Geometric Graph Convolutional Neural Networks. - Przemyslaw Spurek; Tomasz Danel; Jacek Tabor; Marek Smieja; Lukasz Struski; Agnieszka Slowik; Lukasz Maziarka (CoRR, 2019)

Processing of Incomplete Images by (Graph) Convolutional Neural Networks. - Tomasz Danel; Marek Smieja; Lukasz Struski; Przemyslaw Spurek; Lukasz Maziarka (ICONIP (2), 2020)

Spatial Graph Convolutional Networks. - Tomasz Danel; Przemyslaw Spurek; Jacek Tabor; Marek Smieja; Lukasz Struski; Agnieszka Slowik; Lukasz Maziarka (ICONIP (5), 2020)

Molecule Attention Transformer. - Lukasz Maziarka; Tomasz Danel; Slawomir Mucha; Krzysztof Rataj; Jacek Tabor; Stanislaw Jastrzebski (CoRR, 2020)

We should at least be able to Design Molecules that Dock Well. - Tobiasz Cieplinski; Tomasz Danel; Sabina Podlewska; Stanislaw Jastrzebski (CoRR, 2020)

Comparison of Atom Representations in Graph Neural Networks for Molecular Property Prediction. - Agnieszka Pocha; Tomasz Danel; Lukasz Maziarka (CoRR, 2020)

Mol-CycleGAN: a generative model for molecular optimization. - Lukasz Maziarka; Agnieszka Pocha; Jan Kaczmarczyk; Krzysztof Rataj; Tomasz Danel; Michal Warchol (J. Cheminformatics, 2020)

Multitask Learning Using BERT with Task-Embedded Attention. - Lukasz Maziarka; Tomasz Danel (IJCNN, 2021)

Comparison of Atom Representations in Graph Neural Networks for Molecular Property Prediction. - Agnieszka Pocha; Tomasz Danel; Sabina Podlewska; Jacek Tabor; Lukasz Maziarka (IJCNN, 2021)

SONG: Self-Organizing Neural Graphs. - Lukasz Struski; Tomasz Danel; Marek Smieja; Jacek Tabor; Bartosz Zielinski 001 (CoRR, 2021)

HuggingMolecules: An Open-Source Library for Transformer-Based Molecular Property Prediction (Student Abstract). - Piotr Gainski; Lukasz Maziarka; Tomasz Danel; Stanislaw Jastrzebski (AAAI, 2022)

ProGReST: Prototypical Graph Regression Soft Trees for Molecular Property Prediction. - Dawid Rymarczyk; Daniel Dobrowolski; Tomasz Danel (SDM, 2023)

SONGs: Self-Organizing Neural Graphs. - Lukasz Struski; Tomasz Danel; Marek Smieja; Jacek Tabor; Bartosz Zielinski 001 (WACV, 2023)

Docking-based generative approaches in the search for new drug candidates. - Tomasz Danel; Jan Leski; Sabina Podlewska; Igor T. Podolak (CoRR, 2023)

Generative Models Should at Least Be Able to Design Molecules That Dock Well: A New Benchmark. - Tobiasz Cieplinski; Tomasz Danel; Sabina Podlewska; Stanislaw Jastrzebski (J. Chem. Inf. Model., 2023)

Extended study on atomic featurization in graph neural networks for molecular property prediction. - Agnieszka Wojtuch; Tomasz Danel; Sabina Podlewska; Lukasz Maziarka (J. Cheminformatics, 2023)

KinDEL: DNA-Encoded Library Dataset for Kinase Inhibitors. - Benson Chen; Tomasz Danel; Patrick J. McEnaney; Nikhil Jain; Kirill Novikov; Spurti Umesh Akki; Joshua L. Turnbull; Virja Atul Pandya; Boris P. Belotserkovskii; Jared Bryce Weaver; Ankita Biswas; Dat Nguyen; et al. (CoRR, 2024)

Feature-Based Interpolation and Geodesics in the Latent Spaces of Generative Models. - Lukasz Struski; Michal Sadowski; Tomasz Danel; Jacek Tabor; Igor T. Podolak (IEEE Trans. Neural Networks Learn. Syst., 2024)

Relative molecule self-attention transformer. - Lukasz Maziarka; Dawid Majchrowski; Tomasz Danel; Piotr Gainski; Jacek Tabor; Igor T. Podolak; Pawel M. Morkisz; Stanislaw Jastrzebski (J. Cheminformatics, 2024)

Explainable Artificial Intelligence for Bioactivity Prediction: Unveiling the Challenges with Curated CDK2/4/6 Breast Cancer Dataset. - Adam Sulek; Jakub Klimczak; Jakub Jonczyk; Tomasz Kosciolek; Tomasz Danel; Barbara Pucelik (ICCS (1), 2025)

Qsarna: An Online Tool for Smart Chemical Space Navigation in Drug Design. - Marcin Cieslak; Jan Leski; Olga Krzysztynska-Kuleta; Justyna Kalinowska-Tluscik; Tomasz Danel (J. Chem. Inf. Model., 2025)

PROFIS: Design of Target-Focused Libraries by Probing Continuous Fingerprint Space with Recurrent Neural Networks. - Hubert Rybka; Tomasz Danel; Sabina Podlewska (J. Chem. Inf. Model., 2025)

B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data. - Magdalena Proszewska; Tomasz Danel; Dawid Rymarczyk (CoRR, 2025)

Fragment-Wise Interpretability in Graph Neural Networks via Molecule Decomposition and Contribution Analysis. - Sebastian Musial; Bartosz Zielinski 002; Tomasz Danel (CoRR, 2025)