Federated learning (FL) is a distributed machine learning framework that is gaining traction
in view of increasing health data privacy protection needs. By conducting a systematic …

Federated Learning enables machine learning across multiple sources of data and
alleviates the risk of leaking private information between partners thereby encouraging …

Federated multipartner machine learning has been touted as an appealing and efficient
method to increase the effective training data volume and thereby the predictivity of models …

To apply federated learning to drug discovery we developed a novel platform in the context
of European Innovative Medicines Initiative (IMI) project MELLODDY (grant n 831472) …

Quantitative structure–activity relationships (QSARs) are invaluable computational tools for
the prediction of the biological effects and physico-chemical properties of molecules. For …

Being able to predict the cell permeability of cyclic peptides is essential for unlocking their
potential as a drug modality for intracellular targets. With a wide range of studies of cell …

In recent years, a new way of doing machine learning called “Federated Deep Learning” has
become quite important. It has the potential to bring big advantages in many areas like smart …

The exploration of transfer effects and selection of useful auxiliary tasks in multitask learning
and foundation models with downstream tasks remain a largely empirical and …

Yves Moreau is a professor of engineering at the University of Leuven in Belgium. Trained
as an engineer, he turned to computational biology at the beginning of the new millennium …

[en] Predictive modelling of antimicrobial activity of molecules is a crucial step towards the
discovery of anti-infective medicines. Unfortunately, there is a shortage of models covering …