  Prediction of a molecule's 3D conformer ensemble from the molecular graph
holds a key role in areas of cheminformatics and drug discovery. Existing
generative models have several drawbacks including lack of modeling important
molecular geometry elements (e.g. torsion angles), separate optimization stages
prone to error accumulation, and the need for structure fine-tuning based on
approximate classical force-fields or computationally expensive methods such as
metadynamics with approximate quantum mechanics calculations at each geometry.
We propose GeoMol--an end-to-end, non-autoregressive and SE(3)-invariant
machine learning approach to generate distributions of low-energy molecular 3D
conformers. Leveraging the power of message passing neural networks (MPNNs) to
capture local and global graph information, we predict local atomic 3D
structures and torsion angles, avoiding unnecessary over-parameterization of
the geometric degrees of freedom (e.g. one angle per non-terminal bond). Such
local predictions suffice both for the training loss computation, as well as
for the full deterministic conformer assembly (at test time). We devise a
non-adversarial optimal transport based loss function to promote diverse
conformer generation. GeoMol predominantly outperforms popular open-source,
commercial, or state-of-the-art machine learning (ML) models, while achieving
significant speed-ups. We expect such differentiable 3D structure generators to
significantly impact molecular modeling and related applications.
