Pepti-DrugPhAI: Multimodal AI-evaluator for functional pharmacokinetics and toxicity of peptides

Peptide drugs exhibit exceptional selectivity for biological targets and high functional activity making them among the most promising therapeutic agents. However, unsatisfactory pharmacokinetic characteristics remain a critical barrier to their implementation. They often become the primary reason for the failure of promising candidates in preclinical and clinical trials. To address this issue, we present Pepti-DrugPhAI – a comprehensive algorithm for the precise prediction of the pharmacokinetics and toxicity of a wide range of peptide compounds. Unlike many existing solutions, the proposed algorithm is multimodal and can simultaneously process both SMILES representations of chemical structures and amino acid sequences. This ensures efficient processing not only of canonical peptides but also of non-canonical modifications, cyclic structures, and peptide-like small molecules. We also created an original dataset for model training integrating data from public databases and relevant scientific publications. The algorithm simultaneously predicts key parameters including membrane permeability (Caco-2 and PAMPA penetration), distribution and excretion (half-life, clearance, volume of distribution), as well as general and specific toxicity profiles (neurotoxicity, hemolytic activity). A key innovation of the study is the integration of functional pharmacokinetic scoring. The model not only predicts ADMET values but also calculates an adaptive suitability rating for a compound in the context of its specific biological activity. We have conducted a comparative analysis of various data encoding strategies implementing a stack of models ranging from classical machine learning methods to modern transformers and language models in classification modes. Results from testing on independent test sets demonstrate high prediction accuracy and interpretability of features making Pepti-DrugPhAI a powerful tool for more robust selection of promising peptide drug candidates. All data and the code are available in the GitHub repository at https://github.com/EABogdanova/Pepti-DrugPhAI .