论文标题
分子构象异构体的扭转扩散
Torsional Diffusion for Molecular Conformer Generation
论文作者
论文摘要
分子构象生成是计算化学中的基本任务。已经开发了几种机器学习方法，但是没有一个胜过最先进的化学信息法。我们提出了扭转扩散，这是一种新型扩散框架，该框架通过高血压上的扩散过程和外部到内在的得分模型在扭转角度上运行。在类似药物的分子的标准基准上，与机器学习和化学形象方法相比，扭转扩散在RMSD和化学特性方面产生了优越的构象合物，并且比以前的基于扩散的模型更快。此外，我们的模型提供了确切的可能性，我们采用了该模型来构建第一个可推广的Boltzmann发电机。代码可从https://github.com/gcorso/torsional-diffusion获得。
Molecular conformer generation is a fundamental task in computational chemistry. Several machine learning approaches have been developed, but none have outperformed state-of-the-art cheminformatics methods. We propose torsional diffusion, a novel diffusion framework that operates on the space of torsion angles via a diffusion process on the hypertorus and an extrinsic-to-intrinsic score model. On a standard benchmark of drug-like molecules, torsional diffusion generates superior conformer ensembles compared to machine learning and cheminformatics methods in terms of both RMSD and chemical properties, and is orders of magnitude faster than previous diffusion-based models. Moreover, our model provides exact likelihoods, which we employ to build the first generalizable Boltzmann generator. Code is available at https://github.com/gcorso/torsional-diffusion.
