A 3D-QSAR-driven approach to binding mode and affinity prediction

Paolo Tosco; Thomas Balle

doi:10.1021/ci200411s

A 3D-QSAR-driven approach to binding mode and affinity prediction

Paolo Tosco, Thomas Balle

Research output: Contribution to journal › Journal article › Research › peer-review

33 Citations (Scopus)

Abstract

A method for predicting the binding mode of a series of ligands is proposed. The procedure relies on three-dimensional quantitative structure-activity relationships (3D-QSAR) and does not require structural knowledge of the binding site. Candidate alignments are automatically built and ranked according to a consensus scoring function. 3D-QSAR analysis based on the selected binding mode enables affinity prediction of new drug candidates having less than 10 rotatable bonds.

Original language	English
Journal	Journal of Chemical Information and Modeling
Volume	52
Issue number	2
Pages (from-to)	302-307
ISSN	1549-9596
DOIs	https://doi.org/10.1021/ci200411s
Publication status	Published - 2012

Keywords

Former Faculty of Pharmaceutical Sciences

Access to Document

10.1021/ci200411s

Cite this

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abstract = "A method for predicting the binding mode of a series of ligands is proposed. The procedure relies on three-dimensional quantitative structure-activity relationships (3D-QSAR) and does not require structural knowledge of the binding site. Candidate alignments are automatically built and ranked according to a consensus scoring function. 3D-QSAR analysis based on the selected binding mode enables affinity prediction of new drug candidates having less than 10 rotatable bonds.",

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AB - A method for predicting the binding mode of a series of ligands is proposed. The procedure relies on three-dimensional quantitative structure-activity relationships (3D-QSAR) and does not require structural knowledge of the binding site. Candidate alignments are automatically built and ranked according to a consensus scoring function. 3D-QSAR analysis based on the selected binding mode enables affinity prediction of new drug candidates having less than 10 rotatable bonds.

KW - Former Faculty of Pharmaceutical Sciences

U2 - 10.1021/ci200411s

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JO - Journal of Chemical Information and Modeling

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SN - 1549-9596

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