Deep Learning-Based Modeling of Drug –Target Interaction Prediction Incorporating Binding Site Information of Proteins

This study aims to predict unknown ligand –target interactions using one-dimensional SMILES as inputs for ligands and binding site residues for proteins in a computationally efficient manner. We first formulate a Deep learning CNN model using one-dimensional SMILES for drugs and motif-rich binding pocket subsequences of proteins as inputs . We evaluate and compare the proposed deep learning model trained on expert-based features against shallow feature-based machine learning methods. The proposed method achieved better or similar performance on the MSE and AUPR metrics than the shallow methods. Additionally, We show that our deep lea rning model, DeepPS is computationally more efficient than the deep learning model trained on full-length raw sequences of proteins. We conclude that a beneficial research approach would be to integrate structural information of proteins for modeling drug-target interaction prediction of large datas ets for more interpretability, high throughput, and broad applicability.Graphical abstract
Source: Interdisciplinary Sciences, Computational Life Sciences - Category: Bioinformatics Source Type: research