  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.
