AI-Enhanced Study of Biomolecular Dynamics with Molecular Dynamics Simulations

Dear Colleagues,

Dynamics in biomolecules play an essential role in various biological processes such as enzymatic reactions, protein folding, and molecular recognition. Dynamics study in biomolecules combines computational modeling, experimental techniques, and theoretical approaches to enhance our understanding of the intricate workings of life at a molecular level. As a computational technique for studying biomolecular dynamics, molecular dynamics (MD) simulation applies principles of classical physics to simulate the motions and interactions of atoms in a biomolecule over time. With the recent success of artificial intelligence (AI) in protein structure prediction, the potential of AI to revolutionize the study of biomolecular dynamics with MD simulations has been demonstrated in many recent publications. We expect that, in the future, AI will be integrated in unexpected and smart ways into the various aspects (such as force field, propagation algorithm, sampling, and analysis) of dynamic study on biomolecules with MD simulations and expand dramatically the applicability of MD simulations in biomolecular systems.

We look forward to collecting research reporting advances in the AI-enhanced study of biomolecular dynamics with MD simulations. Topics of interest include but are not limited to the following general areas.

AI-based force field;

AI-based propagation algorithm;

AI-enhanced sampling algorithm;

AI-enhanced analysis approach;

Enzymatic reactions;

RNA structure and dynamics;

Protein–nucleic acid complexes;

Protein–ligand complexes;

Protein–membrane systems;

Protein–protein complexes.

Dr. Wenjin Li
Dr. Muhammad Junaid
Guest Editors

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