The Advent of Generative Chemistry

Long Short-Term Memory

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Auto-Encoding Variational Bayes

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A Brief Survey of Deep Reinforcement Learning

Arulkumaran, K., Deisenroth, M. P., Brundage, M., & Bharath, A. A. (2017) · arXiv · doi:10.48550/arxiv.1708.05866 · Cited by 4,733 · see citing works

Deep Reinforcement Learning: A Brief Survey

Arulkumaran, K., Deisenroth, M. P., Brundage, M., & Bharath, A. A. (2017) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2743240 · Cited by 4,733 · see citing works

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 · see citing works

Generative Adversarial Networks

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Quantifying the chemical beauty of drugs

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QSAR Modeling: Where have you been? Where are you going to?

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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 2,087 · see citing works

Prediction of Absolute Solvation Free Energies using Molecular Dynamics Free Energy Perturbation and the OPLS Force Field

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

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Planning chemical syntheses with deep neural networks and symbolic AI

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Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules.

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Junction Tree Variational Autoencoder for Molecular Graph GenerationPreprint

Jin, W.-G., Barzilay, R., & Jaakkola, T. (2018) · International Conference on Machine Learning · doi:10.48550/arxiv.1802.04364 · Cited by 1,783 · 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

NIPS 2016 Tutorial: Generative Adversarial NetworksPreprint

Goodfellow, I. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1701.00160 · Cited by 1,735 · see citing works

Molecular graph convolutions: moving beyond fingerprints

Kearnes, S., McCloskey, K., Berndl, M., Pande, V., & Riley, P. (2016) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-016-9938-8 · Cited by 1,631 · 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

Tutorial on Variational Autoencoders

Doersch, C. (2016) · arXiv · doi:10.48550/arxiv.1606.05908 · Cited by 1,470 · see citing works

Deep reinforcement learning for de novo drug design

Popova, M., Isayev, O., & Tropsha, A. (2018) · Science Advances · doi:10.1126/sciadv.aap7885 · Cited by 1,379 · see citing works

Molecular de-novo design through deep reinforcement learning

Olivecrona, M., Blaschke, T., Engkvist, O., & Chen, H. (2017) · Journal of Cheminformatics · doi:10.1186/s13321-017-0235-x · Cited by 1,358 · see citing works

Correlation of Biological Activity of Phenoxyacetic Acids with Hammett Substituent Constants and Partition Coefficients

HANSCH, C., MALONEY, P. P., FUJITA, T., & MUIR, R. M. (1962) · Nature · doi:10.1038/194178b0 · Cited by 1,056 · see citing works

Ultra-large library docking for discovering new chemotypes

Lyu, J., Wang, S., Balius, T. E., Singh, I., Levit, A., Moroz, Y. S., O’Meara, M. J., Che, T., Algaa, E., Tolmachova, K., et al. (2019) · Nature · doi:10.1038/s41586-019-0917-9 · Cited by 998 · see citing works

The Process of Structure-Based Drug Design

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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

Automated design of ligands to polypharmacological profiles

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The Chemical Space Project

Reymond, J.-L. (2015) · Accounts of Chemical Research · doi:10.1021/ar500432k · Cited by 720 · see citing works

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 · see citing works

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

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Organic synthesis in a modular robotic system driven by a chemical programming language

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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

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 586 · see citing works

Deep learning in biomedicine

Wainberg, M., Merico, D., Delong, A., & Frey, B. J. (2018) · Nature Biotechnology · doi:10.1038/nbt.4233 · Cited by 584 · see citing works

Entropy-Enthalpy Compensation: Role and Ramifications in Biomolecular Ligand Recognition and Design

Chodera, J. D., & Mobley, D. L. (2013) · Annual Review of Biophysics · doi:10.1146/annurev-biophys-083012-130318 · Cited by 531 · see citing works

Generative Recurrent Networks for De Novo Drug Design

Gupta, A., Müller, A. T., Huisman, B. J. H., Fuchs, J. A., Schneider, P., & Schneider, G. (2017) · Molecular Informatics · doi:10.1002/minf.201700111 · Cited by 474 · see citing works

MolGAN: An implicit generative model for small molecular graphs

De Cao, N., & Kipf, T. (2018) · arXiv · doi:10.48550/arxiv.1805.11973 · Cited by 442 · see citing works

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

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

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

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

Water Networks Contribute to Enthalpy/Entropy Compensation in Protein–Ligand Binding

Breiten, B., Lockett, M. R., Sherman, W., Fujita, S., Al-Sayah, M. H., Lange, H., Bowers, C. M., Heroux, A., Krilov, G., & Whitesides, G. M. (2013) · Journal of the American Chemical Society · doi:10.1021/ja4075776 · Cited by 341 · see citing works

