Torsional Diffusion for Molecular Conformer Generation

Abstract

Diffusion-based generative models generate samples by mapping noise to data via the reversal of a diffusion process that typically consists of independent Gaussian noise in every data coordinate. This diffusion process is, however, not well suited to the fundamental task of molecular conformer generation where the degrees of freedom differentiating conformers lie mostly in torsion angles. We, therefore, propose Torsional Diffusion that generates conformers by leveraging the definition of a diffusion process over the space $\mathbb{T}^m$, a high dimensional torus representing torsion angles, and a $SE(3)$ equivariant model capable of accurately predicting the score over this process. Empirically, we demonstrate that our model outperforms state-of-the-art methods in terms of both diversity and precision of generated conformers, reducing the mean minimum RMSD by respectively 31% and 17%. When compared to Gaussian diffusion models, torsional diffusion enables significantly more accurate generation while performing two orders of magnitude fewer inference time-steps.