Meeko: Molecule Parametrization and Software Interoperability for Docking and Beyond

Molecule parametrization is an essential requirement to guarantee the accuracy of docking calculations. Parametrization includes a proper perception of chemical properties such as bonds, formal charges and protonation states. This includes large biological macromolecules, such as proteins and nucleic acids, and small molecules, such as ligands and cofactors. The structures of proteins and nucleic acids are challenging due to omission of several atoms from the structural model, and from the lack of connectivity and bond order information in the PDB and mmCIF file formats. For small molecules, the very large chemical diversity poses challenges for both validating correctness and providing accurate parameters. These challenges affect various modeling approaches like molecular docking and molecular dynamics. Moreover, several specialized methods (particularly in molecular docking) leverage specific chemical properties to add custom potentials, pseudoatoms, or manipulate atomic connectivity. To address these challenges, we developed Meeko, a molecular parametrization Python package that leverages the widely used RDKit cheminformatics library for a chemically accurate description of the molecular representation. Small molecules are modeled as single RDKit molecules, and biological macromolecules as multiple RDKit molecules, one for each residue. Meeko is highly customizable and designed to be easily scriptable for high-throughput processing, replacing MGLTools for receptor and ligand preparation.

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Publication Details

Journal
Journal of Chemical Information and Modeling
Published
2025-12-11
DOI
https://doi.org/10.1021/acs.jcim.5c02271
Citations
3
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
4.51
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article

Meeko: Molecule Parametrization and Software Interoperability for Docking and Beyond

Joani Mato, Althea Hansel-Harris, Niccolò Bruciaferri, Yiran He et al.
3 citations
Journal of Chemical Information and Modeling
Computational Drug Discovery Methods
4.51
article

Meeko: Molecule Parametrization and Software Interoperability for Docking and Beyond

Joani Mato, Althea Hansel-Harris, Niccolò Bruciaferri, Yiran He, Allison Barkdull, Parnika Sharma, Andreas F. Tillack, Ishan Taneja, Stefano Forli, Diogo Santos‐Martins, Giulia Bianco, Manuel A. Llanos, Matthew Holcomb, May‐Linn Paulsen, Jérôme Eberhardt
article en
3 citations

Abstract

Molecule parametrization is an essential requirement to guarantee the accuracy of docking calculations. Parametrization includes a proper perception of chemical properties such as bonds, formal charges and protonation states. This includes large biological macromolecules, such as proteins and nucleic acids, and small molecules, such as ligands and cofactors. The structures of proteins and nucleic acids are challenging due to omission of several atoms from the structural model, and from the lack of connectivity and bond order information in the PDB and mmCIF file formats. For small molecules, the very large chemical diversity poses challenges for both validating correctness and providing accurate parameters. These challenges affect various modeling approaches like molecular docking and molecular dynamics. Moreover, several specialized methods (particularly in molecular docking) leverage specific chemical properties to add custom potentials, pseudoatoms, or manipulate atomic connectivity. To address these challenges, we developed Meeko, a molecular parametrization Python package that leverages the widely used RDKit cheminformatics library for a chemically accurate description of the molecular representation. Small molecules are modeled as single RDKit molecules, and biological macromolecules as multiple RDKit molecules, one for each residue. Meeko is highly customizable and designed to be easily scriptable for high-throughput processing, replacing MGLTools for receptor and ligand preparation.

Journal of Chemical Information and ModelingVol. 65(24)
Scripps Research Institute (US)
Openalex Percentile: Top 5%
Computational Drug Discovery Methods
4.51
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