Torsional-GFN: a conditional conformation generator for small molecules

Lena Nehale Ezzine, Alexandra Volokhova, Piotr Gaiński, Luca Scimeca, Emmanuel Bengio, Prudencio Tossou, Yoshua Bengio, Alex Hernández-García

Keywords: GFLowNet, molecular conformations, torsion angles, Boltzmann distribution, generative models

Abstract:

Generating stable molecular conformations is crucial in several drug discovery applications, such
as estimating the binding affinity of a molecule to
a target. Recently, generative machine learning
methods have emerged as a promising, more efficient method than molecular dynamics for sampling of conformations from the Boltzmann distribution. In this paper, we introduce Torsional-
GFN, a conditional GFlowNet specifically designed to sample conformations of molecules proportionally to their Boltzmann distribution, using
only a reward function as training signal. Conditioned on a molecular graph and its local structure
(bond lengths and angles), Torsional-GFN samples rotations of its torsion angles. Our results
demonstrate that Torsional-GFN is able to sample conformations approximately proportional to
the Boltzmann distribution for multiple molecules
with a single model, and allows for zero-shot
generalization to unseen bond lengths and angles coming from the MD simulations for such
molecules. Our work presents a promising avenue
for scaling the proposed approach to larger molecular systems, achieving zero-shot generalization
to unseen molecules, and including the generation
of the local structure into the GFlowNet model.

Submission Number: 141