  Cell-penetrating peptides (CPPs) are powerful vectors for the intracellular
delivery of a diverse array of therapeutic molecules. Despite their potential,
the rational design of CPPs remains a challenging task that often requires
extensive experimental efforts and iterations. In this study, we introduce an
innovative approach for the de novo design of CPPs, leveraging the strengths of
machine learning (ML) and optimization algorithms. Our strategy, named
LightCPPgen, integrates a LightGBM-based predictive model with a genetic
algorithm (GA), enabling the systematic generation and optimization of CPP
sequences. At the core of our methodology is the development of an accurate,
efficient, and interpretable predictive model, which utilizes 20 explainable
features to shed light on the critical factors influencing CPP translocation
capacity. The CPP predictive model works synergistically with an optimization
algorithm, which is tuned to enhance computational efficiency while maintaining
optimization performance. The GA solutions specifically target the candidate
sequences' penetrability score, while trying to maximize similarity with the
original non-penetrating peptide in order to retain its original biological and
physicochemical properties. By prioritizing the synthesis of only the most
promising CPP candidates, LightCPPgen can drastically reduce the time and cost
associated with wet lab experiments. In summary, our research makes a
substantial contribution to the field of CPP design, offering a robust
framework that combines ML and optimization techniques to facilitate the
rational design of penetrating peptides, by enhancing the explainability and
interpretability of the design process.
