acePredicting Biomolecular Interactions in the Next Decade: Physics-Based Methods Meet AI-Driven Approaches
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 g…
Data-driven machine learning models can rapidly generate biomolecular structures and propose conformational ensembles for recognition events with high predictive performance.
Machine learning models for biomolecular recognition do not inherently enforce thermodynamic consistency and may produce configurations that are not physically realizable.
Physics-based sampling remains limited by computational cost and force field accuracy, while ML ensembles lack guaranteed physical realizability due to missing partition function link.
Evidence
- Peer-reviewedThe Journal of Physical Chemistry Letters2026-07-17
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Truvace Impact Record TRV-2026-0244, v1: “Predicting Biomolecular Interactions in the Next Decade: Physics-Based Methods Meet AI-Driven Approaches.” Truvace, 2026-07-17. /record/TRV-2026-0244 (accessed at citation time). sha256 98fcb59acd74e0cb…
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