Learning Neural Generative Dynamics for Molecular Conformation Generation

Understand

We study how to generate molecule conformations (i.e., 3D structures) from a molecular graph.

• Traditional methods, such as molecular dynamics, sample conformations via computationally expensive simulations.
• Recently, machine learning methods have shown great potential by training on a large collection of conformation data.
• Challenges arise from the limited model capacity for capturing complex distributions of conformations and the difficulty in modeling long-range dependencies between atoms.