  In this paper we tackle the problem of generating conformers of a molecule in
3D space given its molecular graph. We parameterize these conformers as
continuous functions that map elements from the molecular graph to points in 3D
space. We then formulate the problem of learning to generate conformers as
learning a distribution over these functions using a diffusion generative
model, called Molecular Conformer Fields (MCF). Our approach is simple and
scalable, and achieves state-of-the-art performance on challenging molecular
conformer generation benchmarks while making no assumptions about the explicit
structure of molecules (e.g. modeling torsional angles). MCF represents an
advance in extending diffusion models to handle complex scientific problems in
a conceptually simple, scalable and effective manner.

Chemical Physics;Machine Learning