Artificial intelligence-driven prediction and design of cell-penetrating peptides for advanced drug delivery system
TL;DR
An artificial intelligence-driven framework that integrates biochemical rules derived from large language models (LLMs) with conventional peptide descriptors for CPP prediction and design is developed and demonstrates that combining LLM-derived biochemical knowledge with machine learning improves interpretable CPP prediction and candidate prioritisation.
Abstract
Background Cell-penetrating peptides (CPPs) are promising delivery vectors for transporting therapeutic agents across cellular membranes. However, their rational design remains challenging because the relationship between peptide sequence and translocation efficiency is highly complex and nonlinear. Methods In this study, we developed an artificial intelligence-driven framework that integrates biochemical rules derived from large language models (LLMs) with conventional peptide descriptors for CPP prediction and design. Interpretable rules extracted from GPT-4o and DeepSeek were encoded as binary feature vectors and combined with sequence-based descriptors to construct hybrid machine learning models. Model performance was evaluated on the benchmark CPP924 dataset using repeated stratified cross-validation, and the optimized models were further used for de novo CPP generation. The resulting candidates were subsequently assessed using multiple established computational benchmarks. Results The top-performing hybrid classifier achieved a cross-validated accuracy of 0.91 ± 0.03 (best single held-out split, 0.94) on the CPP924 dataset. The LLM-derived rules outperformed conventional physicochemical and fingerprint descriptors and matched amino-acid composition; integrating the rule and composition features yielded the best overall classifier. Using the optimized RF-GPT-Fre and RF-DS-Fre models, we generated six de novo CPP candidates that are sequence-novel (≤53% identity to any training peptide) and retain CPP-like composition and structural features. In-silico evaluation across established tools supports computational prioritisation of these candidates for experimental testing. Conclusion These findings demonstrate that combining LLM-derived biochemical knowledge with machine learning improves interpretable CPP prediction and candidate prioritisation. This study provides a reproducible computational strategy for peptide engineering and establishes a basis for the experimental evaluation of next-generation drug-delivery vehicles.