Search Results for author: Tomasz Danel

Found 9 papers, 6 papers with code

ProGReST: Prototypical Graph Regression Soft Trees for Molecular Property Prediction

1 code implementation7 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.

Graph Regression Molecular Property Prediction +1

Relative Molecule Self-Attention Transformer

no code implementations12 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.

Drug Discovery Self-Supervised Learning

SONG: Self-Organizing Neural Graphs

no code implementations28 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.

Processing of incomplete images by (graph) convolutional neural networks

no code implementations26 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.


We Should at Least Be Able to Design Molecules That Dock Well

1 code implementation20 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.

Drug Discovery

Molecule Attention Transformer

6 code implementations19 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.

Drug Discovery

Spatial Graph Convolutional Networks

2 code implementations11 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.

Image Classification

Feature-Based Interpolation and Geodesics in the Latent Spaces of Generative Models

1 code implementation6 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.

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