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arXiv · 1803.06236

Chemi-net: a graph convolutional network for accurate drug property prediction

Ke Liu·Xiangyan Sun·Lei Jia·Jun Ma·Haoming Xing·Junqiu Wu·Hua Gao·Yax Sun·Florian Boulnois·Jie Fan
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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. The results showed that our deep neural network method improved current methods by a large margin. We foresee that the significantly increased accuracy of ADME prediction seen with Chemi-Net over Cubist will greatly accelerate drug discovery.

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Ke Liu, Xiangyan Sun, Lei Jia, Jun Ma, Haoming Xing, Junqiu Wu, Hua Gao, Yax Sun, Florian Boulnois, Jie Fan. 2018-03-16. Chemi-net: a graph convolutional network for accurate drug property prediction. https://arxiv.org/abs/1803.06236

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