Multitask machine learning for classifying highly and weakly potent kinase inhibitors

Abstract

Compound activity prediction is a major application of machine learning (ML) in pharmaceutical research. Conventional single-task (ST) learning aims to predict active compounds for a given target. In addition, multitask (MT) learning attempts to simultaneously predict active compounds for multiple targets. The underlying rationale of MT learning is to guide and further improve modeling by exploring and exploiting related prediction tasks. For MT learning, deep neural networks (DNNs) are often used, establishing a link between MT and deep learning. In this work, ST and MT strategies for ML methods including DNN were compared in the systematic prediction of highly potent and weakly potent protein kinase inhibitors. A total of 19 030 inhibitors with activity against 103 human kinases were used for modeling. Given its composition, the data set provided many related prediction tasks. DNN, support vector machine, and random forest ST and MT models were derived and compared. Clear trends emerged. Regardless of the method, MT learning consistently outperformed ST modeling. Overall MT-DNNs achieved the highest prediction accuracy, but advantages over other MT-ML methods were only marginal. Furthermore, feature weights were extracted from models to evaluate correlation between different prediction tasks.