SeqGAN: Sequence Generative Adversarial Nets with Policy GradientPreprint

Yu, L., Zhang, W., Wang, J., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1609.05473 · Cited by 292 · 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

Assessing the impact of generative AI on medicinal chemistry

Walters, W. P., & Murcko, M. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0418-2 · Cited by 262 · see citing works

DOGS: Reaction-Driven de novo Design of Bioactive Compounds

Hartenfeller, M., Zettl, H., Walter, M., Rupp, M., Reisen, F., Proschak, E., Weggen, S., Stark, H., & Schneider, G. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002380 · Cited by 251 · see citing works

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 · doi:10.1016/j.arr.2018.11.003 · Cited by 246 · see citing works

How to explore chemical space using algorithms and automation

Gromski, P. S., Henson, A., Granda, J. M., & Cronin, L. (2019) · Nature Reviews Chemistry · doi:10.1038/s41570-018-0066-y · Cited by 241 · see citing works

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 219 · see citing works

CURRENT PROGRESS IN STRUCTURE-BASED RATIONAL DRUG DESIGN MARKS A NEW MINDSET IN DRUG DISCOVERY

Lounnas, V., Ritschel, T., Kelder, J., McGuire, R., Bywater, R. P., & Foloppe, N. (2013) · Computational and Structural Biotechnology Journal · doi:10.5936/csbj.201302011 · Cited by 213 · see citing works

Artificial Intelligence for Drug Discovery, Biomarker Development, and Generation of Novel Chemistry

Zhavoronkov, A. A. (2018) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.8b00930 · Cited by 198 · see citing works

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.v3 · Cited by 195 · see citing works

Artificial Intelligence in Drug Discovery

Sellwood, M. A., Ahmed, M., Segler, M., & Brown, N. (2018) · Future Medicinal Chemistry · doi:10.4155/fmc-2018-0212 · Cited by 177 · see citing works

Automated De Novo Drug Design: Are We Nearly There Yet?

Schneider, G., & Clark, D. E. (2019) · Angewandte Chemie International Edition · doi:10.1002/anie.201814681 · Cited by 168 · see citing works

De Novo Design at the Edge of Chaos

Schneider, P., & Schneider, G. (2016) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.5b01849 · Cited by 167 · see citing works

The enumeration of chemical space

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Deep Learning for Molecular Generation

Xu, Y., Lin, K., Wang, S., Wang, L., Cai, C., Song, C., Lai, L., & Pei, J. (2019) · Future Medicinal Chemistry · doi:10.4155/fmc-2018-0358 · Cited by 136 · see citing works

The drug-maker's guide to the galaxy

Mullard, A. (2017) · Nature · doi:10.1038/549445a · Cited by 135 · see citing works

Matched Molecular Pair Analysis in Short: Algorithms, Applications and Limitations

Tyrchan, C., & Evertsson, E. (2016) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2016.12.003 · Cited by 112 · see citing works

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 · see citing works

A Real-World Perspective on Molecular Design.

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Multi‐Objective Molecular De Novo Design by Adaptive Fragment Prioritization

Reutlinger, M., Rodrigues, T., Schneider, P., & Schneider, G. (2014) · Angewandte Chemie International Edition · doi:10.1002/anie.201310864 · Cited by 90 · see citing works

Building a virtual ligand screening pipeline using free software: a survey

Glaab, E. (2015) · Briefings in Bioinformatics · doi:10.1093/bib/bbv037 · Cited by 84 · see citing works

Computational Chemical Synthesis Analysis and Pathway Design

Feng, F., Lai, L., & Pei, J. (2018) · Frontiers in Chemistry · doi:10.3389/fchem.2018.00199 · Cited by 75 · see citing works

MOARF, an Integrated Workflow for Multiobjective Optimization: Implementation, Synthesis, and Biological Evaluation

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Blind Prediction of Charged Ligand Binding Affinities in a Model Binding Site

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In silico generation of novel, drug-like chemical matter using the LSTM neural networkPreprint

Ertl, P., Lewis, R. A., Martin, É., & Polyakov, V. R. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1712.07449 · Cited by 56 · see citing works

Molecular Generation with Recurrent Neural Networks (RNNs)Preprint

Bjerrum, E. J., & Threlfall, R. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.04612 · Cited by 45 · see citing works

Selectivity Challenges in Docking Screens for GPCR Targets and Antitargets

Weiss, D. R., Karpiak, J. D., Huang, X., Sassano, M. F., Lyu, J., Roth, B. L., & Shoichet, B. K. (2018) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.8b00718 · 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

