Predicting low-energy molecular conformations given a molecular graph is an important but challenging task in computational drug discovery. Existing state-of-the-art approaches either resort to large scale transformer-based models that diffuse over conformer fields, or use computationally expensive methods to generate initial structures and diffuse over torsion angles.

We show that our method successful generates low energy transitions for Alanine Dipeptide as well as the larger Polyproline and Chignolin proteins.

As shown in Figure 13, we applied higher noise settings (SNR 0.005, 0.001) to the

Single particle cryo-electron microscopy (cryo-EM) is a widely used imaging technique for visualizing biomolecular complexes at near-atomic resolution.

However, these methods have notable limitations.

Following this thought, we recast virtual screening as an information retrieval task, i.e., given a

Neural Information Processing Systems http://nips.cc/

Because of the generality of Definition 1.1, it may be that