Physics-Informed Generative Model for Drug-like Molecule Conformers

We present a diffusion-based generative model for conformer
generation.
Our model is focused on the reproduction of the 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 and the Cambridge Structural Database.