Publications de Michael Bronstein

Decoding surface fingerprints for protein-ligand interactions

Small molecules have been the preferred modality for drug development and therapeutic interventions. This molecular format presents a number of advantages, e.g. long half-lives and cell permeability, making it possible to access a wide range of therapeutic ...

2024

A new age in protein design empowered by deep learning

Bruno Emanuel Ferreira De Sousa Correia, Michael Bronstein, Casper Alexander Goverde, Ilia Igashov, Arne Schneuing, Hamed Khakzad

The rapid progress in the field of deep learning has had a significant impact on protein design. Deep learning methods have recently produced a breakthrough in protein structure prediction, leading to the availability of high-quality models for millions of ...

Cell Press2023

De novo design of protein interactions with learned surface fingerprints

Henning Paul-Julius Stahlberg, Didier Trono, Priscilla Turelli, Charlène Mireille Raymonde Raclot, Sandrine Madeleine Suzanne Georgeon, Beat Fierz, Aaron Simone Petruzzella, Anthony Marchand, Bruno Emanuel Ferreira De Sousa Correia, Elisa Oricchio, Zander Harteveld, Pablo Gainza Cirauqui, Freyr Sverrisson, Andreas Scheck, Sarah Wehrle, Stéphane Rosset, Alexandra Krina Van Hall-Beauvais, Alexandra Teslenko, Jane Marsden, Michael Bronstein, Casper Alexander Goverde, Stephen Michael Buckley, Dongchun Ni, Martin Pacesa

Physical interactions between proteins are essential for most biological processes governing life. However, the molecular determinants of such interactions have been challenging to understand, even as genomic, proteomic and structural data increase. This k ...

2023

Systems and methods for de novo design of protein interactions with learned surface fingerprints

Anthony Marchand, Zander Harteveld, Pablo Gainza Cirauqui, Freyr Sverrisson, Andreas Scheck, Sarah Wehrle, Alexandra Krina Van Hall-Beauvais, Michael Bronstein

The present application relates to a computer-implemented systems and methods for protein interaction design using surface fingerprints. The method comprises predicting at least one target interface site with high binding propensity, wherein, optionally, t ...

2023

Partition and Code: learning how to compress graphs

Andreas Loukas, Georgios Bouritsas, Nikolaos Karalias, Michael Bronstein

Can we use machine learning to compress graph data? The absence of ordering in graphs poses a significant challenge to conventional compression algorithms, limiting their attainable gains as well as their ability to discover relevant patterns. On the other ...

2021

Graph Signal Processing for Machine Learning: A Review and New Perspectives

Pascal Frossard, Ntorina Thanou, Xiaowen Dong, Laura Toni, Michael Bronstein

The effective representation, processing, analysis, and visualization of large-scale structured data, especially those related to complex domains, such as networks and graphs, are one of the key questions in modern machine learning. Graph signal processing ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning

Bruno Emanuel Ferreira De Sousa Correia, Pablo Gainza Cirauqui, Freyr Sverrisson, Michael Bronstein

Predicting interactions between proteins and other biomolecules solely based on structure remains a challenge in biology. A high-level representation of protein structure, the molecular surface, displays patterns of chemical and geometric features that fin ...

2019

Decrypting interaction fingerprints in protein molecular surfaces

Bruno Emanuel Ferreira De Sousa Correia, Pablo Gainza Cirauqui, Freyr Sverrisson, Michael Bronstein

SPRINGER2019

Geometric deep learning: going beyond Euclidean data

Pierre Vandergheynst, Michael Bronstein

Many signal processing problems involve data whose underlying structure is non-Euclidean, but may be modeled as a manifold or (combinatorial) graph. For instance, in social networks, the characteristics of users can be modeled as signals on the vertices of ...

2017

Robust Principal Component Analysis on Graphs

Pierre Vandergheynst, Xavier Bresson, Vasilis Kalofolias, Nauman Shahid, Michael Bronstein

Principal Component Analysis (PCA) is the most widely used tool for linear dimensionality reduction and clustering. Still it is highly sensitive to outliers and does not scale well with respect to the number of data samples. Robust PCA solves the first iss ...

2015

Learning class-specific descriptors for deformable shapes using localized spectral convolutional networks

Pierre Vandergheynst, Michael Bronstein

In this paper, we propose a generalization of convolutional neural networks (CNN) to non-Euclidean domains for the analysis of deformable shapes. Our construction is based on localized frequency analysis (a generalization of the windowed Fourier transform ...

Wiley-Blackwell2015

Matrix Completion on Graphs

Pierre Vandergheynst, Xavier Bresson, Vasilis Kalofolias, Michael Bronstein

The problem of finding the missing values of a matrix given a few of its entries, called matrix completion, has gathered a lot of attention in the recent years. Al- though the problem under the standard low rank assumption is NP-hard, Cande`s and Recht sho ...

2014