代表(政治)
构象异构
生成语法
集合(抽象数据类型)
生成模型
训练集
采样(信号处理)
分子
统计物理学
计算机科学
物理
人工智能
量子力学
计算机视觉
滤波器(信号处理)
政治
政治学
法学
程序设计语言
作者
D. C. Williams,Neil Inala
标识
DOI:10.1021/acs.jcim.3c01816
摘要
We present a diffusion-based generative model for conformer generation. Our model is focused on the reproduction of the bonded structure and is constructed from the associated terms traditionally found in classical force fields to ensure a physically relevant representation. Techniques in deep learning are used to infer atom typing and geometric parameters from a training set. Conformer sampling is achieved by taking advantage of recent advancements in diffusion-based generation. By training on large, synthetic data sets of diverse, drug-like molecules optimized with the semiempirical GFN2-xTB method, high accuracy is achieved for bonded parameters, exceeding that of conventional, knowledge-based methods. Results are also compared to experimental structures from the Protein Databank and the Cambridge Structural Database.
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