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
relationship (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 proteochemometrics, multitask deep learning, and Random
Forest methods paired with various descriptors types. DLCA models
demonstrated improved prediction accuracy for both regression and
classification tasks. The best models together with their modeling
sets are provided through publicly available web services at https://predictor.ncats.io.