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

Dominique Beaini · Shenyang(Andy) Huang · Joao 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 · Mirco Ravanellu · Guy Wolf · Prudencio Tossou · Hadrien Mary · Therence Bois · Andrew Fitzgibbon · Blazej Banaszewski · Chad Martin · Dominic Masters

Halle B #5
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Wed 8 May 1:45 a.m. PDT — 3:45 a.m. PDT


Recently, pre-trained foundation models have enabled significant advancements in multiple fields. In molecular machine learning, however, where 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.

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