arXiv Machine Learning
MolGA: Molecular Graph Adaptation with Pre-trained 2D Graph Encoder
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arXiv AI
Jun 3
Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining
arXiv:2509. 22468v2 Announce Type: replace-cross Abstract: High-quality molecular representations are essential for property prediction and molecular design, yet large labeled datasets remain scarce.
arXiv Machine Learning
Sep 29
WEECFP-SuRGE: A Position-Aware Substructure Encoding Method for Molecular Property Prediction
WEECFP-SuRGE introduces a position‑aware substructure encoding method that combines tokenized hierarchical Morgan fingerprints with graph‑distance‑dependent rotations applied at the input and within transformer self‑attention. The approach captures local chemistry, long‑range interactions, and molecular topology without requiring external pretraining or 3‑D conformer generation. Benchmarks on MoleculeNet and the Therapeutic Data Commons ADMET datasets show competitive performance, and a reconstruction procedure correctly identifies constitutional isomers for 92.6% of a 4,200‑molecule library.
arXiv AI
Jul 3
MolSight: A Graph-Aware Vision-Language Model for Unified Chemical Image Understanding
arXiv:2607. 01982v1 Announce Type: cross Abstract: Using molecular large language models (LLMs) as a unified framework for understanding molecular structures and functions is emerging as a new trend in tasks such as molecular design and drug discovery.
arXiv AI
Jul 7
Back to Basics: Improving Molecular Understanding in LLMs via SMILES-Graph Translation
arXiv:2607. 03007v1 Announce Type: cross Abstract: Recent advances in molecular large language models have led to strong performance on molecular understanding and generation tasks, yet these gains often come without reliable structural grounding.
arXiv AI
Oct 5
MolWorld: Molecule World Models for Actionable Molecular Optimization
arXiv:2605.08954v2 Announce Type: replace-cross Abstract: Molecular optimization in drug discovery aims to discover molecules with improved target properties, but practical lead optimization often re...
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.