基于深度学习的3D分子生成模型研究进展

Advances in deep learning-based 3D molecular generative models

分子生成作为药物设计领域的一个基本问题,旨在以低成本和高效率的方式设计出具有理想生物活性和药代动力学属性的新颖分子.近年来深度生成模型在药物设计中得到了广泛应用,大量的模型结构和优化策略得到探索,其中大多是生成一维或二维的分子结构.随着深度学习在处理几何图形数据上的快速发展,面向3D分子的生成模型被提出,因其在直接生成3D分子构象和基于结构的药物设计上的优势和潜力而越来越受到关注.本文对近年来国内外学者在3D分子生成上取得的成果进行了系统的总结和分析,从3D分子生成算法输入的角度将其分为基于隐变量的生成、基于2D分子图的生成和基于3D分子构象的生成;接着从3D分子生成算法输出的角度将其分类为定向生成和非定向生成;随后总结了相关生成模型在主要的公开数据集上的性能,以探究各种生成模型的优缺点;最后对未来可能的研究方向进行了展望.

Molecular generation,aiming at designing novel molecules with desired properties such as biological activity and drug metabolism and pharmacokinetic(DMPK)properties in a low-cost and high-efficient manner,is a fundamental problem in drug discovery.Recently,deep generative models have been widely used in drug discovery,along with numerous model architectures and optimization strategies being explored,most of which are developed to generate onedimensional or two-dimensional molecular structures.Inspired by the rapid development of deep learning in processing geometric graph data,3D generative models for molecular generation have also been proposed,gaining attention for their advantages and potential on direct 3D molecular conformation generation and structure-based drug discovery.This survey has offered a systematic summarization of existing research achievements of the domestic and foreign researchers in recent years in the aspects of 3D molecular generation.According to the input of 3D molecular generation algorithms,it is divided into latent variable-based generation,2D graph-based generation and 3D geometry-based generation.Then,according to the output of 3D molecular generation algorithms,it is divided into goal-directed generation and non-goal-directed generation.Furthermore,the performance of different generative models on main public datasets is summarized,which proves the advantages and disadvantages of the various models.Finally,some promising research directions are proposed.