Papyrus - A large scale curated dataset aimed at bioactivity predictions

01 November 2021, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


With the recent rapid growth of publicly available ligand-protein bioactivity data, there is a trove of viable data that can be used to train machine learning algorithms. However, not all data is equal in terms of size and quality, and a significant portion of researcher’s time is needed to adapt the data to their needs. On top of that, finding the right data for a research question can often be a challenge on its own. As an answer to that, we have constructed the Papyrus dataset (DOI: 10.4121/16896406), comprised of around 60 million datapoints. This dataset contains multiple large publicly available datasets such as ChEMBL and ExCAPE-DB combined with several smaller datasets containing high quality data. The aggregated data has been standardised and normalised in a manner that is suitable for machine learning. We show how data can be filtered in a variety of ways, and also perform some baseline quantitative structure-activity relationship analyses and proteochemometrics modeling. Our ambition is this pruned data collection constitutes a benchmark set that can be used for constructing predictive models, while also providing a solid baseline for related research.


Curated Dataset
Machine Learning

Supplementary materials

Papyrus Supplementary Tables
Contains the information needed to reproduce the data repair, patent mapping and filtering steps as described in the preprint.

Supplementary weblinks


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