PeptimAIze: Revolutionazing Peptide Design through Multi-Property Optimization and Explainable AI

PeptimAIze aims to develop a deep learning model capable of generating therapeutic peptides for inhibiting protein-protein interactions (PPIs). Many PPIs are essential to key physiological and pathological processes, making these interactions ideal targets for selective intervention in several human diseases, including cancer and bacterial infections. However, the therapeutic potential of peptides is often limited by unfavorable physicochemical properties, such as low solubility and poor bioavailability. Current peptide discovery approaches are time- and resource-intensive, generating significant waste through iterative and labour-intensive screening of compound batches across multiple optimisation rounds to experimentally achieve the desired physicochemical properties. While deep learning and generative models have recently emerged for on-demand peptide design, most focus on a single aspect, typically binding affinity, disregarding physicochemical properties and experimental validation. PeptimAIze addresses these gaps by developing a multi-objective deep learning model based on protein language processing that designs peptides by co-optimising binding to the target and the physicochemical properties necessary for optimal administrability and bioavailability. The model will incorporate explainable AI techniques to enhance interpretability and reveal which amino acids in the peptide sequence are associated with the tuning of each considered property. A key step will be the wet-lab validation of the model on targets responsible for vital bacterial functions (Doc/Phd) and cancer development (p53/MDM2 and Bcl-xL/Bak), demonstrating real-world lab applicability. To maximise impact and usability, the model will be deployed through an intuitive, user-friendly app, enabling researchers in academia and industry to design peptides with tailored properties.