Ligand-Target Prediction Using Winnow and Naive Bayesian Algorithms and the Implications of Overall Performance Statistics-Reference-Cited by-同舟云学术

Ligand-Target Prediction Using Winnow and Naive Bayesian Algorithms and the Implications of Overall Performance Statistics

Published:2008-12-04 Issue:12 Volume:48 Page:2313-2325
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Nigsch Florian¹,Bender Andreas¹,Jenkins Jeremy L.¹,Mitchell John B. O.¹

Affiliation:

1. Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom; Lead Discovery Informatics, Center for Proteomic Chemistry, Novartis Institutes for BioMedical Research, 250 Massachusetts Avenue, Cambridge, Massachusetts 02139; and Division of Medicinal Chemistry, Leiden/Amsterdam Center for Drug Research, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands

Publisher

American Chemical Society (ACS)

Subject

Library and Information Sciences,Computer Science Applications,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/ci800079x

Reference36 articles.

1. New Methods for Ligand-Based Virtual Screening: Use of Data Fusion and Machine Learning to Enhance the Effectiveness of Similarity Searching

2. Relating protein pharmacology by ligand chemistry

3. Why do we need so many chemical similarity search methods?

5. 4D-Fingerprints, Universal QSAR and QSPR Descriptors

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