3 citations · 3 across the 2 of their papers we have counts for
6 papers
Relative Molecule Self-Attention Transformer
Łukasz Maziarka, Dawid Majchrowski, Tomasz Danel +5
Self-supervised learning holds promise to revolutionize molecule property prediction - a central task to drug discovery and many more industries - by enabling data efficient learni…
Comparison of Atom Representations in Graph Neural Networks for Molecular Property Prediction
Agnieszka Pocha, Tomasz Danel, Łukasz Maziarka
Graph neural networks have recently become a standard method for analysing chemical compounds. In the field of molecular property prediction, the emphasis is now put on designing n…
Processing of incomplete images by (graph) convolutional neural networks
Tomasz Danel, Marek Śmieja, Łukasz Struski +2
We investigate the problem of training neural networks from incomplete images without replacing missing values. For this purpose, we first represent an image as a graph, in which m…
Molecule Attention Transformer
Łukasz Maziarka, Tomasz Danel, Sławomir Mucha +3
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…
Spatial Graph Convolutional Networks
Tomasz Danel, Przemysław Spurek, Jacek Tabor +4
Graph Convolutional Networks (GCNs) have recently become the primary choice for learning from graph-structured data, superseding hash fingerprints in representing chemical compound…
Mol-CycleGAN - a generative model for molecular optimization
Łukasz Maziarka, Agnieszka Pocha, Jan Kaczmarczyk +2
Designing a molecule with desired properties is one of the biggest challenges in drug development, as it requires optimization of chemical compound structures with respect to many…