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Article Dans Une Revue Molecular Informatics Année : 2022

New pharmacophore fingerprints and Weight‐Matrix Learning for virtual screening. Application to Bcr‐Abl data

Résumé

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 (SWML_R) leads to clearly superior results in terms of several quality measures.
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Dates et versions

hal-03818211 , version 1 (17-10-2022)

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Citer

Hajar Rehioui, Bertrand Cuissart, Abdelkader Ouali, Alban Lepailleur, Jean-Luc Lamotte, et al.. New pharmacophore fingerprints and Weight‐Matrix Learning for virtual screening. Application to Bcr‐Abl data. Molecular Informatics, 2022, ⟨10.1002/minf.202200210⟩. ⟨hal-03818211⟩
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