Supplementary data: Interpretable Machine Learning for the Rational Design and In Vitro Validation of SET-M33 Dendrimeric Antimicrobial Peptides

This repository contains the data, models, and analysis files supporting the study "Interpretable Machine Learning for the Rational Design and In Vitro Validation of SET-M33 Dendrimeric Antimicrobial Peptides". The work develops an interpretable machine-learning framework to design and prioritize species-specific SET-M33 antimicrobial peptide analogs against Escherichia coli, Pseudomonas aeruginosa, and Klebsiella pneumoniae, and validates selected candidates in vitro. The pipeline comprises: (1) Random Forest MIC-prediction models trained on species-specific physicochemical descriptor pools (StarPep/ProtDCal descriptors, WEKA feature selection); (2) generation and prediction of N-terminally elongated cationic analogs; (3) organization of analogs into High-Similarity Physicochemical Networks (HSPN) with Louvain modules; (4) interpretation of activity drivers through SHAP and UMAP analysis; and (5) selection and in vitro MIC validation of candidates. The repository is organized into Training (databases, descriptor pools, and trained models), Test (generated analogs and predictions), Similarity networks (threshold sweeps and selected networks), Post-analysis (SHAP and UMAP outputs), and MIC test (in vitro results for synthesized candidates). A Code folder with the analysis notebooks (HSPN construction, candidate selection, SHAP, and functional-space analysis), documented in an accompanying README, is also included. A README file describing the full directory structure and naming conventions is included.