arXiv AI August 12, 2026 By Yi Yang, Ovidiu Daescu

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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.

By Sampreeti Bhattacharya, Arkaprava Roy

rag fine-tuning

arXiv Machine Learning

3d ago

MoTIF-X: A Multimodal Tokenized Framework for Interpretable and Extensible Molecular Representation Learning

arXiv:2609.37384v1 Announce Type: new Abstract: Molecular representation learning is central to computer-aided drug discovery. Molecular graphs, SMILES strings, and 3D conformations provide complemen...

By Linqing Mo, Jiayu Zhou, Bin Chen

fine-tuning multimodal benchmarks safety

arXiv Machine Learning

Jun 9

VQ-Atom: Semantic Discretization of Local Atomic Environments for Molecular Representation Learning

arXiv:2605. 16823v2 Announce Type: replace Abstract: Large language models succeed by combining large-scale pretraining with meaningful discrete tokens.

By Takayuki Kimura

llms nlp efficiency

arXiv AI

Sep 15

Multi-View Molecular Representation Learning with Hierarchical Graphs and Contextualized Fingerprints

arXiv:2609.15611v1 Announce Type: cross Abstract: Molecular property prediction requires representations that generalize from limited labeled data to structurally novel compounds. Existing molecular...

By Gwang-Hyeon Yun, Jong-Hoon Park, Bing Hu, Helen Chen, Anita Layton, Young-Rae Cho

rag fine-tuning benchmarks

arXiv Machine Learning

Aug 26

MolGA: Molecular Graph Adaptation with Pre-trained 2D Graph Encoder

arXiv:2510.07289v2 Announce Type: replace Abstract: Molecular graph representation learning is widely used in chemical and biomedical research. While pre-trained 2D graph encoders have demonstrated s...

By Xingtong Yu, Chang Zhou, Xinming Zhang, Yuan Fang

safety

arXiv Machine Learning

4d ago

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.

By Robert Epps

llms benchmarks

One email a morning, machine-written

Related stories

An Additive MLP-GNN Framework for Characterizing Chemical and Structural Contributions to Aqueous Solubility

MoTIF-X: A Multimodal Tokenized Framework for Interpretable and Extensible Molecular Representation Learning

VQ-Atom: Semantic Discretization of Local Atomic Environments for Molecular Representation Learning

Multi-View Molecular Representation Learning with Hierarchical Graphs and Contextualized Fingerprints

MolGA: Molecular Graph Adaptation with Pre-trained 2D Graph Encoder

WEECFP-SuRGE: A Position-Aware Substructure Encoding Method for Molecular Property Prediction