Vanhaelen Quentin, Lin Yen-Chu, Zhavoronkov Alex
Insilico Medicine Hong Kong Ltd, Pak Shek Kok, New Territories, Hong Kong.
Insilico Taiwan, Taipei City 115, Taiwan, R.O.C.
ACS Med Chem Lett. 2020 Jul 14;11(8):1496-1505. doi: 10.1021/acsmedchemlett.0c00088. eCollection 2020 Aug 13.
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 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.
生成对抗网络（GANs）于2014年首次发表，是现代人工智能（AI）中最重要的概念之一。GANs将深度学习与博弈论相结合，用于生成或“想象”具有所需属性的新对象。自2016年以来，多种带有强化学习（RL）的GANs已成功应用于药理学中的分子设计。这些技术旨在更有效地利用数据并更好地探索化学空间。我们回顾了利用GANs、RL及相关技术生成具有所需属性的新型分子的最新进展。我们还讨论了生成化学这一新兴领域当前的局限性和挑战。
