MolGrid: Which Chemistry Do Molecular Models Read?

Molecular property prediction guides which molecules are made and tested, in drug discovery and beyond. Its models read graphs, SMILES strings or 3D structures, which leave relations between atoms, such as how strongly two atoms couple through bonds, for the network to infer. A good score does not tell us which of these relations a model actually relies on. We introduce $\text{MolGrid}$, which states this pairwise chemistry explicitly as a symmetric image over atom pairs, with channels for through-bond coupling, partial charge and stereochemistry. Built from SMILES in milliseconds with no 3D input, it is read unchanged by pretrained vision backbones. Its diagonal holds one value per atom and its off-diagonal one per atom pair, the first two terms of a property's expansion over atoms, which predicts that additive properties read the atoms and delocalised ones the pairs. Because every value has a fixed address, we can shuffle either part or remove a channel and retrain a chemically pretrained backbone, against a control that only relabels the atoms. $\text{MolGrid}$ leads reproduced fingerprint, graph and SMILES-transformer baselines on five of seven MoleculeNet scaffold-split tasks each, and 3D Uni-Mol on solubility. Across nine properties, only the HOMO–LUMO gap reads the pairs, $5.7$ percentage points beyond the control, while solvation reads the charges and permeability stereochemistry. Pretraining improves $25$ of $28$ backbone–task pairs without changing which property reads the pairs.