Multidimensional De Novo Design Reveals 5‐HT2B Receptor‐Selective Ligands

Rodrigues, T., Hauser, N., Reker, D., Reutlinger, M., Wunderlin, T., Hamon, J., Koch, G., & Schneider, G. (2014) · Angewandte Chemie International Edition · doi:10.1002/anie.201410201 · Cited by 41 · see citing works

Multidimensional Design of Anticancer Peptides

Lin, Y., Lim, Y. F., Russo, E., Schneider, P., Bolliger, L., Edenharter, A., Altmann, K., Halin, C., Hiss, J. A., & Schneider, G. (2015) · Angewandte Chemie International Edition · doi:10.1002/anie.201504018 · Cited by 40 · see citing works

Erratum: Generative Recurrent Networks for De Novo Drug Design.

Gupta, A., Müller, A. T., Huisman, B. J. H., Fuchs, J. A., Schneider, P., & Schneider, G. (2018) · Molecular Informatics · doi:10.1002/minf.201880141 · Cited by 40 · see citing works

De novo design – hop(p)ing against hope

Schneider, G. (2012) · Drug Discovery Today Technologies · doi:10.1016/j.ddtec.2012.06.001 · Cited by 39 · see citing works

Steering Target Selectivity and Potency by Fragment‐Based De Novo Drug Design

Rodrigues, T., Kudoh, T., Roudnicky, F., Lim, Y. F., Lin, Y., Koch, C. P., Seno, M., Detmar, M. J., & Schneider, G. (2013) · Angewandte Chemie International Edition · doi:10.1002/anie.201304847 · Cited by 23 · see citing works

Evaluation of protein–ligand affinity prediction using steered molecular dynamics simulations

Okimoto, N., Suenaga, A., & Taiji, M. (2016) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2016.1251851 · Cited by 20 · see citing works

Computational approaches streamlining drug discovery

Sadybekov, A., & Katritch, V. (2023) · Nature · doi:10.1038/s41586-023-05905-z · Cited by 1,342 · see citing works

Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

Keith, J. A., Vassilev-Galindo, V., Cheng, B., Chmiela, S., Gastegger, M., Müller, K.-R., & Tkatchenko, A. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00107 · Cited by 849 · see citing works

Artificial intelligence in drug development

Zhang, K., Yang, X., Wang, Y., Yu, Y., Huang, N., Li, G. L., Li, X., Wu, J. C., & Yang, S. (2025) · Nature Medicine · doi:10.1038/s41591-024-03434-4 · Cited by 538 · see citing works

Artificial intelligence in drug discovery: recent advances and future perspectives

Jiménez-Luna, J., Grisoni, F., Weskamp, N., & Schneider, G. (2021) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2021.1909567 · Cited by 464 · see citing works

Generative artificial intelligence in supply chain and operations management: a capability-based framework for analysis and implementation

Jackson, I., Ivanov, D. A., Dolgui, A., & Namdar, J. (2024) · International Journal of Production Research · doi:10.1080/00207543.2024.2309309 · Cited by 402 · 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

Advances in De Novo Drug Design: From Conventional to Machine Learning Methods

Mouchlis, V. D., Afantitis, A., Serra, A., Fratello, M., Papadiamantis, A. G., Aidinis, V., Lynch, I., Greco, D., & Melagraki, G. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22041676 · Cited by 367 · 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

Generative models for molecular discovery: Recent advances and challenges

Bilodeau, C., Jin, W., Jaakkola, T., Barzilay, R., & Jensen, K. F. (2022) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1608 · Cited by 334 · see citing works

Machine learning in materials science: From explainable predictions to autonomous design

Pilania, G. (2021) · Computational Materials Science · doi:10.1016/j.commatsci.2021.110360 · Cited by 290 · 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

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 · doi:10.1021/acs.jcim.2c01191 · Cited by 231 · see citing works

The Evolution of Data-Driven Modeling in Organic Chemistry

Williams, W. L., Zeng, L., Gensch, T., Sigman, M. S., Doyle, A. G., & Anslyn, E. V. (2021) · ACS Central Science · doi:10.1021/acscentsci.1c00535 · Cited by 186 · see citing works

Enhancing preclinical drug discovery with artificial intelligence

Vijayan, R., Kihlberg, J. O., Cross, J. B., & Poongavanam, V. (2021) · Drug Discovery Today · doi:10.1016/j.drudis.2021.11.023 · Cited by 185 · see citing works

Deep learning tools for advancing drug discovery and development

Nag, S., Baidya, A. T. K., Mandal, A., Mathew, A. T., Das, B., Devi, B., & Kumar, R. (2022) · 3 Biotech · doi:10.1007/s13205-022-03165-8 · Cited by 182 · see citing works

