Physics-informed generative model for drug-like molecule conformers

arXiv:2403.07925v1 Announce Type: cross
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 …

abstract arxiv atom cs.lg deep learning diffusion fields found generative physics physics.chem-ph physics-informed q-bio.bm representation terms type typing