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Chemi-Net: A Molecular Graph Convolutional Network for Accurate Drug Property Prediction.

ABSTRACT: Absorption, distribution, metabolism, and excretion (ADME) studies are critical for drug discovery. Conventionally, these tasks, together with other chemical property predictions, rely on domain-specific feature descriptors, or fingerprints. Following the recent success of neural networks, we developed Chemi-Net, a completely data-driven, domain knowledge-free, deep learning method for ADME property prediction. To compare the relative performance of Chemi-Net with Cubist, one of the popular machine learning programs used by Amgen, a large-scale ADME property prediction study was performed on-site at Amgen. For all 13 data sets, Chemi-Net resulted in higher R² values compared with the Cubist benchmark. The median R² increase rate over Cubist was 26.7%. We expect that the significantly increased accuracy of ADME prediction seen with Chemi-Net over Cubist will greatly accelerate drug discovery.

SUBMITTER: Liu K

PROVIDER: S-EPMC6678642 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Chemi-Net: A Molecular Graph Convolutional Network for Accurate Drug Property Prediction.

Liu Ke K Sun Xiangyan X Jia Lei L Ma Jun J Xing Haoming H Wu Junqiu J Gao Hua H Sun Yax Y Boulnois Florian F Fan Jie J

International journal of molecular sciences 20190710 14

Absorption, distribution, metabolism, and excretion (ADME) studies are critical for drug discovery. Conventionally, these tasks, together with other chemical property predictions, rely on domain-specific feature descriptors, or fingerprints. Following the recent success of neural networks, we developed Chemi-Net, a completely data-driven, domain knowledge-free, deep learning method for ADME property prediction. To compare the relative performance of Chemi-Net with Cubist, one of the popular mach ...[more]

PMID: 31295892

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