Chemical language models enable navigation in sparsely populated chemical space

Skinnider, M. A., Stacey, R. G., Wishart, D. S., & Foster, L. J. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00368-1 · Cited by 169 · see citing works

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 141 · see citing works

Current strategies to address data scarcity in artificial intelligence-based drug discovery: A comprehensive review

Gangwal, A., Ansari, A., Ahmad, I., Azad, A. K., & Sulaiman, W. M. A. W. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108734 · Cited by 137 · 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

Deep Generative Models for Materials Discovery and Machine Learning-Accelerated Innovation

Fuhr, A., & Sumpter, B. G. (2022) · Frontiers in Materials · doi:10.3389/fmats.2022.865270 · Cited by 120 · see citing works

Artificial Intelligence and Advanced Materials

López, C. (2022) · Advanced Materials · doi:10.1002/adma.202208683 · Cited by 120 · see citing works

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 · doi:10.3389/fnagi.2022.914017 · Cited by 115 · see citing works

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 108 · see citing works

GenAICHI: Generative AI and HCI

Muller, M., Chilton, L. B., Kantosalo, A., Martin, C. P., & Walsh, G. (2022) · CHI Conference on Human Factors in Computing Systems Extended Abstracts · doi:10.1145/3491101.3503719 · Cited by 101 · see citing works

Artificial intelligence as a smart approach to develop antimicrobial drug molecules: A paradigm to combat drug-resistant infections

Talat, A., & Khan, A. U. (2023) · Drug Discovery Today · doi:10.1016/j.drudis.2023.103491 · Cited by 96 · see citing works

Introduction to small molecule drug discovery and preclinical development

Southey, M. W. Y., & Brunavs, M. (2023) · Frontiers in Drug Discovery · doi:10.3389/fddsv.2023.1314077 · Cited by 94 · see citing works

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 101 · see citing works

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 82 · see citing works

De Novo Peptide and Protein Design Using Generative Adversarial Networks: An Update

Lin, E., Lin, C., & Lane, H. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c01361 · Cited by 74 · see citing works

A review of quantitative structure-activity relationship: The development and current status of data sets, molecular descriptors and mathematical models

Li, J., Zhao, T. J., Yang, Q., Du, S., & Xu, L. (2024) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2024.105278 · Cited by 74 · see citing works

Invalid SMILES are beneficial rather than detrimental to chemical language models

Skinnider, M. A. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00821-x · Cited by 67 · 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

Design of potent antimalarials with generative chemistry

Godinez, W. J., Ma, E. J., Chao, A. T., Pei, L., Skewes-Cox, P., Canham, S. M., Jenkins, J. L., Young, J. M., Martin, É., & Guiguemde, W. A. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00448-w · Cited by 57 · see citing works

Deep learning and generative methods in cheminformatics and chemical biology: navigating small molecule space intelligently

Kell, D. B., Samanta, S., & Swainston, N. (2020) · Biochemical Journal · doi:10.1042/bcj20200781 · Cited by 56 · see citing works

Generative adversarial networks for transition state geometry prediction

Makoś, M. Z., Verma, N., Larson, E. C., Freindorf, M., & Kraka, E. (2021) · The Journal of Chemical Physics · doi:10.1063/5.0055094 · Cited by 54 · see citing works

Cross-disciplinary perspectives on the potential for artificial intelligence across chemistry

Mroz, A. M., Basford, A. R., Hastedt, F., Jayasekera, I. S., Mosquera‐Lois, I., Sedgwick, R., Ballester, P. J., Bocarsly, J. D., del Rio‐Chanona, E. A., Evans, M. L., et al. (2025) · Chemical Society Reviews · doi:10.1039/d5cs00146c · Cited by 59 · see citing works

Research Acceleration in Self‐Driving Labs: Technological Roadmap toward Accelerated Materials and Molecular Discovery

Delgado‐Licona, F., & Abolhasani, M. (2022) · Advanced Intelligent Systems · doi:10.1002/aisy.202200331 · Cited by 55 · see citing works

The Hitchhiker’s Guide to Deep Learning Driven Generative Chemistry

Ivanenkov, Y. A., Zagribelnyy, B. A., Малышев, А. В., Evteev, S. A., Terentiev, V. A., Kamya, P. R. N., Bezrukov, D. S., Aliper, A., Ren, F., & Zhavoronkov, A. A. (2023) · ACS Medicinal Chemistry Letters · doi:10.1021/acsmedchemlett.3c00041 · Cited by 51 · see citing works

