Molecular Activity Prediction using Deep Learning Software Library
Information and Computer Science, Toyohashi University of Technology, Toyohashi, Japan
The 2016 International Conference on Advanced Informatics: Concepts, Theory and Application (ICAICTA), 2016
@article{kato2016molecular,
title={Molecular Activity Prediction using Deep Learning Software Library},
author={Kato, Yoshiki and Hamada, Shinji and Goto, Hitoshi},
journal={Evaluation},
volume={2},
number={15},
pages={15},
year={2016}
}
In order to know how work deep learning method in chemoinformatics and bioinformatics problems, we have attempted to predict the molecular activities using the molecular fingerprints (chemical descriptor vectors) provided by the "Merck molecular activity challenge" competition and an open source deep learning library Chainer. Our result has been able to reproduce almost identical increase-decrease tendencies with the correlation Rs 2 of the champion group in the competition. GPU performance was also examined and the speed gain were more than 11 times than only CPU computation.
November 5, 2016 by hgpu
