Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets
Bibliographic record
Abstract
Pretraining foundation models that adapt to a wide range of molecule tasks have been long pursued by the community of drug discovery. While self-supervised learning methods are developed to leverage the sheer number of unlabeled molecules for pretraining, the landscape of supervised learning is much underexplored due to the absence of proper datasets and codebases. To facilitate the study of supervised learning on molecules, we curate 7 datasets with node- and graph-level supervision, and develop a library for studying multi-task learning models. The datasets are separated into 2 categories. First, the Toy-mix category contains 3 small datasets that are well known and well studied in the literature, but with the additional constraint that they must be used in a multi-task setting. Second, the Large-mix category contains 4 large datasets that, together, contain tens of billions of graph-level data points and tens of billions node-level data points associated to 100M unique molecules, representing orders of magnitude more data than other 2D-GNN datasets. Since molecule tasks are distributed across multiple levels, we design our library to explicitly consider multi-tasking alongside multi-level representations, backed by a large collection of models and features for different levels. With such a library design to accompany the datasets, we hope to accelerate the development of foundational models for molecules.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".