  We present a diffusion-based, generative model for conformer generation. Our
model is focused on the reproduction of bonded structure and is constructed
from the associated terms traditionally found in classical force fields to
ensure a physically relevant representation. Techniques in deep learning are
used to infer atom typing and geometric parameters from a training set.
Conformer sampling is achieved by taking advantage of recent advancements in
diffusion-based generation. By training on large, synthetic data sets of
diverse, drug-like molecules optimized with the semiempirical GFN2-xTB method,
high accuracy is achieved for bonded parameters, exceeding that of
conventional, knowledge-based methods. Results are also compared to
experimental structures from the Protein Databank (PDB) and Cambridge
Structural Database (CSD).
