Datasets for Diversity Beats Size Scaling for Chemical Language Models
Medina Borja (Researcher)
2025
2025/11/7
Switzerland, Geneva
Creative Commons Attribution 4.0 International

数据描述

Datasets for Diversity Beats Size Scaling for Chemical Language Models

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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custom_dataloader.py

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DOI: 10.5281/ZENODO.17552157
zenodo.org
IP: 137.138.76.77
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相关论文

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Diversity Beats Size Scaling for Chemical Language Models

Borja MedinaAlessandro TiboJiazhen HeJon Paul JanetNicklas Österbacka
Npj Drug Discovery
2026
2026/8/4
Vol.3 No.1 p.260
Chemical language models, such as transformers trained on SMILES strings, are increasingly used in drug design and have seen rapid growth in both model capacity and training dataset size. The impact of this scaling on practical downstream performance remains unclear, however. We systematically evalu...
Computational biology and bioinformaticsMathematics and computing
10.1038/S44386-026-00056-3
ISSN:3005-1452