@inproceedings{
volokhova2023towards,
title={Towards equilibrium molecular conformation generation with {GF}lowNets},
author={Alexandra Volokhova and Micha{\l} Koziarski and Alex Hern{\'a}ndez-Garc{\'\i}a and Cheng-Hao Liu and Santiago Miret and Pablo Lemos and Luca Thiede and Zichao Yan and Alan Aspuru-Guzik and Yoshua Bengio},
booktitle={AI for Accelerated Materials Design - NeurIPS 2023 Workshop},
year={2023},
url={https://openreview.net/forum?id=BFIoxpFeZ2}
}
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.
Towards equilibrium molecular conformation generation with GFlowNets
Alexandra Volokhova Michał Koziarski Alex Hernández-García Cheng-Hao Liu Santiago Miret Pablo Lemos Luca Thiede Zichao Yan Alan Aspuru-Guzik Yoshua Bengio