1 citations · 2 across the 5 of their papers we have counts for
6 papers
WOMBAT: Whitebox Oracle for Molecular Benchmarking and Attribution Testing
Dominik Matuszek, Bartosz Zieliński, Tomasz Danel +1
When a graph neural network (GNN) explainer produces an unexpected attribution on a molecule, the attribution alone cannot reveal whether the explainer has failed or the model has…
PhAME: Phenotype-Aware Molecular Editing via Latent Diffusion
Łukasz Janisiów, Sebastian Musiał, Bartosz Zieliński +2
Small-molecule drug discovery requires simultaneous optimization of numerous properties of candidate molecules. These properties can be investigated through the analysis of high-di…
Enhancing Chemical Explainability Through Counterfactual Masking
Łukasz Janisiów, Marek Kochańczyk, Bartosz Zieliński +1
Molecular property prediction is a crucial task that guides the design of new compounds, including drugs and materials. While explainable artificial intelligence methods aim to scr…
Fragment-Wise Interpretability in Graph Neural Networks via Molecule Decomposition and Contribution Analysis
Sebastian Musiał, Bartosz Zieliński, Tomasz Danel
Graph neural networks have demonstrated remarkable success in predicting molecular properties by leveraging the rich structural information encoded in molecular graphs. However, th…
B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data
Magdalena Proszewska, Tomasz Danel, Dawid Rymarczyk
Understanding the reasoning behind deep learning model predictions is crucial in cheminformatics and drug discovery, where molecular design determines their properties. However, cu…
KinDEL: DNA-Encoded Library Dataset for Kinase Inhibitors
Benson Chen, Tomasz Danel, Gabriel H. S. Dreiman +18
DNA-Encoded Libraries (DELs) represent a transformative technology in drug discovery, facilitating the high-throughput exploration of vast chemical spaces. Despite their potential,…