Reinforcement Learning for Generative AI: State of the Art, Opportunities and Open Research Challenges

Franceschelli, G., & Musolesi, M. (2024) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.15278 · Cited by 52 · see citing works

Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence

Duran-Frigola, M., Cigler, M., & Winter, G. E. (2023) · Journal of the American Chemical Society · doi:10.1021/jacs.2c11098 · Cited by 50 · see citing works

Genetic Optimization of Homogeneous Catalysts

Laplaza, R., Gallarati, S., & Corminbœuf, C. (2022) · Chemistry - Methods · doi:10.1002/cmtd.202100107 · Cited by 47 · see citing works

Accelerated Discovery of Macrocyclic CDK2 Inhibitor QR-6401 by Generative Models and Structure-Based Drug Design

Yu, Y., Huang, J., He, H., Han, J., Ye, G., Xu, T., Sun, X., Chen, X., Ren, X., Li, C., et al. (2023) · ACS Medicinal Chemistry Letters · doi:10.1021/acsmedchemlett.2c00515 · Cited by 44 · see citing works

Defining Levels of Automated Chemical Design

Goldman, B. B., Kearnes, S. M., Kramer, T. J., Riley, P., & Walters, W. P. (2022) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.2c00334 · Cited by 44 · 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

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

Surfactant-Specific AI-Driven Molecular Design: Integrating Generative Models, Predictive Modeling, and Reinforcement Learning for Tailored Surfactant Synthesis

Nnadili, M., Okafor, A. N., Olayiwola, T., Akinpelu, D. A., Kumar, R., & Romagnoli, J. A. (2024) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.4c00401 · Cited by 34 · see citing works

Current and future deep learning algorithms for tandem mass spectrometry (MS/MS)‐based small molecule structure elucidation

Liu, Y., Vijlder, T. D., Bittremieux, W., Laukens, K., & Heyndrickx, W. (2021) · Rapid Communications in Mass Spectrometry · doi:10.1002/rcm.9120 · Cited by 34 · see citing works

Applications of Artificial Intelligence in Biotech Drug Discovery and Product Development

Liu, Y., Zhang, L., Jiang, Z., Tian, X., Li, P., Wu, P., Du, W., Yuan, B., Xie, C., Bu, G., et al. (2025) · MedComm · doi:10.1002/mco2.70317 · Cited by 36 · see citing works

Trends in Deep Learning for Property-driven Drug Design

Born, J., & Manica, M. (2021) · Current Medicinal Chemistry · doi:10.2174/0929867328666210729115728 · Cited by 35 · see citing works

Artificial Intelligent Deep Learning Molecular Generative Modeling of Scaffold-Focused and Cannabinoid CB2 Target-Specific Small-Molecule Sublibraries

Bian, Y., & Xie, X. (2022) · Cells · doi:10.3390/cells11050915 · Cited by 32 · see citing works

Language models for the prediction of SARS-CoV-2 inhibitors

Blanchard, A. E., Gounley, J., Bhowmik, D., Chandra Shekar, M., Lyngaas, I., Gao, S., Yin, J., Tsaris, A., Wang, F., & Glaser, J. (2022) · The International Journal of High Performance Computing Applications · doi:10.1177/10943420221121804 · Cited by 30 · see citing works

Generative Artificial Intelligence and Extended Cognition in Science Learning Contexts

Rivera-Novoa, Á., & Arias, D. A. D. (2025) · Science Education · doi:10.1007/s11191-025-00660-1 · Cited by 35 · see citing works

Deep Learning Applied to Ligand-Based De Novo Drug Design

Palazzesi, F., & Pozzan, A. (2021) · Methods in molecular biology · doi:10.1007/978-1-0716-1787-8_12 · Cited by 30 · see citing works

GenAICHI 2023: Generative AI and HCI at CHI 2023

Muller, M. J., Chilton, L. B., Kantosalo, A., Liao, Q. V., Maher, M. L., Martin, C. P., & Walsh, G. (2023) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3544549.3573794 · Cited by 26 · see citing works

GenAICHI 2024: Generative AI and HCI at CHI 2024

Muller, M. J., Kantosalo, A., Maher, M. L., Martin, C. P., & Walsh, G. (2024) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3613905.3636294 · Cited by 27 · see citing works

Empowering precision medicine: regenerative AI in breast cancer

Bhattacharya, S., Saleem, S. M., Singh, A., Singh, S., & Tripathi, S. (2024) · Frontiers in Oncology · doi:10.3389/fonc.2024.1465720 · Cited by 26 · see citing works

Active Site Sequence Representations of Human Kinases Outperform Full Sequence Representations for Affinity Prediction and Inhibitor Generation: 3D Effects in a 1D Model

Born, J., Huynh, T., Stroobants, A., Cornell, W. D., & Manica, M. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c00889 · Cited by 23 · see citing works

Can We Quickly Learn to “Translate” Bioactive Molecules with Transformer Models?

