ArticleJournal of cheminformatics2023
Papyrus: a large-scale curated dataset aimed at bioactivity predictions.
Article in Journal of cheminformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
What it found
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The trial behind it
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Who cites it
29 citing papers in PubMed.
- Pharmacological stratification of public bioactivity databases: a reusable, OECD-anchored curation and benchmarking framework demonstrated for opioid receptors.Digital discovery · 2026Article
- ToxiVerse: chemical bioprofiling, toxicity data sharing and customizable predictive modeling.Bioinformatics (Oxford, England) · 2026Article
- QSAR-ML- and Metadynamics-Guided Design of Symmetrical Bis-Indanones to Overcome Mutational Anchor Loss in Acetylcholinesterase.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Enabling Automatic Generation of Protein-Ligand Complex Data Sets with Atomistic Detail.Journal of chemical information and modeling · 2026Article
- In Silico Drug Design and Discovery: Big Data for Small Molecule Design-2nd Edition.Biomolecules · 2026Article
- LaMGen: LLM-based 3D molecular generation for multi-target drug design.Nature communications · 2026Article
- Data curation in cheminformatics: importance and implementation.Journal of cheminformatics · 2026Review
- ActivityFinder: Toward the Fully Automatic Integration of Structural and Binding Affinity Data.Journal of chemical information and modeling · 2026Article
- Hybrid Dual-Context Prompted Cross-Attention Framework with Language Model Guidance for Multi-Label Prediction of Human Off-Target Ligand-Protein Interactions.International journal of molecular sciences · 2026Article
- Combining Bayesian and Evidential Uncertainty Quantification for Improved Bioactivity Modeling.Journal of chemical information and modeling · 2025Article
- Bioactivity Deep Learning for Complex Structure-Free Compound-Protein Interaction Prediction.Journal of chemical information and modeling · 2025Article
- Toward Assay-Aware Bioactivity Model(er)s: Getting a Grip on Biological Context.Journal of chemical information and modeling · 2025Article
- Integrating Pharmacokinetics and Quantitative Systems Pharmacology Approaches in Generative Drug Design.Journal of chemical information and modeling · 2025Article
- Enhancing Transthyretin Binding Affinity Prediction with a Consensus Model: Insights from the Tox24 Challenge.Chemical research in toxicology · 2025Article
- Generate what you can make: achieving in-house synthesizability with readily available resources in de novo drug design.Journal of cheminformatics · 2025Article
- Normalized Protein-Ligand Distance Likelihood Score for End-to-End Blind Docking and Virtual Screening.Journal of chemical information and modeling · 2025Article
- Augmented BindingNet dataset for enhanced ligand binding pose predictions using deep learning.npj drug discovery · 2025Article
- Docking-Informed Machine Learning for Kinome-wide Affinity Prediction.Journal of chemical information and modeling · 2024Article
- QSPRpred: a Flexible Open-Source Quantitative Structure-Property Relationship Modelling Tool.Journal of cheminformatics · 2024Article
- AlphaFold Meets De Novo Drug Design: Leveraging Structural Protein Information in Multitarget Molecular Generative Models.Journal of chemical information and modeling · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
With the ongoing rapid growth of publicly available ligand-protein bioactivity data, there is a trove of valuable data that can be used to train a plethora of machine-learning algorithms. However, not all data is equal in terms of size and quality and a significant portion of researchers' 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. To meet these challenges, we have constructed the Papyrus dataset. Papyrus is comprised of around 60 million data points. 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 examples of quantitative structure-activity relationship analyses and proteochemometric modelling. Our ambition is that this pruned data collection constitutes a benchmark set that can be used for constructing predictive models, while also providing an accessible data source for research.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.