Towards Interpretable Sparse Graph Representation Learning with Laplacian Pooling. - Emmanuel Noutahi; Dominique Beaini; Julien Horwood; Prudencio Tossou (CoRR, 2019)

Adaptive Deep Kernel Learning. - Prudencio Tossou; Basile Dura; François Laviolette; Mario Marchand; Alexandre Lacoste (CoRR, 2019)

Geodesics in fibered latent spaces: A geometric approach to learning correspondences between conditions. - Tariq Daouda; Reda Chhaibi; Prudencio Tossou; Alexandra-Chloé Villani (CoRR, 2020)

Applying PySCMGroup to Breast Cancer Biomarkers Discovery. - Mazid Abiodoun Osseni; Prudencio Tossou; Jacques Corbeil; François Laviolette (BIOINFORMATICS, 2021)

Rethinking Graph Transformers with Spectral Attention. - Devin Kreuzer; Dominique Beaini; William L. Hamilton; Vincent Létourneau; Prudencio Tossou (NeurIPS, 2021)

On the robustness of generalization of drug-drug interaction models. - Rogia Kpanou; Mazid Abiodoun Osseni; Prudencio Tossou; François Laviolette; Jacques Corbeil (BMC Bioinform., 2021)

3D Infomax improves GNNs for Molecular Property Prediction. - Hannes Stärk; Dominique Beaini; Gabriele Corso; Prudencio Tossou; Christian Dallago; Stephan Günnemann; Pietro Lió (ICML, 2022)

MOT: A Multi-Omics Transformer for Multiclass Classification Tumour Types Predictions. - Mazid Abiodoun Osseni; Prudencio Tossou; François Laviolette; Jacques Corbeil (BIOINFORMATICS, 2023)

Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets. - 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; et al. (CoRR, 2023)

Gotta be SAFE: A New Framework for Molecular Design. - Emmanuel Noutahi; Cristian Gabellini; Michael Craig; Jonathan S. C. Lim; Prudencio Tossou (CoRR, 2023)

Role of Structural and Conformational Diversity for Machine Learning Potentials. - Nikhil Shenoy; Prudencio Tossou; Emmanuel Noutahi; Hadrien Mary; Dominique Beaini; Jiarui Ding (CoRR, 2023)

Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets. - 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; et al. (ICLR, 2024)

OpenQDC: Open Quantum Data Commons. - Cristian Gabellini; Nikhil Shenoy; Stephan Thaler; Semih Cantürk; Daniel McNeela; Dominique Beaini; Michael M. Bronstein; Prudencio Tossou (CoRR, 2024)

Implicit Delta Learning of High Fidelity Neural Network Potentials. - Stephan Thaler; Cristian Gabellini; Nikhil Shenoy; Prudencio Tossou (CoRR, 2024)

Real-World Molecular Out-Of-Distribution: Specification and Investigation. - Prudencio Tossou; Cas Wognum; Michael Craig; Hadrien Mary; Emmanuel Noutahi (J. Chem. Inf. Model., 2024)

Virtual Cells: Predict, Explain, Discover. - Emmanuel Noutahi; Jason S. Hartford; Prudencio Tossou; Shawn Whitfield; Alisandra K. Denton; Cas Wognum; Kristina Ulicna; Michael Craig; Jonathan Hsu; Michael Cuccarese; Emmanuel Bengio; Dominique Beaini; et al. (CoRR, 2025)

Torsional-GFN: a conditional conformation generator for small molecules. - Alexandra Volokhova; Léna Néhale Ezzine; Piotr Gainski; Luca Scimeca; Emmanuel Bengio; Prudencio Tossou; Yoshua Bengio; Alex Hernández-García (CoRR, 2025)