Generating Molecular Conformers with Manifold Diffusion Fields
AuthorsYuyang Wang, Ahmed Elhag, Navdeep Jaitly, Josh Susskind, Miguel Angel Bautista Martin
AuthorsYuyang Wang, Ahmed Elhag, Navdeep Jaitly, Josh Susskind, Miguel Angel Bautista Martin
This paper was accepted at Generative AI and Biology workshop at NeurIPS 2023.
In this paper we tackle the problem of generating a molecule conformation in 3D space given its 2D structure. We approach this problem through the lens of a diffusion model for functions in Riemannian Manifolds. Our approach is simple and scalable, and obtains results that are on par with state-of-the-art while making no assumptions about the explicit structure of molecules.
May 24, 2024research area Methods and Algorithmsconference ICML
We present a novel way to predict molecular conformers through a simple formulation that sidesteps many of the heuristics of prior works and achieves state of the art results by using the advantages of scale. By training a diffusion generative model directly on 3D atomic positions without making assumptions about the explicit structure of molecules (e.g. modeling torsional angles) we are able to radically simplify structure learning, and make it...
We present Manifold Diffusion Fields (MDF), an approach that unlocks learning of diffusion models of data in general non-euclidean geometries. Leveraging insights from spectral geometry analysis, we define an intrinsic coordinate system on the manifold via the eigen-functions of the Laplace-Beltrami Operator. MDF represents functions using an explicit parametrization formed by a set of multiple input-output pairs. Our approach allows to sample...