Physics-Guided Equivariant Neural Networks for Protein Microenvironment Modeling and pKa Prediction
Keywords:
Equivariant neural networks, protein pKa prediction, microenvironment modeling, deep learning, biophysics, SE(3) equivariance, physics-guided machine learningAbstract
Predicting the pKa values of ionizable residues in proteins is a fundamental challenge in biophysics, with profound implications for drug design, enzyme engineering, and understanding biological mechanisms. The accuracy of pKa prediction depends critically on the faithful representation of the protein microenvironment, a three-dimensional chemical context characterized by electrostatic fields, hydrogen-bonding networks, and solvent exposure. Recent advances in geometric deep learning, particularly equivariant neural networks that respect the symmetries of physical space, offer a promising avenue for learning protein microenvironments directly from atomic coordinates. This paper presents a system-level analysis of physics-guided equivariant neural networks for microenvironment modeling and pKa prediction. We examine the architectural integration of SE(3)-equivariant message passing with physically inspired features such as continuum electrostatics and solvation free energies, discussing trade-offs between model expressiveness, interpretability, and computational efficiency. Beyond algorithmic design, we address the broader infrastructure required for training and deploying these models, including data curation from structural databases, high-performance computing considerations, and robustness to conformational variability. Furthermore, we explore governance and policy dimensions: the imperative for standardized benchmarks, fairness across diverse protein families, reproducibility through open-source release, and the ethical implications of precisely predicting protein properties that could be misused. We also consider sustainability metrics and the potential of amortized inference to mitigate the carbon footprint of large-scale training. By connecting architectural innovation with system-level thinking, this work charts a roadmap for responsible development of physics-guided equivariant models that can transform molecular therapeutics while adhering to principles of equity and scientific rigor.
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