Glucose-Responsive Insulin Design via Hybrid Machine Learning, Molecular Dynamics, and Pareto Selection

Abstract

Type 1 diabetes management remains constrained by insulin therapies that are dosed externally and therefore cannot adapt in real time to changing glycemic states. This gap motivates glucose-responsive insulin design, where molecular activity is attenuated in hypoglycemia and amplified in hyperglycemia. We study this objective as a computational prioritization problem rather than a full clinical development program: given insulin-centered molecular candidates, predict a state-conditional activity profile and select a shortlist for downstream experiments under bounded compute. The proposed method is a three-stage hybrid pipeline: a tri-state ranker enforces monotone low/normal/high behavior with uncertainty penalties; a mechanistic reranker augments machine-learning scores with paired-context molecular dynamics descriptors and explicit low-glucose safety gating; and an uncertainty-aware Pareto selector balances efficacy, hypoglycemia risk, and manufacturability. We provide formal definitions of objectives, feasible sets, and optimality criteria, and include theorem-level results with complete proofs for ordering and dominance properties. Validation uses a reproducible synthetic proxy protocol with fixed seeds, sweeps, confidence intervals, and symbolic checks. Relative to strong baselines, the selected pipeline improves tri-state rank correlation, reduces top-k false positives under safety constraints, raises nondominated shortlist quality, and passes symbolic assumption checks. The findings support a practical thesis: hybrid statistical-mechanistic ranking with explicit multi-objective uncertainty control can materially improve preclinical computational triage for glucose-responsive insulin candidates, while still exposing key data and translation gaps that must be addressed for broader external validity.