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.

本文提出了一种新的分子构象生成算法，通过基于扭转角度的扩散方法在高维空间上操作，并使用内外积分评分模型，其性能在药物样品基准中明显优于机器学习和化学信息学方法，同时比基于扩散模型的先前模型快几个数量级，此外，该模型提供了精确的似然度，用于构建第一个有效的玻尔兹曼生成器。

分子构象生成的扭转扩散