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molSimplify 2.0: Improved Structure Generation for Automating Discovery in Inorganic Molecular and Reticular

Gianmarco G Terrones1, Roland G St Michel1,2, Jacob W Toney1

  • 1Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.

Journal of Chemical Information and Modeling
|February 25, 2026
PubMed
Summary
This summary is machine-generated.

The molSimplify software has been updated for automated molecular and materials modeling. New features improve transition metal complex generation and enable high-throughput de novo design for various materials.

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Area of Science:

  • Computational Chemistry
  • Materials Science
  • Cheminformatics

Background:

  • Automated molecular modeling requires efficient tools for handling complex chemical structures.
  • Generating transition metal complexes (TMCs) with high denticity and avoiding steric clashes is challenging.
  • Extending modeling capabilities to periodic systems and metalloenzymes is crucial for broader applications.

Purpose of the Study:

  • To provide an overview of molSimplify's core functionalities and recent enhancements.
  • To introduce new classes and improved algorithms for molecular and materials modeling.
  • To demonstrate the utility of molSimplify for de novo generation of TMCs and other complex systems.

Main Methods:

  • Description of mol3D and atom3D classes for storing and manipulating atomic/bonding information.
  • Introduction of mol2D class for graph-based uniqueness and substructure identification.
  • Enhancements to decoration and substructure addition for systematic molecular derivatization.
  • Improvements in transition metal complex generation, including steric clash elimination and high denticity ligand handling.
  • Integration with machine learning models for predicting coordinating atom identities.
  • Application of protein3D class for metalloenzyme modeling.
  • Demonstration using a workflow for generating Ir complexes from SMILES strings.

Main Results:

  • Enhanced capabilities for reading, modifying, and characterizing molecular geometries.
  • Systematic derivatization of template molecules through improved decoration and substructure functions.
  • Successful generation of transition metal complexes (TMCs) with higher denticity and without steric clashes.
  • High-throughput, de novo TMC generation enabled by machine learning integration.
  • Demonstrated applications in periodic systems (MOFs) and metalloenzymes.
  • Validation of the workflow by accurately generating structures of known Ir complexes.

Conclusions:

  • Recent enhancements to molSimplify significantly improve automated molecular and materials modeling.
  • The updated code facilitates high-throughput de novo generation of TMCs and related structures.
  • molSimplify is extendable to various periodic materials like MOFs, COFs, and zeolites, as well as multimetallic TMCs.