Conformation Generation using Transformer Flows
This addresses a bottleneck in molecular modeling for drug discovery or materials science, offering a significant performance gain for large molecules.
The paper tackles the problem of generating 3D molecular conformations for large molecules, where existing methods scale poorly, and presents ConfFlow, a flow-based transformer model that improves accuracy by up to 40% compared to state-of-the-art learning-based methods.
Estimating three-dimensional conformations of a molecular graph allows insight into the molecule's biological and chemical functions. Fast generation of valid conformations is thus central to molecular modeling. Recent advances in graph-based deep networks have accelerated conformation generation from hours to seconds. However, current network architectures do not scale well to large molecules. Here we present ConfFlow, a flow-based model for conformation generation based on transformer networks. In contrast with existing approaches, ConfFlow directly samples in the coordinate space without enforcing any explicit physical constraints. The generative procedure is highly interpretable and is akin to force field updates in molecular dynamics simulation. When applied to the generation of large molecule conformations, ConfFlow improve accuracy by up to $40\%$ relative to state-of-the-art learning-based methods. The source code is made available at https://github.com/IntelLabs/ConfFlow.