Generating 3D molecular conformers via equivariant coarse-graining and aggregated attention

Molecular conformer generation is a fundamental task in computational chemistry. The objective is to predict stable low-energy 3D molecular structures, known as conformers, given the 2D molecule. Accurate molecular conformations are crucial for various applications that depend on precise spatial and geometric qualities, including drug discovery and protein docking. We introduce CoarsenConf, an SE(3)-equivariant hierarchical variational autoencoder (VAE) that pools information from fine-grain atomic coordinates to a coarse-grain subgraph level representation for efficient autoregressive conformer generation. Coarse-graining reduces the dimensionality of the problem allowing conditional autoregressive generation rather than generating all coordinates independently, as done in prior work.