Towards equilibrium molecular conformation generation with GFlowNets

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..

Medienart:

Preprint

Erscheinungsjahr:

2023

Erschienen:

2023

Enthalten in:

arXiv.org - (2023) vom: 20. Okt. Zur Gesamtaufnahme - year:2023

Sprache:

Englisch

Beteiligte Personen:

Volokhova, Alexandra [VerfasserIn]
Koziarski, Michał [VerfasserIn]
Hernández-García, Alex [VerfasserIn]
Liu, Cheng-Hao [VerfasserIn]
Miret, Santiago [VerfasserIn]
Lemos, Pablo [VerfasserIn]
Thiede, Luca [VerfasserIn]
Yan, Zichao [VerfasserIn]
Aspuru-Guzik, Alán [VerfasserIn]
Bengio, Yoshua [VerfasserIn]

Links:

Volltext [kostenfrei]

Themen:

000
Computer Science - Artificial Intelligence
Computer Science - Machine Learning

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

XAR041301463