no code implementations • 30 Oct 2023 • Nikhil Shenoy, Prudencio Tossou, Emmanuel Noutahi, Hadrien Mary, Dominique Beaini, Jiarui Ding
In the field of Machine Learning Interatomic Potentials (MLIPs), understanding the intricate relationship between data biases, specifically conformational and structural diversity, and model generalization is critical in improving the quality of Quantum Mechanics (QM) data generation efforts.
1 code implementation • 16 Oct 2023 • Emmanuel Noutahi, Cristian Gabellini, Michael Craig, Jonathan S. C Lim, Prudencio Tossou
Traditional molecular string representations, such as SMILES, often pose challenges for AI-driven molecular design due to their non-sequential depiction of molecular substructures.
1 code implementation • 6 Oct 2023 • Dominique Beaini, Shenyang Huang, Joao Alex Cunha, Zhiyi Li, Gabriela Moisescu-Pareja, Oleksandr Dymov, Samuel Maddrell-Mander, Callum McLean, Frederik Wenkel, Luis Müller, Jama Hussein Mohamud, Ali Parviz, Michael Craig, Michał Koziarski, Jiarui Lu, Zhaocheng Zhu, Cristian Gabellini, Kerstin Klaser, Josef Dean, Cas Wognum, Maciej Sypetkowski, Guillaume Rabusseau, Reihaneh Rabbany, Jian Tang, Christopher Morris, Ioannis Koutis, Mirco Ravanelli, Guy Wolf, Prudencio Tossou, Hadrien Mary, Therence Bois, Andrew Fitzgibbon, Błażej Banaszewski, Chad Martin, Dominic Masters
Recently, pre-trained foundation models have enabled significant advancements in multiple fields.
1 code implementation • NeurIPS Workshop AI4Scien 2021 • Hannes Stärk, Dominique Beaini, Gabriele Corso, Prudencio Tossou, Christian Dallago, Stephan Günnemann, Pietro Liò
Molecular property prediction is one of the fastest-growing applications of deep learning with critical real-world impacts.
no code implementations • 29 Sep 2021 • Hannes Stärk, Dominique Beaini, Gabriele Corso, Prudencio Tossou, Christian Dallago, Stephan Günnemann, Pietro Lio
Molecular property prediction is one of the fastest-growing applications of deep learning with critical real-world impacts.
1 code implementation • NeurIPS 2021 • Devin Kreuzer, Dominique Beaini, William L. Hamilton, Vincent Létourneau, Prudencio Tossou
Here, we present the $\textit{Spectral Attention Network}$ (SAN), which uses a learned positional encoding (LPE) that can take advantage of the full Laplacian spectrum to learn the position of each node in a given graph.
1 code implementation • 16 May 2020 • Tariq Daouda, Reda Chhaibi, Prudencio Tossou, Alexandra-Chloé Villani
This work introduces a geometric framework and a novel network architecture for creating correspondences between samples of different conditions.
no code implementations • 25 Sep 2019 • Prudencio Tossou, Basile Dura, Daniel Cohen, Mario Marchand, François Laviolette, Alexandre Lacoste
Due to the significant costs of data generation, many prediction tasks within drug discovery are by nature few-shot regression (FSR) problems, including accurate modelling of biological assays.
no code implementations • 25 Sep 2019 • Emmanuel Noutahi, Dominique Beani, Julien Horwood, Prudencio Tossou
Recent work in graph neural networks (GNNs) has led to improvements in molecular activity and property prediction tasks.
no code implementations • 28 May 2019 • Emmanuel Noutahi, Dominique Beaini, Julien Horwood, Sébastien Giguère, Prudencio Tossou
We benchmark LaPool on molecular graph prediction and understanding tasks and show that it outperforms recent GNNs.
no code implementations • 28 May 2019 • Prudencio Tossou, Basile Dura, Francois Laviolette, Mario Marchand, Alexandre Lacoste
Deep kernel learning provides an elegant and principled framework for combining the structural properties of deep learning algorithms with the flexibility of kernel methods.