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Predicting molecular activity on nuclear receptors by multitask neural networks

The interest in multitask and deep learning strategies has been increasing in the last few years, in application to large and complex dataset for quantitative structure‐activity relationship (QSAR) analysis. Multitask approaches allow the simultaneous prediction of molecular properties that are related, through information sharing, whereas deep learning strategies increase the potential of capturing nonlinear relationships. In this work, we compare the binary classification capability of multitask deep and shallow neural networks to single‐task strategies used as benchmark (i.e., as k‐nearest neighbours, N‐nearest neighbours, random forest and Naïve Bayes), as well as multitask supervised self‐organizing maps.