tmGNN-XAI: An Explainable Graph Neural Network Tool for Predicting Electronic Properties of Transition Metal...

Predicting the electronic properties of transition metal complexes (TMCs) from 2D molecular graphs remains challenging; organic-trained property models lack TMC transferability, universal interatomic potentials require 3D coordinates rather than SMILES, and tools providing holistic electronic property prediction with atom-level explainability and calibrated uncertainty remain limited. We present tmGNN-XAI, a multitask relational graph convolutional network that predicts seven quantum-chemical properties of TMCs directly from SMILES strings and produces perturbation-based atom-level attributions for each prediction. The model encodes dative coordination bonds as a dedicated edge type distinct from covalent bonds and is trained on 100,703 complexes from the tmQM data set spanning 30 transition metals. Test-set performance is competitive with a Chemprop D-MPNN baseline, achieving R² = 0.979 for metal partial charge and R² = 0.964 and 0.949 for HOMO and LUMO energies. Across all 100,703 complexes, donor atoms (N, O, S, P) appear among the top-five most important atoms in more than 99.8% of complexes for every property, a large-scale data-driven result consistent with ligand field theory. A trust framework combining ensemble agreement with attribution direction separates predictions into four reliability scenarios; confident predictions achieve 1.6 to 2.5 times lower mean absolute error than uncertain ones for five of seven properties. The framework generalizes to cross-level DFT validation, phototherapy candidate screening (area under the ROC curve (AUC) = 0.735), and indirect redox prediction via Koopmans' theorem. An interactive web application makes property predictions, atom-level attributions, and trust labels accessible without programming or DFT expertise. tmGNN-XAI is designed as an explainable, first-tier screening tool for TMC electronic property estimation.