5 papers
Neural Petri flows for chemical reactions
Jose Eduardo Escrig Molina, Daniel Probst
Petri nets have been used to describe chemical processes such as reactions.They map well to chemistry: Places are the bonds between atoms and the free valence of each atom, a token…
Inductive Graph Layout with Implicit Neural Fields
Berfin Inal, Daniel Probst
A graph layout is normally a table of free coordinates. We optimise a function with a fixed number of parameters instead. This gives a drawing a sample complexity and an extens…
Graph Set Transformer
Jose E. Escrig Molina, Baoquan Chen, Daniel Probst
We introduce the Graph Set Transformer (GST), a neural network architecture for learning on sets of graphs, designed for tasks in which per-element predictions depend on set-wide c…
Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings
Aditya Sengar, Ali Hariri, Daniel Probst +2
Generating diverse, all-atom conformational ensembles of dynamic proteins such as G-protein-coupled receptors (GPCRs) is critical for understanding their function, yet most generat…
Implicit Neural Representations of Molecular Vector-Valued Functions
Jirka Lhotka, Daniel Probst
Molecules have various computational representations, including numerical descriptors, strings, graphs, point clouds, and surfaces. Each representation method enables the applicati…