Tysinger, E. P., Rai, B. K., & Sinitskiy, A. V. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c01618 · Cited by 23 · see citing works

A New Era of Artificial Intelligence (AI): Transforming Drug Discovery and Development

Ali, S., Tian, X., Chen, H., & Zhou, J. (2025) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.5c03159 · Cited by 23 · see citing works

Genetic Algorithms and Machine Learning for Predicting Surface Composition, Structure, and Chemistry: A Historical Perspective and Assessment

Roberts, J., Bursten, J. R. S., & Risko, C. M. (2021) · Chemistry of Materials · doi:10.1021/acs.chemmater.1c00538 · Cited by 23 · see citing works

Combating small-molecule aggregation with machine learning

Lee, K., Yang, A., Lin, Y., Reker, D., Bernardes, G. J. L., & Rodrigues, T. (2021) · Cell Reports Physical Science · doi:10.1016/j.xcrp.2021.100573 · Cited by 22 · see citing works

Amortized Tree Generation for Bottom-up Synthesis Planning and Synthesizable Molecular DesignPreprint

Gao, W., Mercado, R., & Coley, C. W. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.06389 · Cited by 37 · see citing works

Mental Models of Generative AI Chatbot Ecosystems

Wang, X.-Y., Wang, X.-Z., Park, S., & Yao, Y.-X. (2025) · International Conference on Intelligent User Interfaces · doi:10.1145/3708359.3712125 · Cited by 24 · see citing works

Electron density-based GPT for optimization and suggestion of host–guest binders

Gutiérrez, J. M. P., Granda, J. M., Ayme, J., Bajczyk, M. D., Wilbraham, L., & Cronin, L. (2024) · Nature Computational Science · doi:10.1038/s43588-024-00602-x · Cited by 19 · see citing works

AI-Driven De Novo Design and Development of Nontoxic DYRK1A Inhibitors

González, E., Varas, P., González-Naranjo, P., Ulzurrun, E., Marcos-Ayuso, G., Pérez, C., Paez, J. A., Insua, D. R., Santana, S. R., & Campillo, N. E. (2025) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.5c00512 · Cited by 18 · see citing works

Multimodal Transformers and Their Applications in Drug Target Discovery for Aging and Age-Related Diseases

Steurer, B., Vanhaelen, Q., & Zhavoronkov, A. A. (2024) · The Journals of Gerontology Series A · doi:10.1093/gerona/glae006 · Cited by 16 · see citing works

Design, Synthesis, and Biological Evaluation of Novel Orally Available Covalent CDK12/13 Dual Inhibitors for the Treatment of Tumors

Lu, H., Sun, D., Wang, Z., Cui, H., Min, L., Zhang, H., Zhang, Y., Wu, J., Cai, X., Ding, X., et al. (2025) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.4c01616 · Cited by 17 · see citing works

GenAICHI 2025: Generative AI and HCI at CHI 2025

Muller, M. J., Chilton, L. B., Maher, M. L., Martin, C. P., Choi, M., Walsh, G., & Kantosalo, A. (2025) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3706599.3707213 · Cited by 18 · see citing works

Transmol: repurposing a language model for molecular generation

Zhumagambetov, R., Molnár, F., Peshkov, V. A., & Fazli, S. (2021) · RSC Advances · doi:10.1039/d1ra03086h · Cited by 15 · see citing works

Machine learning & deep learning tools in pharmaceutical sciences: A comprehensive review

Javid, S., Rahmanulla, A., Ahmed, M. G., Sultana, R., & Kumar, B. R. P. (2025) · Intelligent Pharmacy · doi:10.1016/j.ipha.2024.11.003 · Cited by 17 · see citing works

AI ‐Driven Discovery of High Performance Polymer Electrodes for Next‐Generation Batteries

Ganti, S. V. S., Wölfel, L., & Kuenneth, C. (2025) · Journal of Polymer Science · doi:10.1002/pol.20250198 · Cited by 16 · see citing works

Derivatization Design of Synthetically Accessible Space for Optimization: In Silico Synthesis vs Deep Generative Design

Makara, G. M., Kovács, L., Szabó, I., & Pőcze, G. (2021) · ACS Medicinal Chemistry Letters · doi:10.1021/acsmedchemlett.0c00540 · Cited by 14 · see citing works

