Machine learning approaches for predicting compounds that interact with therapeutic and ADMET related proteins

10.1002/jps.20985

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Bibliographic Details
Main Authors:	Li, H., Yap, C.W., Ung, C.Y., Xue, Y., Li, Z.R., Han, L.Y., Lin, H.H., Chen, Y.Z.
Other Authors:	PHARMACY
Format:	Review
Published:	2014
Subjects:	Computer aided drug design High throughput technologies In silico modeling Neural networks Pharmacokinetic/pharmacodynamic models
Online Access:	http://scholarbank.nus.edu.sg/handle/10635/114479
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Institution:	National University of Singapore

id	sg-nus-scholar.10635-114479
record_format	dspace
spelling	sg-nus-scholar.10635-1144792023-10-30T09:23:07Z Machine learning approaches for predicting compounds that interact with therapeutic and ADMET related proteins Li, H. Yap, C.W. Ung, C.Y. Xue, Y. Li, Z.R. Han, L.Y. Lin, H.H. Chen, Y.Z. PHARMACY COMPUTATIONAL SCIENCE SINGAPORE-MIT ALLIANCE Computer aided drug design High throughput technologies In silico modeling Neural networks Pharmacokinetic/pharmacodynamic models 10.1002/jps.20985 Journal of Pharmaceutical Sciences 96 11 2838-2860 JPMSA 2014-12-02T06:54:15Z 2014-12-02T06:54:15Z 2007-11 Review Li, H., Yap, C.W., Ung, C.Y., Xue, Y., Li, Z.R., Han, L.Y., Lin, H.H., Chen, Y.Z. (2007-11). Machine learning approaches for predicting compounds that interact with therapeutic and ADMET related proteins. Journal of Pharmaceutical Sciences 96 (11) : 2838-2860. ScholarBank@NUS Repository. https://doi.org/10.1002/jps.20985 00223549 http://scholarbank.nus.edu.sg/handle/10635/114479 000250618700002 Scopus
institution	National University of Singapore
building	NUS Library
continent	Asia
country	Singapore Singapore
content_provider	NUS Library
collection	ScholarBank@NUS
topic	Computer aided drug design High throughput technologies In silico modeling Neural networks Pharmacokinetic/pharmacodynamic models
spellingShingle	Computer aided drug design High throughput technologies In silico modeling Neural networks Pharmacokinetic/pharmacodynamic models Li, H. Yap, C.W. Ung, C.Y. Xue, Y. Li, Z.R. Han, L.Y. Lin, H.H. Chen, Y.Z. Machine learning approaches for predicting compounds that interact with therapeutic and ADMET related proteins
description	10.1002/jps.20985
author2	PHARMACY
author_facet	PHARMACY Li, H. Yap, C.W. Ung, C.Y. Xue, Y. Li, Z.R. Han, L.Y. Lin, H.H. Chen, Y.Z.
format	Review
author	Li, H. Yap, C.W. Ung, C.Y. Xue, Y. Li, Z.R. Han, L.Y. Lin, H.H. Chen, Y.Z.
author_sort	Li, H.
title	Machine learning approaches for predicting compounds that interact with therapeutic and ADMET related proteins
title_short	Machine learning approaches for predicting compounds that interact with therapeutic and ADMET related proteins
title_full	Machine learning approaches for predicting compounds that interact with therapeutic and ADMET related proteins
title_fullStr	Machine learning approaches for predicting compounds that interact with therapeutic and ADMET related proteins
title_full_unstemmed	Machine learning approaches for predicting compounds that interact with therapeutic and ADMET related proteins
title_sort	machine learning approaches for predicting compounds that interact with therapeutic and admet related proteins
publishDate	2014
url	http://scholarbank.nus.edu.sg/handle/10635/114479
_version_	1781789286848266240

Machine learning approaches for predicting compounds that interact with therapeutic and ADMET related proteins

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