Novel consensus architecture to improve performance of large-scale multitask deep learning QSAR models

Advances in the development of high-throughput screening and automated chemistry have rapidly accelerated the production of chemical and biological data, much of them freely accessible through literature aggregator services such as ChEMBL and PubChem. Here we explore how to use this comprehensive mapping of chemical biology space to support the development of large-scale quantitative structure-activity relationships (QSAR) models. We propose a new Deep Learning Consensus Architecture (DLCA) that combines consensus and multitask deep learning approaches together to generate large-scale QSAR models. This method improves knowledge transfer across different target/assays while also integrating contributions from models based on different descriptors. The proposed approach was validated and compared with Protochemometrics, Multitask Deep Learning and Random Forest methods paired with various descriptors types. DLCA models demonstrated improved prediction accuracy for both regression and classification tasks. T...