Predicting Biomolecular Interactions in the Next Decade: Physics-Based Methods Meet AI-Driven Approaches.
TL;DR
It is argued that, since physics-based simulations and machine learning provide complementary approximations to the underlying probability distribution associated with biomolecular recognition events, and they excel respectively in consistency with free-energy landscapes and state populations and in predictive accuracy, the central challenge for the coming decade will be integrating them into hybrid frameworks that are scalable and transferable.
Abstract
The quantitative prediction of biomolecular recognition is crucial to molecular science. The challenge is not merely structural determination but the prediction of (thermo)dynamic and kinetic observables arising from high-dimensional molecular ensembles, such as free energies, conformational distributions, and rate processes across different conditions. As the field shifts from structure-centric to ensemble-based descriptions, two complementary modeling strategies have matured: explicit energy-based approaches grounded in statistical mechanics and data-driven models that learn statistical representations of molecular configurations from large data sets. Physics-based methods, including molecular dynamics and free energy perturbation, estimate observables by sampling (Boltzmann-distributed) configurations under approximate molecular Hamiltonians, thereby providing mechanistic interpretability and thermodynamic consistency, albeit at non-negligible computational cost and with inherent force field limitations. In contrast, modern machine learning approaches rapidly generate structures and propose conformational ensembles without explicit thermodynamic weighting, by learning statistical patterns in structural and bioactivity data. While these methods often achieve high predictive performance, they do not inherently enforce thermodynamic consistency due to the lack of an explicit connection to a partition function and thus may produce configurations that are not physically realizable. We argue that, since physics-based simulations and machine learning provide complementary approximations to the underlying probability distribution associated with biomolecular recognition events, and they excel respectively in consistency with free-energy landscapes and state populations and in predictive accuracy, the central challenge for the coming decade will be integrating them into hybrid frameworks that are scalable and transferable.