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The Advent of Generative Chemistry

Cite this: ACS Med. Chem. Lett. 2020, 11, 8, 1496–1505
Publication Date (Web):July 14, 2020
https://doi.org/10.1021/acsmedchemlett.0c00088
Copyright © 2020 American Chemical Society
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    Abstract

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    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 Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsmedchemlett.0c00088.

    • Key features of AI-based models designed for de novo molecular generation which have been published in peer-reviewed journals (PDF)

    • Glossary containing the definition of key technical terms commonly used within the field of AI/ML (PDF)

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