A3 Book chapter

Negative Image-Based Rescoring: Using Cavity Information to Improve Docking Screening




List of Authors: Pentikäinen Olli T., Postila Pekka A.

Publisher: Humana Press Inc.

Publication year: 2021

Book title *: Protein-Ligand Interactions and Drug Design

Journal name in source: Methods in Molecular Biology

Title of series: Methods in Molecular Biology

Volume number: 2266

ISBN: 978-1-0716-1208-8

eISBN: 978-1-0716-1209-5

DOI: http://dx.doi.org/10.1007/978-1-0716-1209-5_8


Abstract

Molecular docking produces often lackluster results in real-life virtual screening assays that aim to discover novel drug candidates or hit compounds. The problem lies in the inability of the default docking scoring to properly estimate the Gibbs free energy of binding, which impairs the recognition of the best binding poses and the separation of active ligands from inactive compounds. Negative image-based rescoring (R-NiB) provides both effective and efficient way for re-ranking the outputted flexible docking poses to improve the virtual screening yield. Importantly, R-NiB has been shown to work with multiple genuine drug targets and six popular docking algorithms using demanding benchmark test sets. The effectiveness of the R-NiB methodology relies on the shape/electrostatics similarity between the target protein’s ligand-binding cavity and the docked ligand poses. In this chapter, the R-NiB method is described with practical usability in mind.


Last updated on 2021-24-06 at 11:57