New Pharmacophore Fingerprints and Weight‐matrix Learning for Virtual Screening. Application to Bcr‐Abl Data. Issue 1 (4th November 2022)

• Record Type: Journal Article
• Authors: Rehioui, Hajar; Cuissart, Bertrand; Ouali, Abdelkader; Lepailleur, Alban; Lamotte, Jean‐Luc; Bureau, Ronan; Zimmermann, Albrecht
• Is Part Of: Molecular informatics. Volume 42:Issue 1(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-11-04
• Subjects: Pharmacophore -- Molecular fingerprint -- Feed Forward Neural Network -- Clustering; Cheminformatics -- Periodicals; QSAR (Biochemistry) -- Periodicals; Structure-activity relationships (Biochemistry) -- Periodicals; Drugs -- Structure-activity relationships -- Periodicals; 615.19
• DOI: 10.1002/minf.202200210
• Languages: English
• ISSNs: 1868-1743

Abstract

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 :