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- Kexin Huang
- Harvard University , Boston, MA 02115, USA
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- Tianfan Fu
- Georgia Institute of Technology , Atlanta, GA 30332, USA
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- Lucas M Glass
- IQVIA , Cambridge, MA 02139, USA
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- Marinka Zitnik
- Harvard University , Boston, MA 02115, USA
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- Cao Xiao
- IQVIA , Cambridge, MA 02139, USA
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- Jimeng Sun
- University of Illinois at Urbana-Champaign , Urbana, IL 61801, USA
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- Jonathan Wren
- editor
説明
<jats:title>Abstract</jats:title> <jats:sec> <jats:title>Summary</jats:title> <jats:p>Accurate prediction of drug–target interactions (DTI) is crucial for drug discovery. Recently, deep learning (DL) models for show promising performance for DTI prediction. However, these models can be difficult to use for both computer scientists entering the biomedical field and bioinformaticians with limited DL experience. We present DeepPurpose, a comprehensive and easy-to-use DL library for DTI prediction. DeepPurpose supports training of customized DTI prediction models by implementing 15 compound and protein encoders and over 50 neural architectures, along with providing many other useful features. We demonstrate state-of-the-art performance of DeepPurpose on several benchmark datasets.</jats:p> </jats:sec> <jats:sec> <jats:title>Availability and implementation</jats:title> <jats:p>https://github.com/kexinhuang12345/DeepPurpose.</jats:p> </jats:sec> <jats:sec> <jats:title>Supplementary information</jats:title> <jats:p>Supplementary data are available at Bioinformatics online.</jats:p> </jats:sec>
収録刊行物
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- Bioinformatics
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Bioinformatics 36 (22-23), 5545-5547, 2020-12-01
Oxford University Press (OUP)