Torsional Diffusion for Molecular Conformer Generation
This work addresses the problem of generating accurate molecular conformers for computational chemists, offering a novel method that improves performance and speed over prior approaches.
The authors tackled molecular conformer generation by proposing torsional diffusion, a diffusion framework operating on torsion angles, which outperformed existing machine learning and cheminformatics methods on a drug-like molecule benchmark in RMSD and chemical properties while being much faster.
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.