Abstract Development of new products often relies on the discovery of novel molecules.
While conventional molecular design involves using human expertise to propose …

Geometric deep learning (GDL) is based on neural network architectures that incorporate
and process symmetry information. GDL bears promise for molecular modelling applications …

Score-based generative models (SGMs) have recently demonstrated impressive results in
terms of both sample quality and distribution coverage. However, they are usually applied …

The estimation of chemical reaction properties such as activation energies, rates, or yields is
a central topic of computational chemistry. In contrast to molecular properties, where …

Characterizing uncertainty in machine learning models has recently gained interest in the
context of machine learning reliability, robustness, safety, and active learning. Here, we …

The field of bioimage analysis is currently impacted by a profound transformation, driven by
the advancements in imaging technologies and artificial intelligence. The emergence of …

We introduce a method combining variational autoencoders (VAEs) and deep metric
learning to perform Bayesian optimisation (BO) over high-dimensional and structured input …

A central task in computational drug discovery is to construct models from known active
molecules to find further promising molecules for subsequent screening. However, typically …

According to the surveys of the World Health Organization, distracted driving is one of main
causes of road traffic accidents. To improve road traffic safety, real-time detection of drivers' …

In recent years, generative machine learning approaches have attracted significant attention
as an enabling approach for designing novel molecular materials with minimal design bias …