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Deep Learning for Molecular Generation

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Computer-aided multi-objective optimization in small molecule discovery

Fromer, J. C., & Coley, C. W. (2023) · Patterns · doi:10.1016/j.patter.2023.100678 · Cited by 130 · see citing works

Defining and Exploring Chemical Spaces

Coley, C. W. (2020) · Trends in Chemistry · doi:10.1016/j.trechm.2020.11.004 · Cited by 126 · see citing works

CReM: chemically reasonable mutations framework for structure generation

Полищук, П. Г. (2020) · Journal of Cheminformatics · doi:10.1186/s13321-020-00431-w · Cited by 119 · see citing works

Deep learning and knowledge-based methods for computer-aided molecular design—toward a unified approach: State-of-the-art and future directions

Alshehri, A. S., Gani, R., & You, F. (2020) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2020.107005 · Cited by 119 · see citing works

Relevant Applications of Generative Adversarial Networks in Drug Design and Discovery: Molecular De Novo Design, Dimensionality Reduction, and De Novo Peptide and Protein Design

Lin, E., Lin, C., & Lane, H. (2020) · Molecules · doi:10.3390/molecules25143250 · Cited by 112 · see citing works

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 106 · see citing works

Quantum computing for near-term applications in generative chemistry and drug discovery.

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Generative Models for Automatic Chemical Design

Schwalbe‐Koda, D., & Gómez‐Bombarelli, R. (2020) · Lecture notes in physics · doi:10.1007/978-3-030-40245-7_21 · Cited by 100 · see citing works

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 98 · see citing works

Masked graph modeling for molecule generation

Mahmood, O., Mansimov, E., Bonneau, R., & Cho, K. (2021) · Nature Communications · doi:10.1038/s41467-021-23415-2 · Cited by 92 · see citing works

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 90 · see citing works

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 · see citing works

Deep Aging Clocks: The Emergence of AI-Based Biomarkers of Aging and Longevity

Zhavoronkov, A. A., & Mamoshina, P. (2019) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2019.05.004 · Cited by 88 · see citing works

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 88 · see citing works

Digital transformation of the Pharmaceutical Industry: A future research agenda for management studies

Miozza, M., Brunetta, F., & Appio, F. P. (2024) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2024.123580 · Cited by 85 · see citing works

Deep Convolutional Generative Adversarial Network (dcGAN) Models for Screening and Design of Small Molecules Targeting Cannabinoid Receptors

Bian, Y., Wang, J., Jun, J. J., & Xie, X. (2019) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.9b00500 · Cited by 79 · see citing works

Computational applications using data driven modeling in process Systems: A review

Bishnu, S. K., Alnouri, S. Y., & Al‐Mohannadi, D. M. (2023) · Digital Chemical Engineering · doi:10.1016/j.dche.2023.100111 · Cited by 78 · see citing works

Allosteric Regulation at the Crossroads of New Technologies: Multiscale Modeling, Networks, and Machine Learning

Verkhivker, G. M., Agajanian, S., Hu, G., & Tao, P. (2020) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2020.00136 · Cited by 76 · see citing works

Guidelines for Recurrent Neural Network Transfer Learning-Based Molecular Generation of Focused Libraries

Amabilino, S., Pogány, P., Pickett, S. D., & Green, D. V. S. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00343 · Cited by 74 · see citing works

Open Macromolecular Genome: Generative Design of Synthetically Accessible Polymers

Kim, S., Schroeder, C. M., & Jackson, N. E. (2023) · ACS Polymers Au · doi:10.1021/acspolymersau.3c00003 · Cited by 74 · see citing works

Pathway-Guided Deep Neural Network toward Interpretable and Predictive Modeling of Drug Sensitivity

Deng, L., Cai, Y., Zhang, W., Yang, W., Gao, B., & Liu, H. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00331 · Cited by 71 · see citing works

Machine Learning and Deep Learning in Data-Driven Decision Making of Drug Discovery and Challenges in High-Quality Data Acquisition in the Pharmaceutical Industry

Kumar, S. A., Kumar, T. D. A., Beeraka, N. M., Pujar, G. V., Singh, M., Akshatha, H. S., & Bhagyalalitha, M. (2021) · Future Medicinal Chemistry · doi:10.4155/fmc-2021-0243 · Cited by 71 · see citing works

Deep Learning in Diverse Intelligent Sensor Based Systems

Zhu, Y., Wang, M., Yin, X., Zhang, J., Meijering, E. H. W., & Hu, J. (2022) · Sensors · doi:10.3390/s23010062 · Cited by 67 · see citing works

Deep biomarkers of aging and longevity: from research to applications

Zhavoronkov, A. A., Li, R., Ma, C., & Mamoshina, P. (2019) · Aging · doi:10.18632/aging.102475 · Cited by 63 · see citing works

