MVGNet: Prediction of PI3K Inhibitors Using Multitask Learning and Multiview Frameworks

Abstract

PI3K (phosphatidylinositol 3-kinase) is an intracellular phosphatidylinositol kinase composed of a regulatory subunit, p85, and a catalytic subunit, p110. Based on the different structures of the p110 catalytic subunit, PI3K can be divided into four isoforms: PI3Kα, PI3Kβ, PI3Kγ, and PI3Kδ. As molecularly targeted drugs, PI3K inhibitors have demonstrated antiproliferative effects on tumor cells and can also induce cancer cell death. In this study, a multiview deep learning framework (MVGNet) is proposed, which integrates fragment-based pharmacophore information and utilizes multitask learning to capture correlation information between subtasks. This framework predicts the inhibitory activity of molecules against the four PI3K isoforms (PI3Kα, PI3Kβ, PI3Kγ, and PI3Kδ). Compared to baseline prediction models based on three traditional machine learning methods (RF, SVM, and XGBoost) and four deep learning algorithms (GAT, D-MPNN, CMPNN, and KANO), our model demonstrates superior performance. The evaluation results show that our model achieves the highest average AUC-ROC and AUC-PR values on the test set, which are 0.927 ± 0.006 and 0.980 ± 0.002, respectively. This study provides a reference for exploring the structure–activity relationship of PI3K inhibitors.