1 code implementation • 7 Oct 2022 • Dawid Rymarczyk, Daniel Dobrowolski, Tomasz Danel
In this work, we propose the novel Prototypical Graph Regression Self-explainable Trees (ProGReST) model, which combines prototype learning, soft decision trees, and Graph Neural Networks.
no code implementations • 12 Oct 2021 • Łukasz Maziarka, Dawid Majchrowski, Tomasz Danel, Piotr Gaiński, Jacek Tabor, Igor Podolak, Paweł Morkisz, Stanisław Jastrzębski
Self-supervised learning holds promise to revolutionize molecule property prediction - a central task to drug discovery and many more industries - by enabling data efficient learning from scarce experimental data.
no code implementations • 28 Jul 2021 • Łukasz Struski, Tomasz Danel, Marek Śmieja, Jacek Tabor, Bartosz Zieliński
Recent years have seen a surge in research on deep interpretable neural networks with decision trees as one of the most commonly incorporated tools.
1 code implementation • 23 Nov 2020 • Agnieszka Pocha, Tomasz Danel, Łukasz Maziarka
Graph neural networks have recently become a standard method for analysing chemical compounds.
no code implementations • 26 Oct 2020 • Tomasz Danel, Marek Śmieja, Łukasz Struski, Przemysław Spurek, Łukasz Maziarka
We investigate the problem of training neural networks from incomplete images without replacing missing values.
1 code implementation • 20 Jun 2020 • Tobiasz Cieplinski, Tomasz Danel, Sabina Podlewska, Stanislaw Jastrzebski
To close this gap, we propose a benchmark based on docking, a popular computational method for assessing molecule binding to a protein.
6 code implementations • 19 Feb 2020 • Łukasz Maziarka, Tomasz Danel, Sławomir Mucha, Krzysztof Rataj, Jacek Tabor, Stanisław Jastrzębski
Designing a single neural network architecture that performs competitively across a range of molecule property prediction tasks remains largely an open challenge, and its solution may unlock a widespread use of deep learning in the drug discovery industry.
2 code implementations • 11 Sep 2019 • Tomasz Danel, Przemysław Spurek, Jacek Tabor, Marek Śmieja, Łukasz Struski, Agnieszka Słowik, Łukasz Maziarka
Graph Convolutional Networks (GCNs) have recently become the primary choice for learning from graph-structured data, superseding hash fingerprints in representing chemical compounds.
1 code implementation • 6 Apr 2019 • Łukasz Struski, Michał Sadowski, Tomasz Danel, Jacek Tabor, Igor T. Podolak
In the case of geodesics, we search for the curves with the shortest length, while in the case of generative models we typically apply linear interpolation in the latent space.