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Open AccessArticle

In-Silico Molecular Binding Prediction for Human Drug Targets Using Deep Neural Multi-Task Learning

Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon KS015, Korea
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Genes 2019, 10(11), 906; https://doi.org/10.3390/genes10110906
Received: 2 September 2019 / Revised: 28 October 2019 / Accepted: 5 November 2019 / Published: 7 November 2019
In in-silico prediction for molecular binding of human genomes, promising results have been demonstrated by deep neural multi-task learning due to its strength in training tasks with imbalanced data and its ability to avoid over-fitting. Although the interrelation between tasks is known to be important for successful multi-task learning, its adverse effect has been underestimated. In this study, we used molecular interaction data of human targets from ChEMBL to train and test various multi-task and single-task networks and examined the effectiveness of multi-task learning for different compositions of targets. Targets were clustered based on sequence similarity in their binding domains and various target sets from clusters were chosen. By comparing the performance of deep neural architectures for each target set, we found that similarity within a target set is highly important for reliable multi-task learning. For a diverse target set or overall human targets, the performance of multi-task learning was lower than single-task learning, but outperformed single-task for the target set containing similar targets. From this insight, we developed Multiple Partial Multi-Task learning, which is suitable for binding prediction for human drug targets.
Keywords: in-silico bioactivity prediction; virtual screening; multi-task learning; deep learning in-silico bioactivity prediction; virtual screening; multi-task learning; deep learning
MDPI and ACS Style

Lee, K.; Kim, D. In-Silico Molecular Binding Prediction for Human Drug Targets Using Deep Neural Multi-Task Learning. Genes 2019, 10, 906.

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