TMC-LewisGen: Automated Generation of Lewis-like Structures for Transition–Metal Complexes

Abstract

Abstract Lewis structures constitute a foundational language in chemical education, mechanistic reasoning, synthesis, and modern cheminformatics. With the accelerating shift toward data-driven and AI-assisted molecular science, the ability to rapidly generate Lewis-like representations, including RDKit-parsable SMILES, has become essential for large-scale analysis and predictive modeling. While mature tools exist for organic molecules, transition-metal complexes (TMCs) remain systematically underserved owing to their inherently diverse coordination patterns. To address this gap, we develop TMC-LewisGen, an automated tool for generating Lewis-like structures of TMCs directly from Cartesian coordinates. The task is formulated as a discrete variable optimization problem, where a comprehensive set of explicit chemical rules defines the feasible solution space and integer linear programming (ILP) identifies a globally optimal assignment of bond orders, lone pairs, and formal charges, followed by postprocessing corrections. The final outputs include Lewis-like structures, RDKit-parsable SMILES, and interpretive bonding reports framed within the covalent bond classification (CBC) formalism, in which ligands are categorized as L-, X-, or Z- type donors. Benchmarking on nearly 29,000 TMCs demonstrates robust performance, with valid SMILES generated for 100% of the structures and subsecond ILP solution times in most cases. Representative examples, including Werner-type complexes, η-coordinated π-ligands, and metal–carbene motifs, demonstrate broad applicability. The framework supports large-scale structural annotation, automated chemical interpretation, and data-driven modeling of TMCs. A freely accessible web server for 3D TMC analysis and visualization is available at https://tmc-lewis-structure.streamlit.app/.