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

We provide an overview of core molSimplify code functionality
and recent updates that enhance its capabilities for automated molecular
and materials modeling. We describe the mol3D and atom3D classes, which store atomic and
bonding information for a wide range of functions, including reading,
modifying, and characterizing molecular geometries from common file
formats. Enhancements to decoration and substructure addition functions
enable the systematic derivatization of template molecules. We introduce
a new mol2D class that enables graph-based
uniqueness checks and substructure identification. Most importantly,
we introduce improvements to transition metal complex (TMC) generation
that eliminate steric clashes and enable structure building with ligands
of higher denticity. Integration with machine learning models that
predict coordinating atom identities enables truly high-throughput, de novo TMC generation. We describe applications of molSimplify
outside of isolated TMCs, including extensions to periodic systems
(e.g., metal–organic frameworks) and to metalloenzymes through
the protein3D class. We demonstrate our improved
combined structure prediction and generation workflow by generating
structures of a database of experimentally characterized Ir complexes
from only the SMILES strings of their respective ligands. We envision
that recent enhancements will make the code easily extendable to other
periodic materials, such as covalent organic frameworks and zeolites,
or to multimetallic TMCs.