Is Multitask Deep Learning Practical for Pharma?

Multitask deep learning has emerged as a powerful tool for computational drug discovery. However, despite a number of preliminary studies, multitask deep networks have yet to be widely deployed in the pharmaceutical and biotech industries. This lack of acceptance stems from both software difficultie...

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Veröffentlicht in:Journal of chemical information and modeling Jg. 57; H. 8; S. 2068
Hauptverfasser: Ramsundar, Bharath, Liu, Bowen, Wu, Zhenqin, Verras, Andreas, Tudor, Matthew, Sheridan, Robert P, Pande, Vijay
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States 28.08.2017
Schlagworte:
Absorption, Radiation
Drug Discovery - methods
Inhibitory Concentration 50
Machine Learning
Protein Kinase Inhibitors - chemistry
Protein Kinase Inhibitors - pharmacology
Serine Proteinase Inhibitors - chemistry
Serine Proteinase Inhibitors - pharmacology
Software
Ultraviolet Rays
ISSN:1549-960X, 1549-960X
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Zusammenfassung:Multitask deep learning has emerged as a powerful tool for computational drug discovery. However, despite a number of preliminary studies, multitask deep networks have yet to be widely deployed in the pharmaceutical and biotech industries. This lack of acceptance stems from both software difficulties and lack of understanding of the robustness of multitask deep networks. Our work aims to resolve both of these barriers to adoption. We introduce a high-quality open-source implementation of multitask deep networks as part of the DeepChem open-source platform. Our implementation enables simple python scripts to construct, fit, and evaluate sophisticated deep models. We use our implementation to analyze the performance of multitask deep networks and related deep models on four collections of pharmaceutical data (three of which have not previously been analyzed in the literature). We split these data sets into train/valid/test using time and neighbor splits to test multitask deep learning performance under challenging conditions. Our results demonstrate that multitask deep networks are surprisingly robust and can offer strong improvement over random forests. Our analysis and open-source implementation in DeepChem provide an argument that multitask deep networks are ready for widespread use in commercial drug discovery.
Bibliographie:ObjectType-Article-1
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ISSN:1549-960X
1549-960X
DOI:10.1021/acs.jcim.7b00146