arXiv Machine Learning
GraphNOSE: A Graph Transformer in Olfaction
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arXiv Machine Learning
Aug 27
A General-Purpose Molecular Foundation Model Transfers Across Diverse Olfactory Tasks
The study fine‑tunes the Uni‑Mol2 molecular foundation model on the GS‑LF benchmark for multi‑label odor descriptor prediction. The resulting model matches or surpasses state‑of‑the‑art baselines on the primary benchmark and successfully transfers to four downstream olfactory tasks—including cross‑dataset prediction, odorless vs. odorous classification, enantiomer evaluation, and odor mixture discriminability—without further deep‑learning training. The enantiomer analysis demonstrates that 3D molecular representations can distinguish mirror‑image molecules, a capability lacking in 2D graph models, though predicting stereochemistry’s perceptual effects remains unresolved.
arXiv Machine Learning
Jul 3
An Additive MLP-GNN Framework for Characterizing Chemical and Structural Contributions to Aqueous Solubility
arXiv:2607. 02212v1 Announce Type: cross Abstract: Aqueous solubility is a key property in early-stage drug discovery, but most predictive models merge physicochemical descriptors and molecular graph information into a single representation, obscuring whether a prediction is driven by global chemistry, molecular structure, or both.
arXiv Machine Learning
Jun 5
End-to-End Subgraph Detection with GraphDETR
arXiv:2606. 06364v1 Announce Type: new Abstract: Subgraph detection seeks to identify whether and where instances of query patterns occur within a larger graph.
arXiv Machine Learning
Jun 19
MolGraphBench: A Benchmark of GNN Architectures for Molecular Regression Tasks
arXiv:2602. 20573v3 Announce Type: replace Abstract: Molecules are often represented as SMILES strings, which can be readily converted to hand-crafted descriptors or fingerprints (FP) for molecular property prediction.
arXiv AI
Aug 12
BiScale-GTR: Fragment-Aware Graph Transformers for Multi-Scale Molecular Representation Learning
arXiv:2604. 06336v2 Announce Type: replace-cross Abstract: Fragment-level representations provide a natural way to capture recurring molecular substructures and reuse their learned representations across molecules.
arXiv Machine Learning
Jun 11
GLACIER: A Multimodal Student-Teacher Foundation Model for Molecular Property Prediction
arXiv:2606. 11382v1 Announce Type: new Abstract: Deep learning models facilitate the discovery of molecules with tailored properties among billions of candidate compounds.