Open AccessThe Advent of Generative Chemistry
Author(s) -
Quentin Vanhaelen,
YenChu Lin,
Alex Zhavoronkov
Publication year - 2020
Publication title -
acs medicinal chemistry letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.065
H-Index - 66
ISSN - 1948-5875
DOI - 10.1021/acsmedchemlett.0c00088
Subject(s) - generative grammar , computer science , chemical space , adversarial system , bridging (networking) , artificial intelligence , reinforcement learning , data science , cognitive science , chemistry , drug discovery , psychology , computer network , biochemistry
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.