrophic Lateral Sclerosis ResearchComputational Drug Discovery Methods

Cited by 115