Artificial intelligence for molecular mechanism discovery

Autor(en)

Hendrik Jung, Roberto Covino, A. Arjun, Christian Leitold, Peter G. Bolhuis, Christoph Dellago, Gerhard Hummer

Abstrakt

We develop a machine learning algorithm to extract the mechanism of transitions between metastable states from molecular dynamics simulations. Our algorithm combines transition path sampling (TPS), deep learning, and statistical inference to simulate the dynamics of complex molecular reorganizations. We iteratively train a deep learning model on the outcomes of the shooting moves used in TPS. By learning to predict the transition dynamics, the artificial intelligence (AI) system at the center of the algorithm gradually reveals the underlying mechanism of the transition dynamics, and at the same time increases the efficiency of the rare-event sampling. The AI system can simultaneously learn from and guide multiple TPS simulations running in parallel. In a second step, we then distill the knowledge about the transition mechanism encoded in the deep learning model into a reduced mathematical model. The reduced model concisely represents the key features of the mechanism in an explicit analytical, human-understandable form. We apply the algorithm to molecular systems ranging from ion dissociation in aqueous solution over gas hydrate formation to the oligomerization of a transmembrane alpha helix involved in membrane sensing. In all cases, the AI system accurately predicts the transition dynamics and extracts reduced mathematical models of the underlying mechanisms.

Organisation(en)

Computergestützte Physik und Physik der Weichen Materie

Externe Organisation(en)

Max-Planck-Institut für Biophysik, Frankfurt Institute for Advanced Studies (FIAS), University of Amsterdam (UvA)

Journal

Biophysical Journal

Band

122

Seiten

281a-282a

Anzahl der Seiten

1

ISSN

0006-3495

DOI

https://doi.org/10.1016/j.bpj.2022.11.1602

Publikationsdatum

02-2023

Peer-reviewed

Ja

ÖFOS 2012

106006 Biophysik, 103043 Computational Physics

ASJC Scopus Sachgebiete

Biophysics

Link zum Portal

https://ucrisportal.univie.ac.at/de/publications/7d3bd5f1-afc4-4fc7-9ab7-0b854f7554fa