Generation of conformational ensembles of small molecules via surrogate model-assisted molecular dynamics
Journal article, 2024

The accurate prediction of thermodynamic properties is crucial in various fields such as drug discovery and materials design. This task relies on sampling from the underlying Boltzmann distribution, which is challenging using conventional approaches such as simulations. In this work, we introduce surrogate model-assisted molecular dynamics (SMA-MD), a new procedure to sample the equilibrium ensemble of molecules. First, SMA-MD leverages deep generative models to enhance the sampling of slow degrees of freedom. Subsequently, the generated ensemble undergoes statistical reweighting, followed by short simulations. Our empirical results show that SMA-MD generates more diverse and lower energy ensembles than conventional MD simulations. Furthermore, we showcase the application of SMA-MD for the computation of thermodynamical properties by estimating implicit solvation free energies.

molecular dynamics

generative models

equilibrium sampling

molecular conformation generation

property prediction

Boltzmann distribution

Author

Juan Viguera Diez

AstraZeneca AB

Chalmers, Computer Science and Engineering (Chalmers), Data Science and AI

Sara Romeo Atance

AstraZeneca AB

Student at Chalmers

Ola Engkvist

Chalmers, Computer Science and Engineering (Chalmers)

AstraZeneca AB

Simon Olsson

Chalmers, Computer Science and Engineering (Chalmers), Data Science and AI

Machine Learning: Science and Technology

26322153 (eISSN)

Vol. 5 2 025010

Subject Categories

Medical Engineering

Theoretical Chemistry

DOI

10.1088/2632-2153/ad3b64

More information

Latest update

4/26/2024