Towards Equilibrium Molecular Conformation Generation with GFlowNets
Abstract
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.
Cite
Text
Volokhova et al. "Towards Equilibrium Molecular Conformation Generation with GFlowNets." NeurIPS 2023 Workshops: AI4Mat, 2023.Markdown
[Volokhova et al. "Towards Equilibrium Molecular Conformation Generation with GFlowNets." NeurIPS 2023 Workshops: AI4Mat, 2023.](https://mlanthology.org/neuripsw/2023/volokhova2023neuripsw-equilibrium/)BibTeX
@inproceedings{volokhova2023neuripsw-equilibrium,
title = {{Towards Equilibrium Molecular Conformation Generation with GFlowNets}},
author = {Volokhova, Alexandra and Koziarski, Michał and Hernández-García, Alex and Liu, Cheng-Hao and Miret, Santiago and Lemos, Pablo and Thiede, Luca and Yan, Zichao and Aspuru-Guzik, Alan and Bengio, Yoshua},
booktitle = {NeurIPS 2023 Workshops: AI4Mat},
year = {2023},
url = {https://mlanthology.org/neuripsw/2023/volokhova2023neuripsw-equilibrium/}
}