CLEW: The Generation of Pharmacophore Hypotheses Through Machine Learning
- 1 January 1998
- journal article
- research article
- Published by Taylor & Francis in SAR and QSAR in Environmental Research
- Vol. 9 (1-2) , 53-81
- https://doi.org/10.1080/10629369808039149
Abstract
The paper describes the program CLEW, which utilizes learning and geometrical fitting to discover pharmacophores from a set of active and inactive compounds. The program first divides the compounds into similar classes. It then utilizes machine learning to derive a set of rules that relate structure to activity for each class. Then it finds the common features among all classes. These common features are used by a geometrical fitting program that tries to a 3D fit between these features between minimized conformations for every active molecule in every class. Such a fit is used to infer a pharmacophore.Keywords
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