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.

中文翻译：

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生成化学的出现。


生成对抗网络 (GAN) 于 2014 年首次发布，是现代人工智能 (AI) 中最重要的概念之一。 GAN 连接深度学习和博弈论，用于生成或“想象”具有所需属性的新对象。自 2016 年以来，多个具有强化学习（RL）的 GAN 已成功应用于药理学中的从头分子设计。这些技术旨在更有效地利用数据并更好地探索化学空间。我们回顾了生成具有所需特性的新型分子的最新进展，重点关注 GAN、RL 和相关技术的应用。我们还讨论了新兴的生成化学领域当前的局限性和挑战。