An <scp>ANI</scp>‐2 enabled open‐source protocol to estimate ligand strain after docking

In protein-ligand docking, the score assigned to a protein-ligand complex is approximate. Especially, the internal energy of the ligand is difficult to compute precisely using a molecular mechanics based force-field, introducing significant noise in the rank-ordering of ligands. We propose an open-source protocol (https://github.com/UnixJunkie/MMO), using two quantum mechanics (QM) single point energy calculations, plus a Monte Carlo (Monte Carlo) based ligand minimization procedure in-between, to estimate ligand strain after docking. The MC simulation uses the ANI-2x (QM approximating) force field and is performed in the dihedral space. On some protein targets, using strain filtering after docking allows to significantly improve hit rates. We performed a structure-based virtual screening campaign on nine protein targets from the Laboratoire d'Innovation Thérapeutique-PubChem assays dataset using Cambridge crystallographic data centre genetic optimization for ligand docking. Then, docked ligands were submitted to the strain estimation protocol and the impact on hit rate was analyzed. As for docking, the method does not always work. However, if sufficient active and inactive molecules are known for a given protein target, its efficiency can be evaluated.

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Publication Details

Journal
Journal of Computational Chemistry
Published
2024-10-05
DOI
https://doi.org/10.1002/jcc.27478
Citations
6
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
2.17

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article

An ANI‐2 enabled open‐source protocol to estimate ligand strain after docking

Koji Tsuda, Francois Berenger
6 citations
Journal of Computational Chemistry
Computational Drug Discovery Methods
2.17
article

An ANI‐2 enabled open‐source protocol to estimate ligand strain after docking

Koji Tsuda, Francois Berenger
article en
6 citations

Abstract

In protein-ligand docking, the score assigned to a protein-ligand complex is approximate. Especially, the internal energy of the ligand is difficult to compute precisely using a molecular mechanics based force-field, introducing significant noise in the rank-ordering of ligands. We propose an open-source protocol (https://github.com/UnixJunkie/MMO), using two quantum mechanics (QM) single point energy calculations, plus a Monte Carlo (Monte Carlo) based ligand minimization procedure in-between, to estimate ligand strain after docking. The MC simulation uses the ANI-2x (QM approximating) force field and is performed in the dihedral space. On some protein targets, using strain filtering after docking allows to significantly improve hit rates. We performed a structure-based virtual screening campaign on nine protein targets from the Laboratoire d'Innovation Thérapeutique-PubChem assays dataset using Cambridge crystallographic data centre genetic optimization for ligand docking. Then, docked ligands were submitted to the strain estimation protocol and the impact on hit rate was analyzed. As for docking, the method does not always work. However, if sufficient active and inactive molecules are known for a given protein target, its efficiency can be evaluated.

Journal of Computational ChemistryVol. 46(1)
The University of Tokyo (JP)
Japan Agency for Medical Research and Development
Industry, innovation and infrastructure
Openalex Percentile: Top 11%
Computational Drug Discovery Methods
2.17
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