Automated High-Throughput Screening and Design of Integrin-Targeted Peptide Libraries for Enhanced Cell Adhesion Modulation via Bayesian Optimization and Multi-objective Evolutionary Algorithms

**Abstract:** Integrins play a pivotal role in cell adhesion, migration, and signaling, making them attractive targets for therapeutic intervention in diseases ranging from cancer to fibrosis. Traditional peptide library screening for integrin-selective ligands is labor-intensive and inefficient. This research presents a novel framework, combining Bayesian optimization (BO) with multi-objective evolutionary algorithms (MOEA) and high-throughput cellular assays, to dramatically accelerate the discovery and optimization of integrin-targeted peptides with enhanced cell adhesion modulation capabilities. The framework facilitates both quantitative structure-activity relationship (QSAR) modeling and automated design of peptide sequences, significantly increasing the probability of identifying potent and selective ligands across a broad range of integrin subtypes. This approach capitalizes on existing peptide synthesis technologies and cellular assay platforms for rapid, automated screening, ultimately yielding a rapid pipeline for developing integrin modulators usable in regenerative medicine and targeted drug delivery.