arXiv Machine Learning June 19, 2026 By Rajan, Ishaan Gupta

MolGraphBench: A Benchmark of GNN Architectures for Molecular Regression Tasks

Read the original on arXiv Machine Learning →

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.

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arXiv Machine Learning

Jun 2

OgBench: A Framework for Evaluating Graph Neural Networks on Omics Data

arXiv:2605. 15511v2 Announce Type: replace Abstract: Graph Neural Networks (GNNs) have become the dominant framework for inductive graph-level learning.

By Louisa Cornelis, Johan Mathe, Louis Van Langendonck, Guillermo Bern\'ardez, Nina Miolane

benchmarks

arXiv Machine Learning

Jul 30

Examining the Efficacy of Graph Neural Network Message-Passing in Regression Contexts

arXiv:2607. 26404v1 Announce Type: new Abstract: Graph Neural Networks (GNN) facilitate effective prediction on graph data such as molecules, media networks and neural network blueprints.

By Keith G. Mills, Aedan J. DeFrates, Joong Ho Kim

benchmarks

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

Sep 10

GraphNOSE: A Graph Transformer in Olfaction

arXiv:2609.05694v1 Announce Type: new Abstract: Predicting olfactory qualities from molecular structure is an open problem in chemoinformatics. Although linear models can link molecular features to o...

By Mrityunjay Sharma, Sarabeshwar Balaji, Valentina Parma, Ritesh Kumar

llms rag benchmarks

arXiv Machine Learning

Aug 6

Geometry-Informed Parameter-Efficient Fine-Tuning of Pre-trained Molecular GNNs for Blood-Brain Barrier Permeability Prediction

arXiv:2608. 04257v1 Announce Type: new Abstract: Blood-brain barrier permeability (BBBP) prediction is a critical screening task in central nervous system drug discovery, where candidate molecules must be assessed for whether they can cross, or should be prevented from crossing, the blood-brain barrier.

By Marco Vieto Vega, Long D. Nguyen, Binh P. Nguyen

fine-tuning

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.

By Dexiong Chen, Till Hendrik Schulz, Karsten Borgwardt

llms computer-vision

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OgBench: A Framework for Evaluating Graph Neural Networks on Omics Data

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