arXiv Machine Learning
MolLedger: An Additive Graph Neural Network with Chemically Grounded ADME Attributions
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arXiv Machine Learning
Oct 22
Fragment-Level Graph Encoding for Interpretable Molecular Property Prediction
The paper introduces a fragment‑level graph encoding for molecular property prediction, using BRICS decomposition to represent molecules as graphs of synthetically meaningful fragments. This change of input representation allows any message‑passing GNN and post‑hoc attribution method to produce explanations at the functional‑group level, matching atom‑level accuracy on smaller graphs. Experiments across multiple architectures, tasks, and explainers show that fragment‑level encoding recovers known pharmacophores more reliably than post‑hoc aggregation of atom‑level explanations.
arXiv Machine Learning
Sep 30
Benchmarking graph-based models for in-silico toxicity prediction in drug discovery
arXiv:2609.37555v1 Announce Type: new Abstract: Drug discovery is a costly and high-risk process, where toxicity-related failures remain a major cause of attrition in both preclinical and clinical st...
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
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B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data
The paper introduces B-XAIC, a benchmark for evaluating Explainable AI methods on Graph Neural Networks using real-world chemical data. It addresses shortcomings of previous frameworks that rely on artificial datasets and lack direct links to explanation faithfulness. By providing diverse tasks with known ground-truth rationales, B-XAIC enables a comprehensive assessment of XAI methods in the molecular domain.
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
Jul 10
path_boost: A Python Package for Interpretable Graph-Level Prediction using Path-Based Gradient Boosting
arXiv:2607. 07935v1 Announce Type: cross Abstract: We present path_boost, a Python package for interpretable supervised learning on graph-structured input data.