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The Advent of Generative Chemistry
Open accessAbstract
Generative adversarial networks (GANs), first published in 2014, are among the most important concepts in modern artificial intelligence (AI). Bridging deep learning and game theory, GANs are used to generate or “imagine” new objects with desired properties. Since 2016, multiple GANs with reinforcement learning (RL) have been successfully applied in pharmacology for de novo molecular design. Those techniques aim at a more efficient use of the data and a better exploration of the chemical space. We review recent advances for the generation of novel molecules with desired properties with a focus on the applications of GANs, RL, and related techniques. We also discuss the current limitations and challenges in the new growing field of generative chemistry.
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“The development of ultra-large chemical libraries (94) and generative chemistry methods, particularly those based on Generative Adversarial Networks (GANs), have significantly expanded the possibilities for hit generation and drug discovery.”
From Artificial Intelligence for Drug Discovery: Are We There Yet? (2024) · cites this paper as [94]
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Metadata
- Authors
- Vanhaelen, Q., Lin, Y.-C., & Zhavoronkov, A.
- Affiliations
- Insilico Medicine Hong Kong Ltd , Pak Shek Kok , ,
- Corresponding author
- Vanhaelen, Quentin
- Venue
- ACS Medicinal Chemistry Letters
- Publisher
- American Chemical Society (ACS)
- Published
- 2020-07-14
- Volume
- 11
- Issue
- 8
- Pages
- 1496-1505
- ISSN
- 1948-5875 (ISSN-L)
- PMC
- PMC7429972
- OpenAlex
- W3043461363
- Cited by
- 151
- References
- 76
- Citations by year
- 2026: 18 · 2025: 27 · 2024: 32 · 2023: 26 · 2022: 16 · 2021: 30 · 2020: 2
- FWCI
- 9.19
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- ACS Medicinal Chemistry Letters (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Keywords
- generative chemistry, generative adversarial networks, reinforcement learning, de novo molecular design, drug discovery, chemical space exploration, molecular property prediction, generative models
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