INTEGRATING AI AND MOLECULAR MODELING: TOWARD ULTRAPRECISE PROTEIN STRUCTURE PREDICTION

Abstract

Accurate protein structure prediction is fundamental to understanding biological function and accelerating drug discovery. Recent breakthroughs in deep learning, exemplified by AlphaFold, have reshaped structural biology by producing high-accuracy models from sequence alone. However, challenges remain for difficult targets, conformational ensembles, and dynamic regions. This paper explores an integrated approach that couples state-of-the-art AI predictors with molecular modeling and physicsbased refinement to achieve ultra-precise protein structures. We propose a hybrid pipeline that uses deep-learning-derived models as starting conformations for molecular dynamics (MD) refinement guided by enhanced sampling and specialized force-field tuning. A synthetic experimental comparison across a set of representative proteins demonstrates that the hybrid approach reduces RMSD relative to AlphaFold alone while providing physically consistent conformations and uncertainty-aware predictions. The results highlight trade-offs between computational cost and accuracy, and we discuss practical guidelines for choosing approaches based on target properties