Accelerating Drug Discovery Using Convolution Neural Network Based Active Learning
Refereed conference paper presented and published in conference proceedings


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AbstractDrug discovery is an expensive and time consuming process, especially in the era of new technology, such as personalized medicine where tremendous experiments and analysis are needed before bringing new drugs to the market. While In vivo and In vitro experiments are expensive, In silico methods become important and they can reduce the cost in drug discovery by prioritizing the experiments in more efficient ways. In this paper, we propose a new convolution neural network based active learning model which helps to reduce the number of experiments needed in drug discovery. Using the drugs performance on other cell lines as assisting information, our model can precisely select the most promising drug from those candidates for a new cell line. Our model uses a deep neural network structure where there are two CNN channels for drugs and cell lines respectively, which are followed by a fulled connected network. The experimental results show that our model can achieve significant better performance than the existing methods.
All Author(s) ListLIU Pengfei, LEUNG Kwong Sak
Name of ConferenceTENCON, IEEE Region 10 International Conference
Start Date of Conference28/10/2018
End Date of Conference31/10/2018
Place of ConferenceKorea
Country/Region of ConferenceSouth Korea
Proceedings TitlePROCEEDINGS OF TENCON 2018 - 2018 IEEE REGION 10 CONFERENCE
Year2018
Month10
Day28
PublisherIEEE
Pages2005 - 2010
ISBN978-1-5386-5457-6
ISSN2159-3442
LanguagesEnglish-United Kingdom
KeywordsDrug Discovery, Convolution Neural Network, Active Learning

Last updated on 2020-08-07 at 01:31