Daniel Probst

Mapping the Space of Chemical Reactions Using Attention-Based Neural Networks

Organic reactions are usually assigned to classes containing reactions with similar reagents and mechanisms. Reaction classes facilitate the communication of complex concepts and efficient...

Visualization of Very Large High-Dimensional Data Sets as Minimum Spanning Trees

The chemical sciences are producing an unprecedented amount of large, high-dimensional data sets containing chemical structures and associated properties. However, there are currently no...

Social and environmental impact of recent developments in machine learning on biology and chemistry research

Potential societal and environmental effects such as the rapidly increasing resource use and the associated environmental impact, reproducibility issues, and exclusivity, the privatization of ML...

Application of an automated machine learning-genetic algorithm (AutoML-GA) coupled with computational fluid dynamics simulations for rapid engine design optimization

In recent years, the use of machine learning-based surrogate models for computational fluid dynamics (CFD) simulations has emerged as a promising technique for reducing the computational cost...

Neural Petri flows for chemical reactions

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 is a unit of...

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings

An Early-Warning System to Support Activities in the Management of Customer Equity and How to Obtain the Most from Spatial Customer Equity Potentials

Boosting Protein Graph Representations through Static-Dynamic Fusion

Inductive Graph Layout with Implicit Neural Fields

A graph layout is normally a table of $N$ free coordinates. We optimise a function with a fixed number of parameters instead. This gives a drawing a sample complexity and an extensible domain....

FloDR: An invertible dimensionality reduction method based on a normalising flow

It is common for two-dimensional embeddings of high-dimensional data to be read far beyond what they can support. Distances in and between clusters, the meaning behind empty spaces, and the amount...

Graph Set Transformer

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 context as well...

Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddings

Generating diverse, all-atom conformational ensembles of dynamic proteins such as G-protein-coupled receptors (GPCRs) is critical for understanding their function, yet most generative models...

To Bin or not to Bin: Alternative Representations of Mass Spectra

Mass spectrometry, especially so-called tandem mass spectrometry, is commonly used to assess the chemical diversity of samples. The resulting mass fragmentation spectra are representations of...

Implicit Neural Representations of Molecular Vector-Valued Functions

Molecules have various computational representations, including numerical descriptors, strings, graphs, point clouds, and surfaces. Each representation method enables the application of various...