Emerging biotechnology applications in natural product and synthetic pharmaceutical analyses

Chen, S., Li, Z., Zhang, S., Zhou, Y., Xiao, X., Cui, P., Xu, B., Zhao, Q., Kong, S., & Dai, Y. (2022) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2022.08.025 · Cited by 63 · see citing works

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.v2 · Cited by 63 · see citing works

Artificial intelligence in the pharmaceutical sector: current scene and future prospect

Kalyane, D., Sanap, G., Paul, D., Shenoy, S., Anup, N., Polaka, S., Tambe, V., & Tekade, R. K. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-814455-8.00003-7 · Cited by 61 · see citing works

Recent advances and application of generative adversarial networks in drug discovery, development, and targeting

Tripathi, S., Augustin, A. I., Dunlop, A., Sukumaran, R., Dheer, S., Zavalny, A., Haslam, O., Austin, T. C., Donchez, J., Tripathi, P. K., et al. (2022) · Artificial Intelligence in the Life Sciences · doi:10.1016/j.ailsci.2022.100045 · Cited by 61 · see citing works

Accelerating drug target inhibitor discovery with a deep generative foundation model

Chenthamarakshan, V., Hoffman, S. C., Owen, C. D., Lukacik, P., Strain-Damerell, C. M., Fearon, D., Malla, T. R., Tumber, A., Schofield, C. J., Duyvesteyn, H. M. E., et al. (2023) · Science Advances · doi:10.1126/sciadv.adg7865 · Cited by 61 · see citing works

Computer-Aided Drug Design

Bharatam, P. V. (2021) · Springer eBooks · doi:10.1007/978-981-15-5534-3_6 · Cited by 58 · see citing works

Drug Analogs from Fragment-Based Long Short-Term Memory Generative Neural Networks

Awale, M., Sirockin, F., Stiefl, N., & Reymond, J. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00902 · Cited by 58 · see citing works

Applications of Deep-Learning in Exploiting Large-Scale and Heterogeneous Compound Data in Industrial Pharmaceutical Research

David, L., Arús‐Pous, J., Karlsson, J., Engkvist, O., Bjerrum, E. J., Kogej, T., Kriegl, J. M., Beck, B., & Chen, H. (2019) · Frontiers in Pharmacology · doi:10.3389/fphar.2019.01303 · Cited by 58 · see citing works

Computer-aided molecular design of solvents for chemical separation processes

Chai, S., Song, Z., Zhou, T., Zhang, L., & Qi, Z. (2021) · Current Opinion in Chemical Engineering · doi:10.1016/j.coche.2021.100732 · Cited by 57 · see citing works

Computational Approaches for De Novo Drug Design: Past, Present, and Future

Liu, X., IJzerman, A. P., & van Westen, G. J. P. (2020) · Methods in molecular biology · doi:10.1007/978-1-0716-0826-5_6 · Cited by 51 · see citing works

Advanced AI and ML frameworks for transforming drug discovery and optimization: With innovative insights in polypharmacology, drug repurposing, combination therapy and nanomedicine

Ambreen, S., Umar, M., Noor, A., Jain, H., & Ali, R. (2024) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2024.117164 · Cited by 50 · see citing works

nach0: multimodal natural and chemical languages foundation model

Livne, M., Miftahutdinov, Z., Tutubalina, E. V., Kuznetsov, M., Polykovskiy, D. A., Brundyn, A., Jhunjhunwala, A., Costa, A. B., Aliper, A., Aspuru‐Guzik, A., et al. (2024) · Chemical Science · doi:10.1039/d4sc00966e · Cited by 50 · see citing works

Engineering a Sustainable Future: Harnessing Automation, Robotics, and Artificial Intelligence with Self-Driving Laboratories

Sadeghi, S., Canty, R. B., Mukhin, N., Xu, J., Delgado‐Licona, F., & Abolhasani, M. (2024) · ACS Sustainable Chemistry & Engineering · doi:10.1021/acssuschemeng.4c02177 · Cited by 48 · see citing works

Shape-based Machine Learning Models for the Potential Novel COVID-19 Protease Inhibitors Assisted by Molecular Dynamics Simulation

Nayarisseri, A., Khandelwal, R., Madhavi, M., Selvaraj, C., Panwar, U., Sharma, K., Hussain, T., & Singh, S. K. (2020) · Current Topics in Medicinal Chemistry · doi:10.2174/1568026620666200704135327 · Cited by 48 · see citing works

Generative Model for Proposing Drug Candidates Satisfying Anticancer Properties Using a Conditional Variational Autoencoder

Joo, S., Kim, M. S., Yang, J., & Park, J. (2020) · ACS Omega · doi:10.1021/acsomega.0c01149 · Cited by 48 · see citing works

