New Pharmacophore Fingerprints and Weight‐matrix Learning for Virtual Screening. Application to Bcr‐Abl Data. Issue 1 (4th November 2022) - Rehioui, Hajar; Cuissart, Bertrand; Ouali, Abdelkader; Lepailleur, Alban; Lamotte, Jean‐Luc; Bureau, Ronan; Zimmermann, Albrecht
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. Abstract :