CLEW: The Generation of Pharmacophore Hypotheses Through Machine Learning

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.