Graph Foundation Models for Protein Electrostatics: Transfer Learning Across Ionization and Stability Prediction Tasks
Keywords:
protein electrostatics; graph foundation models; transfer learning; ionization states; protein stability; molecular graphs; large-scale systemsAbstract
Predicting the electrostatic properties of proteins remains a fundamental challenge for molecular biology and drug design, particularly the accurate estimation of ionization states and thermodynamic stability across diverse sequence and structural contexts. Recent breakthroughs in deep learning have opened a pathway toward graph-based foundation models that can capture complex physical interactions at scale, yet a comprehensive systems perspective that spans architecture design, transfer learning strategies, infrastructure deployment, and socio-technical implications is underdeveloped. This paper presents a long-form analysis of graph foundation models tailored for protein electrostatics, with a focus on transfer learning between the prediction of residue-level pKa values and the estimation of mutation-induced stability changes. We examine the underlying architectural trade-offs that arise when enforcing equivariance, incorporating multi-scale attention, and designing pre-training objectives that reconcile physical priors with data-driven learning. The work systematically discusses how such models can be fine-tuned for distinct downstream tasks while managing catastrophic forgetting, calibration, and domain shift. Beyond algorithmic concerns, we address the computational infrastructure required to train and serve these large models sustainably, and we interrogate the fairness and representational biases that may emerge from uneven coverage of protein families in training corpora. Governance, policy, and reproducibility frameworks are evaluated alongside deployment scenarios in industrial drug discovery pipelines. By weaving together structural design, system engineering, and regulatory foresight, this paper provides a holistic reference for the next generation of protein electrostatics models, arguing that scientific impact and societal robustness must evolve in tandem with architectural innovation.
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