Papyrus - A large scale curated dataset aimed at bioactivity predictions

DOI:10.4121/16896406.v3
The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/16896406
Datacite citation style:
Olivier Béquignon; Bongers, Brandon; Jespers, W. (Willem); IJzerman, Adriaan P.; Bob van de Water et. al. (2022): Papyrus - A large scale curated dataset aimed at bioactivity predictions. Version 3. 4TU.ResearchData. dataset. https://doi.org/10.4121/16896406.v3
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

choose version:
version 3 - 2022-04-04 (latest)
version 2 - 2021-11-01 version 1 - 2021-10-29

This repository contains the Papyrus dataset, an aggregated dataset of small molecule bioactivities, as described in the manuscript "Papyrus - A large scale curated dataset aimed at bioactivity predictions" (Work in Progress).

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, comprised of around 60 million datapoints. This dataset contains multiple large publicly available datasets such as ChEMBL and ExCAPE-DB combined with smaller datasets containing high quality data. This 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 rudimentary quantitative structure-activity relationship and proteochemometrics modeling. Our ambition is to create a benchmark set that can be used for constructing predictive models, while also providing a solid baseline for related research.

History

  • 2021-10-29 first online
  • 2022-04-04 published, posted

Publisher

4TU.ResearchData

Format

g-zipped tab-separated files and g-zipped SD files

Funding

  • Enhacing TRANslational SAFEty Assessment through Integrative Knowledge Management (grant code 777365) [more info...] European Commission

Organizations

Leiden Academic Centre for Drug Research (LACDR), The Netherlands
Leiden University, The Netherlands

DATA

Files (4)