Prudencio Tossou

Virtual Cells: Predict, Explain, Discover

Emmanuel Noutahi

Jason Hartford

Shawn Whitfield

Ali Denton

Cas Wognum

Kristina Ulicna

Jonathan Hsu

Michael Cuccarese

Emmanuel Bengio

Christopher Gibson

Daniel Cohen

Berton Earnshaw

2025-05-20

ArXiv (preprint)

arxiv.org

Virtual Cells: Predict, Explain, Discover

Emmanuel Noutahi

Jason Hartford

Shawn Whitfield

Ali Denton

Cas Wognum

Kristina Ulicna

Michael Craig

Jonathan Hsu

Michael Cuccarese

Emmanuel Bengio

Christopher Gibson

Daniel Cohen

Berton Earnshaw

2025-05-01

arXiv (published)

arxiv.org

Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets

Shenyang Huang

Joao Alex Cunha

Zhiyi Li

Gabriela Moisescu-Pareja

Oleksandr Dymov

Samuel Maddrell-Mander

Callum McLean

Jama Hussein Mohamud

Michael Craig

Cristian Gabellini

Kerstin Klaser

Josef Dean

Cas Wognum … (see 15 more)

Maciej Sypetkowski

Ioannis Koutis

Hadrien Mary

Therence Bois

Andrew William Fitzgibbon

Blazej Banaszewski

Chad Martin

Dominic Masters

Recently, pre-trained foundation models have enabled significant advancements in multiple fields. In molecular machine learning, however, wh… (see more)ere datasets are often hand-curated, and hence typically small, the lack of datasets with labeled features, and codebases to manage those datasets, has hindered the development of foundation models. In this work, we present seven novel datasets categorized by size into three distinct categories: ToyMix, LargeMix and UltraLarge. These datasets push the boundaries in both the scale and the diversity of supervised labels for molecular learning. They cover nearly 100 million molecules and over 3000 sparsely defined tasks, totaling more than 13 billion individual labels of both quantum and biological nature. In comparison, our datasets contain 300 times more data points than the widely used OGB-LSC PCQM4Mv2 dataset, and 13 times more than the quantum-only QM1B dataset. In addition, to support the development of foundational models based on our proposed datasets, we present the Graphium graph machine learning library which simplifies the process of building and training molecular machine learning models for multi-task and multi-level molecular datasets. Finally, we present a range of baseline results as a starting point of multi-task and multi-level training on these datasets. Empirically, we observe that performance on low-resource biological datasets show improvement by also training on large amounts of quantum data. This indicates that there may be potential in multi-task and multi-level training of a foundation model and fine-tuning it to resource-constrained downstream tasks. The Graphium library is publicly available on Github and the dataset links are available in Part 1 and Part 2.

2024-01-16

ICLR.cc/2024/Conference (poster)

Role of Structural and Conformational Diversity for Machine Learning Potentials

Nikhil Shenoy

Emmanuel Noutahi

Hadrien Mary

Jiarui Ding

In the field of Machine Learning Interatomic Potentials (MLIPs), understanding the intricate relationship between data biases, specifically … (see more)conformational and structural diversity, and model generalization is critical in improving the quality of Quantum Mechanics (QM) data generation efforts. We investigate these dynamics through two distinct experiments: a fixed budget one, where the dataset size remains constant, and a fixed molecular set one, which focuses on fixed structural diversity while varying conformational diversity. Our results reveal nuanced patterns in generalization metrics. Notably, for optimal structural and conformational generalization, a careful balance between structural and conformational diversity is required, but existing QM datasets do not meet that trade-off. Additionally, our results highlight the limitation of the MLIP models at generalizing beyond their training distribution, emphasizing the importance of defining applicability domain during model deployment. These findings provide valuable insights and guidelines for QM data generation efforts.

2023-10-27

NeurIPS.cc/2023/Workshop/AI4Science (oral)

MOT: A Multi-Omics Transformer for Multiclass Classification Tumour Types Predictions

Mazid Osseni

Franccois Laviolette

Jacques Corbeil

2023-02-16

Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (published)

3D Infomax improves GNNs for Molecular Property Prediction

Hannes Stärk

Gabriele Corso

Christian Dallago

Stephan Günnemann

Pietro Lio

Molecular property prediction is one of the fastest-growing applications of deep learning with critical real-world impacts. Including 3D mol… (see more)ecular structure as input to learned models improves their predictions for many molecular properties. However, this information is infeasible to compute at the scale required by most real-world applications. We propose pre-training a model to understand the geometry of molecules given only their 2D molecular graph. Using methods from self-supervised learning, we maximize the mutual information between a 3D summary vector and the representations of a Graph Neural Network (GNN) such that they contain latent 3D information. During fine-tuning on molecules with unknown geometry, the GNN still generates implicit 3D information and can use it to inform downstream tasks. We show that 3D pre-training provides significant improvements for a wide range of molecular properties, such as a 22% average MAE reduction on eight quantum mechanical properties. Crucially, the learned representations can be effectively transferred between datasets with vastly different molecules.

2022-06-28

Proceedings of the 39th International Conference on Machine Learning (published)

proceedings.mlr.press

Rethinking Graph Transformers with Spectral Attention

William L. Hamilton

In recent years, the Transformer architecture has proven to be very successful in sequence processing, but its application to other data str… (see more)uctures, such as graphs, has remained limited due to the difficulty of properly defining positions. Here, we present the