DeepCPP: A Deep Learning‐Based Framework for Enhanced Prediction of Cell Penetrating Peptides

Cell‐penetrating peptides (CPPs) are bioactive molecules characterized by a positive charge, primarily attributed to lysine, arginine, or histidine residues, which enable their traversal across cells’ lipid bilayer membranes. This unique property positions CPPs as promising candidates for intracellular delivery of therapeutic agents. The expanding database of experimentally validated CPPs has facilitated significant advancements in computational modeling, enabling in silico prediction and engineering of established and novel CPPs. Despite progress in this domain, challenges persist in improving the performance of CPP prediction models. This study introduces DeepCPP, a new deep learning‐based framework for efficient CPP prediction. DeepCPP employs a multihead decision‐level late‐fusion approach that uses a trainable weighted fusion mechanism to combine branch‐specific predictions generated by a multilayer convolutional neural network‐guided encoder and a multilayer graph neural network‐guided encoder. These encoders learn evolutionary features from an evolutionary information‐based representation and chemical, topological, and stereochemical features from a molecular graph‐based representation, respectively. Comprehensive experiments conducted on established benchmark datasets reveal the effectiveness of the DeepCPP model, exhibiting performance superior to state‐of‐the‐art models in CPP prediction. To promote broader accessibility, we provide a user‐friendly web service for the proposed DeepCPP model, accessible at https://deepcpp.streamlit.app/ .