Publications by Daniel Probst | EPFL Graph Search

Boosting Protein Graph Representations through Static-Dynamic Fusion

Pierre Vandergheynst, Bruno Emanuel Ferreira De Sousa Correia, Daniel Probst

Machine learning for protein modeling faces significant challenges due to proteins' inherently dynamic nature, yet most graph-based machine learning methods rely solely on static structural information. Recently, the growing availability of molecular dynam ...

2025

Molecular set representation learning

Pierre Vandergheynst, Maria Boulougouri, Daniel Probst

Computational representation of molecules can take many forms, including graphs, string encodings of graphs, binary vectors or learned embeddings in the form of real-valued vectors. These representations are then used in downstream classification and regre ...

Springer Science and Business Media LLC2024

Learning on compressed molecular representations

Daniel Probst

Last year, a preprint gained notoriety, proposing that a k-nearest neighbour classifier is able to outperform large-language models using compressed text as input and normalised compression distance (NCD) as a metric. In chemistry and biochemistry, molecul ...

ROYAL SOC CHEMISTRY2024

An explainability framework for deep learning on chemical reactions exemplified by enzyme-catalysed reaction classification

Daniel Probst

Assigning or proposing a catalysing enzyme given a chemical or biochemical reaction is of great interest to life sciences and chemistry alike. The exploration and design of metabolic pathways and the challenge of finding more sustainable enzyme-catalysed a ...

2023

Aiming beyond slight increases in accuracy

Daniel Probst

Owing to the diminishing returns of deep learning and the focus on model accuracy, machine learning for chemistry might become an endeavour exclusive to well-funded institutions and industry. Extending the focus to model efficiency and interpretability wil ...

NATURE PORTFOLIO2023

The Societal and Scientific Importance of Inclusivity, Diversity, and Equity in Machine Learning for Chemistry

Daniel Probst

While the introduction of practical deep learning has driven progress across scientific fields, recent research highlighted that the requirement of deep learning for ever-increasing computational resources and data has potential negative impacts on the sci ...

2023

Alchemical analysis of FDA approved drugs

Philippe Schwaller, Daniel Probst, Jean-Louis Reymond, Markus Orsi

Chemical space maps help visualize similarities within molecular sets. However, there are many different molecular similarity measures resulting in a confusing number of possible comparisons. To overcome this limitation, we exploit the fact that tools desi ...

2023