WEECFP-SuRGE: A Position-Aware Substructure Encoding Method for Molecular Property Prediction
Computational molecular property prediction requires representations that capture local chemistry, long-range interactions, and molecular topology. Conventional fingerprints provide efficient local substructure features, whereas learned graph and sequence models can represent broader context but often rely on pretraining or three-dimensional conformers. We introduce Wide Encoded Extended Connectivity Fingerprints (WEECFP) with Substructure Rotary Graph-distance Encoding (SuRGE), a tokenized hierarchical Morgan representation in which graph-distance-dependent rotations are applied at the input and within transformer self-attention. Across MoleculeNet and the Therapeutic Data Commons ADMET ...
| Authors | Robert Epps |
|---|---|
| Primary category | cs.LG |
| Published | 4 September 2026 |
| Last updated at source | 25 September 2026 |
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