1 citations · 1 across the 6 of their papers we have counts for
6 papers
Bayes-PD: Exploring a Sequence to Binding Bayesian Neural Network model trained on Phage Display data
Ilann Amiaud-Plachy, Michael Blank, Oliver Bent +1
Phage display is a powerful laboratory technique used to study the interactions between proteins and other molecules, whether other proteins, peptides, DNA or RNA. The under-utilis…
GeoGraph: Geometric and Graph-based Ensemble Descriptors for Intrinsically Disordered Proteins
Eoin Quinn, Marco Carobene, Jean Quentin +3
While deep learning has revolutionized the prediction of rigid protein structures, modelling the conformational ensembles of Intrinsically Disordered Proteins (IDPs) remains a key…
Universally applicable and tunable graph-based coarse-graining for Machine learning force fields
Christoph Brunken, Sebastien Boyer, Mustafa Omar +7
Coarse-grained (CG) force field methods for molecular systems are a crucial tool to simulate large biological macromolecules and are therefore essential for characterisations of bi…
Generative Model for Small Molecules with Latent Space RL Fine-Tuning to Protein Targets
Ulrich A. Mbou Sob, Qiulin Li, Miguel Arbesú +3
A specific challenge with deep learning approaches for molecule generation is generating both syntactically valid and chemically plausible molecular string representations. To addr…
Protein binding affinity prediction under multiple substitutions applying eGNNs on Residue and Atomic graphs combined with Language model information: eGRAL
Arturo Fiorellini-Bernardis, Sebastien Boyer, Christoph Brunken +4
Protein-protein interactions (PPIs) play a crucial role in numerous biological processes. Developing methods that predict binding affinity changes under substitution mutations is f…
Predicting protein stability changes under multiple amino acid substitutions using equivariant graph neural networks
Sebastien Boyer, Sam Money-Kyrle, Oliver Bent
The accurate prediction of changes in protein stability under multiple amino acid substitutions is essential for realising true in-silico protein re-design. To this purpose, we pro…