molecular-property-gnn

Molecular Property GNN

Description

This skill covers graph neural networks for molecular property prediction: molecular graph construction from SMILES, SDF, RDKit molecules, and 3D conformers; node and edge featurization; 2D message-passing models; 3D continuous-filter and directional models; validation under scaffold and chemistry shifts; uncertainty estimation; and integration with molecular featurization, surrogate validation, and scientific visualization. Invoke this skill when predicting molecular or small-cluster properties such as solubility, toxicity, HOMO/LUMO gaps, dipoles, reaction barriers, binding affinity, or materials-relevant molecular descriptors from molecular graphs or conformers.

Domain Context

Molecular GNNs encode a molecule as a graph where atoms are nodes and bonds, distances, or neighbor relations are edges. A 2D molecular GNN operates on chemical connectivity: atom types, formal charges, aromaticity, bond orders, stereochemistry, and ring membership. A 3D molecular GNN also uses conformer coordinates and must respect Euclidean symmetry: scalar properties should be invariant to translation and rotation, while vector or tensor properties require equivariant outputs.

The graph is an approximation to the molecular information available at prediction time. A SMILES-derived graph has no explicit electron density, solvent environment, crystal packing, conformer ensemble, or assay protocol. A conformer-derived graph captures geometry, but only for the chosen conformation and coordinate generation workflow. For flexible molecules, properties such as solvation free energy, binding affinity, or dipole moment may depend on an ensemble rather than a single conformer. [EXPERT REVIEW NEEDED]

The validation split often matters more than the architecture. Random splits measure interpolation over molecules similar to those already seen. Scaffold splits, assay splits, time splits, or chemistry-aware splits measure more realistic transfer to new chemotypes, experimental campaigns, or molecular families. Reporting only random-split benchmark metrics is usually insufficient for scientific use.