Sampling diverse, thermodynamically feasible molecular conformations plays a crucial role in predicting properties of a molecule. In this paper we propose to use GFlowNet for sampling conformations of small molecules from the Boltzmann distribution, as determined by the molecule's energy. The proposed approach can be used in combination with energy estimation methods of different fidelity and discovers a diverse set of low-energy conformations for highly flexible drug-like molecules. We demonstrate that GFlowNet can reproduce molecular potential energy surfaces by sampling proportionally to the Boltzmann distribution.
@article{arxiv.2310.14782,
title = {Towards equilibrium molecular conformation generation with GFlowNets},
author = {Alexandra Volokhova and Michał Koziarski and Alex Hernández-García and Cheng-Hao Liu and Santiago Miret and Pablo Lemos and Luca Thiede and Zichao Yan and Alán Aspuru-Guzik and Yoshua Bengio},
journal= {arXiv preprint arXiv:2310.14782},
year = {2023}
}