基于深度学习的全新药物设计研究进展

Deep learning approaches for de novo drug design:an overview

先导化合物的设计和发现是新药研发中最具挑战性和创造性的阶段,其过程需考虑候选分子的结构新颖性、生物活性、靶标选择性、可合成性、成药性和安全性等多种属性的优化.虽然计算机辅助药物设计方法的发展和应用大大节省了先导化合物发现阶段的时间和经济成本,但仍未能扭转新药研发成功率低的现状.近年来,随着深度学习技术的不断发展,基于深度学习的全新药物设计方法为先导化合物的发现带来新的契机,前景巨大.这些全新药物设计模型使用的深度学习框架包括编码-解码器、循环神经网络、生成对抗网络、强化学习等.本文综述了这些深度学习框架的基本原理、模型输入分子表征以及效果评测指标,并对其在全新药物设计领域的应用前景进行了展望.

The design and discovery of lead compounds is the most challenging and creative stage in drug development,and multiple attributes of candidate molecules need to be optimized in this process,such as structural novelty,biological activity,target selectivity,synthesability,druggability,and safety.Although the development and application of computer-aided drug design methods have substantially reduced the time and cost in the lead compound discovery stage,it has not yet been able to reverse the current situation of low success rate of drug development.In recent years,with the continuous development of deep learning(DL)technology,DL-based de novo drug design methods have brought new opportunities for the discovery of lead compounds.The DL frameworks used by these new drug design models include encoder-decoder,recurrent neural network,generative adversarial networks,and reinforcement learning.In this paper,the basic theory,the input molecular representation and the evaluation metrics of their methods are reviewed,and the application prospect of DL-based de novo drug design methods is also discussed.