A Multi-View Deep Learning Method for Predicting Blood-Brain Barrier Permeability of Peptides

The blood-brain barrier (BBB) plays a crucial role in protecting brain health by acting as a barrier between the brain and blood vessels. This barrier also presents challenges for delivering peptide drugs to brain targets. There is a pressing need for computational methods to accurately predict the permeability of peptides across the BBB. However, existing approaches face challenges due to limited real experimentally data and incomplete molecular information within peptide sequences. In this paper, we introduce MultiB3Pred, a multi-view deep learning method designed to address these challenges. Our method makes three key contributions. Firstly, we employ a effective amino acid replacement strategy for data augmentation. Secondly, We utilize sequence embeddings from a biologically pretrained model ProtT5 [1], further refined by a Transformer to capture dependencies on our specific dataset, leading to better sequence representations for the sequence predictor. Lastly, we derive SMILES from the sequences and train a novel SMILES learner. Precisely, the physicochemical properties of the molecules with the graph representation captured by the graph neural network from the molecular graphs are integrated through multilayer perceptron. The predicted probabilities from two sub-predictors are averaged to obtain the final result. Experiments demonstrate that MultiB3Pred achieves state-of-the-art accuracy and Matthews correlation coefficient of 94.4% and 89.9% respectively, showcasing its excellent performance in predicting blood-brain barrier penetration. At the same time, the stability of the model is confirmed by the good results of the 5-fold crossover experiment.