WOMBAT is a benchmark comprising 14 whitebox graph neural networks (GNNs) whose message‑passing weights are manually set to detect specific SMARTS motifs. Each model’s decision rule is explicitly known, providing a ground truth for attribution that allows researchers to identify and study errors in post‑hoc explainers such as GNNExplainer, PGExplainer, and Integrated Gradients. The authors validate the models on millions of PubChem molecules, demonstrate how Integrated Gradients can be misled to spread attribution, and release the dataset, models, and evaluation code for future XAI tool development.
By Dominik Matuszek, Bartosz Zieli\'nski, Tomasz Danel, Dawid Rymarczyk
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...
By Noel Suarez-Barro, Manuel Lama, Juan C. Vidal
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.
By Rafael Lopes Almeida (School of Pharmacy, University of Eastern Finland, Kuopio, Finland), Ina P\"ohner (School of Pharmacy, University of Eastern Finland, Kuopio, Finland)
arXiv:2506. 13196v5 Announce Type: replace Abstract: Accurate prediction of protein-ligand binding affinity is critical for drug discovery.
By Han Liu, Keyan Ding, Peilin Chen, Yinwei Wei, Liqiang Nie, Dapeng Wu, Shiqi Wang
arXiv:2608.30636v1 Announce Type: new
Abstract: Optimizing absorption, distribution, metabolism, and excretion (ADME) is an important part of small molecule drug discovery. Many machine learning mode...
By Christina X. Ji
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
The paper introduces MPGE, a Multi‑Perspective Graph Explainer that provides three complementary explanations—factual support, counterfactual sensitivity, and exemplar tolerance—for graph neural network predictions on molecular data. It formulates a shared constrained optimization that balances prediction preservation, compactness, and edit cost, and extends CF‑GNNExplainer to learn symmetric edge rankings for counterfactuals. Experiments on several benchmark datasets show that factual masks retain a small fraction of edges while counterfactual and exemplar explanations achieve high coverage, demonstrating the method’s ability to reveal model behavior without claiming chemical validity.
By Mahtab Sarvmaili
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
The paper introduces concept bottlenecks into deep graph generators to enhance interpretability and steerability in molecular graph generation. By embedding human-interpretable concepts, the authors demonstrate improved distributional fidelity (lower FCD) while maintaining validity and uniqueness. They also present a Concept Controller optimization method for steering generation, evaluated within a one-shot molecular graph generation framework.
By Sarabeshwar Balaji, Ritesh Kumar, Lav R. Varshney
arXiv:2606. 30961v1 Announce Type: cross Abstract: Advances in deep learning architectures and representations have enabled ML-driven chemical property prediction, but state-of-the-art (SOTA) models have remained largely confined to independent codebases and lack support for diverse chemical species.
By Jacob W. Toney, Samir Darouich, Yiran Wang, Aaron G. Garrison, Johannes K\"astner, Heather J. Kulik
arXiv:2607. 00931v1 Announce Type: new Abstract: Predicting cancer drug response from transcriptomic profiles is a cornerstone of precision oncology, yet the scientific value of machine learning models hinges not solely on predictive accuracy, but also on their capacity to generate reliable biological insights.
By Martino Ciaperoni, Margherita Lalli, Simone Piaggesi, Martina Varisco, Francesco Carli, Riccardo Guidotti, Dino Pedreschi, Francesco Raimondi, Fosca Giannotti
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