Feature-Driven Prediction of HOMO-LUMO Gaps in Transition-Metal Complexes Using the SLEET Model: A SMILES-Based Transformer Framework

A feature-driven model, SLEET, built upon the early reported SchNet-bs-RAN framework, that combines the approaches of SchNet and the bond-step representation weighted by the reduced atom number, is reported for evaluating the molecular electronic structure properties of transition-metal complexes (TMCs). Ligands were derived by segmenting purely two-dimensional SMILES representations, and metal-ligand interactions were modeled by using a Transformer-like architecture to construct a property prediction framework that aligns closely with chemical knowledge. This approach effectively captures the characteristics of the ligand field within TMCs. Consequently, the SLEET model delivers precise HOMO-LUMO gap predictions comparable to those achieved by three-dimensional information-based models while also demonstrating strong performance in predicting the molecular-weight-independent electronic properties.