Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA, 15261, USA.
NIH National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, Pittsburgh, PA, 15261, USA.
J Mol Model. 2021 Feb 4;27(3):71. doi: 10.1007/s00894-021-04674-8.
The de novo design of molecular structures using deep learning generative models introduces an encouraging solution to drug discovery in the face of the continuously increased cost of new drug development. From the generation of original texts, images, and videos, to the scratching of novel molecular structures the creativity of deep learning generative models exhibits the height machine intelligence can achieve. The purpose of this paper is to review the latest advances in generative chemistry which relies on generative modeling to expedite the drug discovery process. This review starts with a brief history of artificial intelligence in drug discovery to outline this emerging paradigm. Commonly used chemical databases, molecular representations, and tools in cheminformatics and machine learning are covered as the infrastructure for generative chemistry. The detailed discussions on utilizing cutting-edge generative architectures, including recurrent neural network, variational autoencoder, adversarial autoencoder, and generative adversarial network for compound generation are focused. Challenges and future perspectives follow.
使用深度学习生成模型对分子结构进行从头设计，为药物发现带来了令人鼓舞的解决方案，有助于应对新药研发成本的不断增加。从原始文本、图像和视频的生成，到新颖分子结构的生成，深度学习生成模型的创造力展示了机器智能所能达到的高度。本文旨在综述基于生成模型来加速药物发现过程的生成化学的最新进展。本文从人工智能在药物发现领域的简要历史开始，概述了这一新兴范例。涵盖了化学信息学和机器学习中常用的化学数据库、分子表示和工具，作为生成化学的基础。详细讨论了如何利用先进的生成架构，包括递归神经网络、变分自动编码器、对抗自动编码器和生成对抗网络进行化合物生成。随后探讨了挑战和未来展望。
