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Luis Müller

Alumni

Publications

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
Michael Craig
Michał Koziarski
Jiarui Lu
Zhaocheng Zhu
Cristian Gabellini
Kerstin Klasers
Josef Dean
Cas Wognum … (voir 15 de plus)
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 shown significant advancements in multiple fields. However, the lack of datasets with labeled f… (voir plus)eatures and codebases has hindered the development of a supervised foundation model for molecular tasks. Here, we have carefully curated seven datasets specifically tailored for node- and graph-level prediction tasks to facilitate supervised learning on molecules. Moreover, to support the development of multi-task learning on our proposed datasets, we created the Graphium graph machine learning library. Our dataset collection encompasses two distinct categories. Firstly, the TOYMIX category modifies three small existing datasets with additional data for multi-task learning. Secondly, the LARGEMIX category includes four large-scale datasets with 344M graph-level data points and 409M node-level data points from ∼5M unique molecules. Finally, the ultra-large dataset contains 2,210M graph-level data points and 2,031M node-level data points coming from 86M molecules. Hence our datasets represent an order of magnitude increase in data volume compared to other 2D-GNN datasets. In addition, recognizing that molecule-related tasks often span multiple levels, we have designed our library to explicitly support multi-tasking, offering a diverse range of multi-level representations, i.e., representations at the graph, node, edge, and node-pair level. We equipped the library with an extensive collection of models and features to cover different levels of molecule analysis. By combining our curated datasets with this versatile library, we aim to accelerate the development of molecule foundation models. Datasets and code are available at https://github.com/datamol-io/graphium.
2024-01-15
ICLR.cc/2024/Conference (poster)
doi.org
openreview.net