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ZENODO
Dataset . 2025
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
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Datasets for Diversity Beats Size Scaling for Chemical Language Models

Authors: Medina, Borja;

Datasets for Diversity Beats Size Scaling for Chemical Language Models

Abstract

mols/ contains PubChem IDs for all source molecules. ind* denotes training sets, val* validation sets, and test.txt the test set. pairs/ contains molecular pairs (excluding self-pairs) and Tanimoto similarities in the form of HDF5 files. priors/ contains final model checkpoints. data_pipeline.toml is the configuration file used for pre-processing using the REINVENT4 data pipeline (version 4.5). custom_loader.py is a custom dataloader that was used along with the training code from this GitHub repository. Data in the HDF5 files can be accessed like so: import h5py f = h5py.File("pairs/val161K/final_0.h5py") # Keys are PubChem IDs keys = f.keys() # Loop over all pairs for key in keys: data = f[key] for pair in data: # (source ID, target ID, similarity) print(pair)

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average