Generative Deep Learning for de Novo Drug Design─A Chemical Space Odyssey

Özçelik, R., Brinkmann, H., Criscuolo, E., & Grisoni, F. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c00641 · Cited by 48 · see citing works

A systematic review of deep learning chemical language models in recent era

Flores-Hernandez, H., & Martínez-Ledesma, E. (2024) · Journal of Cheminformatics · doi:10.1186/s13321-024-00916-y · Cited by 46 · see citing works

In Search of Beautiful Molecules: A Perspective on Generative Modeling for Drug Design

van den Broek, R. L., Patel, S., van Westen, G. J. P., Jespers, W., & Sherman, W. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c01203 · Cited by 46 · see citing works

Application of Artificial Intelligence in Drug Discovery

Chopra, H., Baig, A. A., Gautam, R. K., & Kamal, M. A. (2022) · Current Pharmaceutical Design · doi:10.2174/1381612828666220608141049 · Cited by 45 · see citing works

Molecular Generation for Desired Transcriptome Changes With Adversarial Autoencoders

Shayakhmetov, R., Kuznetsov, M., Zhebrak, A., Kadurin, A., Nikolenko, S., Aliper, A. M., & Polykovskiy, D. A. (2020) · Frontiers in Pharmacology · doi:10.3389/fphar.2020.00269 · 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

Potential Non-Covalent SARS-CoV-2 3C-like Protease Inhibitors Designed Using Generative Deep Learning Approaches and Reviewed by Human Medicinal Chemist in Virtual RealityPreprint

Zhavoronkov, A. A., Zagribelnyy, B. A., Zhebrak, A., Aladinskiy, V., Terentiev, V. A., Vanhaelen, Q., Bezrukov, D. S., Polykovskiy, D. A., Shayakhmetov, R., Filimonov, A., et al. (2020) · ChemRxiv · doi:10.26434/chemrxiv.12301457.v1 · Cited by 44 · see citing works

A novel, covalent broad-spectrum inhibitor targeting human coronavirus Mpro

Sun, J., Sun, D., Yang, Q., Wang, D., Peng, J., Guo, H., Ding, X., Zhao, C., Yuan, B., Ivanenkov, Y. A., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-59870-4 · Cited by 40 · 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

Optimizing drug design by merging generative AI with a physics-based active learning framework

Filella-Mercè, I., Molina, A., Díaz, L., Orzechowski, M., Berchiche, Y. A., Zhu, Y. M., Vilalta-Mor, J., Malo, L., Yekkirala, A. S., Ray, S. S., et al. (2025) · Communications Chemistry · doi:10.1038/s42004-025-01635-7 · Cited by 39 · see citing works

Virtual Screening Meets Deep Learning

Pérez-Sianes, J., Pérez‐Sánchez, H., & Díaz, F. (2018) · Current Computer - Aided Drug Design · doi:10.2174/1573409914666181018141602 · Cited by 38 · see citing works

Compressed graph representation for scalable molecular graph generation

Kwon, Y., Lee, D., Choi, Y.-S., Shin, K., & Kang, S. (2020) · Journal of Cheminformatics · doi:10.1186/s13321-020-00463-2 · Cited by 38 · see citing works

Discovery of Tetrahydropyrazolopyrazine Derivatives as Potent and Selective MYT1 Inhibitors for the Treatment of Cancer

Wang, Y., Wang, C., Liu, T., Qi, H., Chen, S., Cai, X., Zhang, M., Aliper, A., Ren, F., Ding, X., et al. (2023) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.3c01476 · Cited by 37 · see citing works

SELFIES-based adversarial autoencoder framework for de novo drug-like molecule generation

Kamma, V., Vinuthna, K., Madhiraju, P., Reddy, A. B. S., & Ballem, R. (2026) · CRC Press eBooks · doi:10.1201/9781003679622-98

De Novo Molecular Generation with Stacked Adversarial Model

Liu, Y., & Bailey, J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-97546-3_12 · Cited by 3 · see citing works

Application of Generative Autoencoder in <i>De Novo</i> Molecular Design

Blaschke, T., Olivecrona, M., Engkvist, O., Bajorath, J., & Chen, H. (2018) · Molecular Informatics · doi:10.1002/minf.201700123 · Cited by 411 · see citing works

GAMS: Graph-Based Autoregressive Molecular Generation System for Drug Discovery

Sha</p>, <. (2025) · Frontiers in Medical Science Research · doi:10.25236/fmsr.2025.070604

Molecular generative model based on conditional variational autoencoder for de novo molecular design

Lim, J., Ryu, S., Kim, J. W., & Kim, W. Y. (2018) · Journal of Cheminformatics · doi:10.1186/s13321-018-0286-7 · Cited by 421 · see citing works