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PhAME: Phenotype-Aware Molecular Editing via Latent Diffusion
Åukasz Janisiów, Sebastian MusiaÅ, Bartosz ZieliÅski +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
Åukasz Janisiów, Marek KochaÅczyk, Bartosz ZieliÅski +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 MusiaÅ, Bartosz ZieliÅski, 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…