化学信息学
化学空间
计算机科学
生成语法
理论计算机科学
图形
人工智能
点(几何)
生成模型
最大值和最小值
生物系统
数学
药物发现
计算化学
化学
几何学
生物
数学分析
生物化学
作者
Niklas W. A. Gebauer,Michael Gastegger,Kristof T. Schütt
出处
期刊:Cornell University - arXiv
日期:2019-01-01
被引量:45
标识
DOI:10.48550/arxiv.1906.00957
摘要
Deep learning has proven to yield fast and accurate predictions of quantum-chemical properties to accelerate the discovery of novel molecules and materials. As an exhaustive exploration of the vast chemical space is still infeasible, we require generative models that guide our search towards systems with desired properties. While graph-based models have previously been proposed, they are restricted by a lack of spatial information such that they are unable to recognize spatial isomerism and non-bonded interactions. Here, we introduce a generative neural network for 3d point sets that respects the rotational invariance of the targeted structures. We apply it to the generation of molecules and demonstrate its ability to approximate the distribution of equilibrium structures using spatial metrics as well as established measures from chemoinformatics. As our model is able to capture the complex relationship between 3d geometry and electronic properties, we bias the distribution of the generator towards molecules with a small HOMO-LUMO gap - an important property for the design of organic solar cells.
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