New Pharmacophore Fingerprints and Weight‐matrix Learning for Virtual Screening. Application to Bcr‐Abl Data

Abstract

In this work, we propose to analyze the potential of a new type of pharmacophoric descriptors coupled to a novel feature transformation technique, called Weight‐Matrix Learning (WML, based on a feed‐forward neural network). The application concerns virtual screening on a tyrosine kinase named BCR‐ABL. First, the compounds were described using three different families of descriptors: our new pharmacophoric descriptors, and two circular fingerprints, ECFP4 and FCFP4. Afterwards, each of these original molecular representations were transformed using either an unsupervised WML method or a supervised one. Finally, using these transformed representations, K‐Means clustering algorithm was applied to automatically partition the molecules. Combining our pharmacophoric descriptors with supervised Weight‐Matrix Learning (SWMLR) leads to clearly superior results in terms of several quality measures.