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molli: A General Purpose Python Toolkit for Combinatorial Small Molecule Library Generation, Manipulation, and

Alexander S Shved1, Blake E Ocampo1, Elena S Burlova1

  • 1Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.

Journal of Chemical Information and Modeling
|October 23, 2024
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Summary
This summary is machine-generated.

We developed "molli," a Python toolkit for managing large in silico molecular libraries. This cheminformatics module streamlines the creation and analysis of 3D libraries from 2D structures, aiding chemical research.

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Area of Science:

  • Computational Chemistry
  • Cheminformatics
  • Molecular Modeling

Background:

  • Managing large in silico molecular libraries is crucial for modern chemistry research.
  • Existing tools may lack streamlined interfaces for complex library manipulation and analysis.

Purpose of the Study:

  • Introduce the MOLecular LIibrary toolkit (molli), a Python 3 cheminformatics module.
  • Provide a streamlined interface for constructing, managing, and analyzing large in silico molecular libraries.
  • Facilitate high-fidelity 3D library generation from 2D chemical structures.

Main Methods:

  • Developed a Python 3 module (molli) for in silico library manipulation.
  • Integrated with computational chemistry programs (OpenBabel, RDKit, ORCA, NWChem, xTB/CREST) for geometry optimization, property calculation, and conformer generation.
  • Implemented conformer-dependent grid-based feature calculators and 3D visualization interfaces.

Main Results:

  • Enabled expansion of 3D combinatorial libraries from 2D CDXML fragments with high stereochemical fidelity.
  • Facilitated numerical representation and visualization of thousands of library members.
  • Demonstrated workflows for descriptor calculation on catalyst libraries and NMR chemical shift prediction.

Conclusions:

  • Molli offers a powerful and streamlined approach to in silico molecular library analysis.
  • The toolkit enhances human understanding of complex molecular libraries through integrated visualization.
  • Molli supports diverse cheminformatics workflows, from descriptor calculation to property prediction.