Torsional Diffusion for Molecular Conformer Generation

• URL: http://arxiv.org/abs/2206.01729v1
• Date: Wed, 1 Jun 2022 04:30:41 GMT
• Title: Torsional Diffusion for Molecular Conformer Generation
• Authors: Bowen Jing, Gabriele Corso, Jeffrey Chang, Regina Barzilay, Tommi Jaakkola
• Abstract summary: torsional diffusion is a novel diffusion framework that operates on the space of torsion angles. On a standard benchmark of drug-like molecules, torsional diffusion generates superior conformer ensembles. Our model provides exact likelihoods, which we employ to build the first generalizable Boltzmann generator.
• Score: 28.225704750892795
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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