Deep Learning Based Methods for Molecular Similarity Searching: A Systematic Review

Nasser, M., Yusof, U. K., & Salim, N. B. (2023) · Processes · doi:10.3390/pr11051340 · Cited by 14 · see citing works

Improving the Reliability of Molecular String Representations for Generative Chemistry

Reboul, E., Wefers, Z., Prabakaran, H., Waldispühl, J., & Taly, A. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c02261 · Cited by 13 · see citing works

AI's role in pharmaceuticals: Assisting drug design from protein interactions to drug development

Bechelli, S., & Delhommelle, J. (2023) · Artificial Intelligence Chemistry · doi:10.1016/j.aichem.2023.100038 · Cited by 14 · see citing works

Utilizing AI for the Identification and Validation of Novel Therapeutic Targets and Repurposed Drugs for Endometriosis

Liu, B. H. M., Lin, Y., Long, X., Hung, S. W., Gaponova, A. V., Ren, F., Zhavoronkov, A. A., Pun, F. W., & Wang, C. C. (2024) · Advanced Science · doi:10.1002/advs.202406565 · Cited by 13 · see citing works

Discovery of Potent, Highly Selective, and Orally Bioavailable MTA Cooperative PRMT5 Inhibitors with Robust In Vivo Antitumor Activity

Zhang, M., Ding, X., Cao, Z., Yang, Y., Ding, X., Cai, X., Zhang, M., Aliper, A. M., Ren, F., Lu, H., et al. (2025) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.4c02732 · Cited by 12 · see citing works

Artificial intelligence in anti-obesity drug discovery: unlocking next-generation therapeutics

Gangwal, A., & Lavecchia, A. (2025) · Drug Discovery Today · doi:10.1016/j.drudis.2025.104333 · Cited by 13 · see citing works

Discovery of macrocyclic CDK2/4/6 inhibitors with improved potency and DMPK properties through a highly efficient macrocyclic drug design platform

Meng, F., Liu, J., Cao, Z., Yu, J., Steurer, B., Yang, Y., Wang, Y., Cai, X., Zhang, M., Ren, F., et al. (2024) · Bioorganic Chemistry · doi:10.1016/j.bioorg.2024.107285 · Cited by 10 · see citing works

Discovery of a Novel Macrocyclic Noncovalent CDK7 Inhibitor for Cancer Therapy

Lu, H., Zhang, Y., Liu, J., Jiang, T., Yu, X., Zhang, H., Liang, T., Peng, J., Cai, X., Lan, X., et al. (2024) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.4c02098 · Cited by 10 · see citing works

The Structure‐Mechanics Relationship of Bamboo‐Epidermis and Inspired Composite Design by Artificial Intelligence

Zhao Qin, A., & Destree, A. P. (2024) · Advanced Materials · doi:10.1002/adma.202414970 · Cited by 10 · see citing works

Chemistry42: An AI-based platform for de novo molecular designPreprint

Ivanenkov, Y. A., Zhebrak, A., Bezrukov, D. S., Zagribelnyy, B. A., Aladinskiy, V., Polykovskiy, D. A., Lane, E., Kamya, P. R. N., Aliper, A. M., & Zhavoronkov, A. A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.09050 · Cited by 12 · see citing works

Generating Bioactive Natural Product-inspired Molecules with Machine Intelligence

Schneider, P., Altmann, K., & Schneider, G. (2022) · CHIMIA International Journal for Chemistry · doi:10.2533/chimia.2022.396 · Cited by 8 · see citing works

Prediction of the Infrared Absorbance Intensities and Frequencies of Hydrocarbons: A Message Passing Neural Network Approach

Tameh, M. S., Coropceanu, V., Purcell, T. A. R., & Brédas, J. (2024) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.4c06745 · Cited by 8 · see citing works

Building a pathway of GenAI-enabled capabilities for supply chain management

Callefi, M. H. B. M., Magalhães, E., Negrão, L. L. L., Thürer, M., Siegler, J., Tomašević, I., & Filho, M. G. (2026) · International Journal of Production Research · doi:10.1080/00207543.2026.2636664 · Cited by 8 · see citing works

Emerging Data Practices: Data Work in the Era of Large Language Models

Garcia, A. A., Candello, H. C. D. S. P., Badillo-Urquiola, K. A., & Wong-Villacrés, M. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3714069 · Cited by 8 · see citing works

FBDD & De Novo Drug Design

Das, A., Nandi, A., Kumari, V., & Alvala, M. (2023) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9789815179934123010008 · Cited by 7 · see citing works

Research on the Identification and Evaluation of Aerobics Movements Based on Deep Learning

