AI-Driven Digital Reactor for TLR4-Targeted Pulmonary Peptide Discovery

Pulmonary inflammatory diseases remain a major global health challenge, necessitating the development of novel, targeted therapeutic strategies. Peptide-based therapeutics have emerged as promising candidates due to their high specificity, biocompatibility, and tunable physicochemical properties. However, traditional experimental screening of therapeutic peptides is time-consuming and resource-intensive. In this study, we propose and implement a smart digital reactor, a fully computational, AI-assisted frame work for the systematic screening, evaluation, and molecular validation of pulmonary therapeutic peptides. The digital reactor integrates curated peptide datasets, physicochemical filtering under lung-specific conditions, feature extraction, multi-criteria scoring, structural modeling, and molecular docking analysis. Using this approach, a golden peptide candidate (P009) was identified and structurally evaluated for its interaction with Toll-like receptor 4 (TLR4), a key mediator of pulmonary inflammatory signaling. Docking results revealed a stable binding mode dominated by electrostatic and hydrogen-bond interactions, highlighting critical lysine residues as binding hotspots. This work demonstrates the feasibility of a digital reactor paradigm for accelerating pulmonary peptide drug discovery and provides a mechanistic foundation for future molecular dynamics simulations and experimental validation.