Towards equilibrium molecular conformation generation with GFlowNets

Alexandra Volokhova, Michał Koziarski, Alex Hernández-García, Cheng-Hao Liu, Santiago Miret +5 more
10/20/2023
cs.LGcs.AI

No confident code implementation found for this paper.

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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Torsional-GFN: a conditional conformation generator for small molecules

86% match2025

Learning Neural Generative Dynamics for Molecular Conformation Generation

85% match2 repos472021

FlexiFlow: decomposable flow matching for generation of flexible molecular ensemble

84% match2025

Learning conformational ensembles of proteins based on backbone geometry

83% match1 repo472025

Transferable Boltzmann Generators

82% match2024

Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learning

82% match2023

Cite this paper

@article{volokhova2023towards,
  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},
  year   = {2023},
  eprint = {2310.14782},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2310.14782v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2310.14782.svg)](https://codeofpaper.com/paper/2310.14782)

Discussion