Zhao, H., Wang, A., & Fan, Y. (2021) · Scientific Programming · doi:10.1155/2021/6433260 · Cited by 6 · see citing works

An AI-Driven Framework for Discovery of BACE1 Inhibitors for Alzheimer’s DiseasePreprint

Xie, E., Hasegawa, K., Kementzidis, G., Papadopoulos, E., Aktas, B. H., & Deng, Y. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.05.15.594361 · Cited by 6 · see citing works

Generation of Rational Drug-like Molecular Structures Through a Multiple-Objective Reinforcement Learning Framework

Zhang, X., Gao, H., Qi, Y., Li, Y., & Wang, R. (2024) · Molecules · doi:10.3390/molecules30010018 · Cited by 5 · see citing works

Goal-directed molecule generation with fine-tuning by policy gradient

Sha, C., & Zhu, F. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.123127 · Cited by 5 · see citing works

Strategic partnerships for AI-driven drug discovery: The role of relational dynamics

Kint, S., Dolfsma, W. A., & Robinson, D. (2024) · Drug Discovery Today · doi:10.1016/j.drudis.2024.104242 · Cited by 8 · see citing works

Comparing Molecules Generated by MMPDB and REINVENT4 with Ideas from Drug Discovery Design Teams

Sun, S., & Huggins, D. J. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c00250 · Cited by 4 · see citing works

PROTACs in targeted protein degradation: Advances in development and AI-enhanced drug discovery

Du, M., Liu, T., Wang, W., Fan, S., Tian, M., Du, Q., Wang, Z., Li, X., Liu, X., Xia, Q., et al. (2026) · European Journal of Medicinal Chemistry Reports · doi:10.1016/j.ejmcr.2026.100332 · Cited by 4 · see citing works

Configurable soft computing-based generative model: The search for catalytic peptides

Mauša, G., Njirjak, M., Otović, E., & Kalafatović, D. (2023) · MRS Advances · doi:10.1557/s43580-023-00629-8 · Cited by 4 · see citing works

Deep Generative Models Enable Navigation in Sparsely Populated Chemical SpacePreprint

Skinnider, M. A., Stacey, R. G., Wishart, D. S., & Foster, L. J. (2021) · ChemRxiv · doi:10.26434/chemrxiv.13638347 · Cited by 4 · see citing works

PDE4D inhibitors: Opening a new era of PET diagnostics for Alzheimer's disease

SHI, L. ‐., Wang, X., Si, H., & Song, W. (2024) · Neurochemistry International · doi:10.1016/j.neuint.2024.105903 · Cited by 3 · see citing works

Optimization of binding affinities in chemical space with generative pre-trained transformer and deep reinforcement learning

Xu, X., Zhou, J., Zhu, C., Zhan, Q., Li, Z., Zhang, R., Wang, Y., Liao, X., & Gao, X. (2023) · F1000Research · doi:10.12688/f1000research.130936.1 · Cited by 3 · see citing works

Artificial Intelligence and Machine Learning in Drug Discovery

Shah, P., Thakkar, D. V., Panchal, N., & Jha, R. (2024) · Advances in healthcare information systems and administration book series · doi:10.4018/979-8-3693-2897-2.ch003 · Cited by 3 · see citing works

CDGCN: Conditional de novo Drug Generative Model Using Graph Convolution Networks

Mallick, S., & Bhadra, S. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-29119-7_7 · Cited by 3 · see citing works

GANs for Molecule Generation in Drug Design and Discovery

Zhang, Z., Li, F., Guan, J., Kong, Z., Shi, L., & Zhou, S. (2022) · Intelligent systems reference library · doi:10.1007/978-3-030-91390-8_11 · Cited by 8 · see citing works

Artificial Intelligence Generative Chemistry Design of Target-Specific Scaffold-Focused Small Molecule Drug Libraries

Bian, Y., Hou, G., & Xie, X. (2023) · Springer eBooks · doi:10.1007/978-981-99-1284-1_31

Generative Adversarial Networks for De Novo Molecular Design

Lee, Y. J., Kahng, H., & Kim, S. B. (2021) · Molecular Informatics · doi:10.1002/minf.202100045 · Cited by 38 · see citing works

Generative chemistry: drug discovery with deep learning generative models

Bian, Y., & Xie, X.-Q. (2021) · Journal of Molecular Modeling · doi:10.1007/s00894-021-04674-8 · Cited by 193 · see citing works

InstGAN: Instant Actor-Critic-Driven GAN for De Novo Molecule Generation and Property Optimization

Tang, H., Li, C., Kamei, S., Yamanishi, Y., & Morimoto, Y. (2025) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2025/694 · Cited by 1 · see citing works