Physics-informed generative model for drug-like molecule conformers

Abstract

We present a diffusion-based, generative model for conformer generation. Our model is focused on the reproduction of bonded structure and is constructed from the associated terms traditionally found in classical force fields to ensure a physically relevant representation. Techniques in deep learning are used to infer atom typing and geometric parameters from a training set. Conformer sampling is achieved by taking advantage of recent advancements in diffusion-based generation. By training on large, synthetic data sets of diverse, drug-like molecules optimized with the semiempirical GFN2-xTB method, high accuracy is achieved for bonded parameters, exceeding that of conventional, knowledge-based methods. Results are also compared to experimental structures from the Protein Databank (PDB) and Cambridge Structural Database (CSD).

To appear in the Journal of Chemical Information and Modeling

References in corpus (7)

• Score-Based Generative Modeling through Stochastic Differential Equations
• Segment Anything
• GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation
• Understanding Diffusion Models: A Unified Perspective
• Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise
• Learning Neural Generative Dynamics for Molecular Conformation Generation
• Consistency Models