Predicting Biomolecular Interactions in the Next Decade: Physics-Based Methods Meet AI-Driven Approaches

Area of Science:

• Molecular modeling
• Computational biochemistry
• Biophysics

Background:

• Quantitative prediction of biomolecular recognition is essential for molecular science.
• The field is shifting from structure-centric to ensemble-based descriptions.
• Two main modeling strategies exist: physics-based and data-driven (machine learning).

Purpose of the Study:

• To compare and contrast physics-based and machine learning approaches for modeling molecular ensembles.
• To highlight the complementary strengths and weaknesses of each method.
• To propose a future direction for integrating these approaches.

Main Methods:

• Physics-based methods: Molecular dynamics, free energy perturbation.
• Data-driven methods: Machine learning models trained on structural and bioactivity data.
• Analysis of thermodynamic consistency and predictive accuracy.

Main Results:

• Physics-based methods offer mechanistic interpretability and thermodynamic consistency but are computationally expensive.
• Machine learning methods provide rapid structure generation and high predictive performance but may lack thermodynamic rigor.
• Each method approximates the underlying probability distribution of biomolecular recognition events.

Conclusions:

• Physics-based simulations and machine learning provide complementary insights into biomolecular recognition.
• Integrating these hybrid frameworks is crucial for scalable and transferable models.
• Future research should focus on combining the strengths of both approaches for enhanced predictive power and physical realism.