Improved graph-based multitask learning model with sparse sharing for quantitative structure-property relationship prediction of drug molecules

The quantitative structure-property relationship (QSPR) is a fundamental technique for evaluating and screening potentially valuable molecules in the field of drug discovery. There is an urgent need to speed up pharmaceutical research and development and a huge chemical space to explore, which necessitate effective and precise computer-aided QSPR modeling methods. Previous studies with various deep learning models are limited because they are trained on separate small datasets, known as the small-sample problem. Using transfer learning, this article describes a sparse sharing method that uses advanced graph-based models to construct an efficient and reasonable multitask learning workflow for QSPR prediction. The proposed workflow is systematically and comprehensively tested with four benchmark datasets containing different targets, and several precisely predicted molecular examples are illustrated. The results demonstrate that an obvious improvement in the prediction of molecular properties is achieved, along with the ability to predict multiple properties simultaneously.