ArticleNucleic acids research2016
SureChEMBL: a large-scale, chemically annotated patent document database.
Article in Nucleic acids research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 94 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
94 citing papers in PubMed.
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- A Benchmark Evaluation of Chemical Structure Extraction from Patents: Insights and Challenges in Chemical Structure Recognition.Chemical research in toxicology · 2026Article
- Evaluating AI-Generated Molecules for Drug Discovery: From Generic Metrics to Translational Readiness.International journal of molecular sciences · 2026Review
- Centralized Review of Alzheimer's Disease and Related Dementias Biomedical Repositories and Computational Methods.Bioengineering (Basel, Switzerland) · 2026Review
- Predictive machine learning models for rational permeability design in de novo macrocycle engineering: a review.Journal of cheminformatics · 2026Review
- Structural optimization of drug molecules with incrementally trained language models.Nature communications · 2026Article
- AI-assisted interpretation of Markush structures in pharmaceutical patents: a review of emerging tools, datasets, and challenges.Journal of cheminformatics · 2026Review
- Harnessing machine learning and multi-scale modeling to discover novel ALOX15 inhibitors from marine natural products.Molecular diversity · 2026Article
- Ligand-based machine learning models to classify active compounds for prostaglandin EP2 receptor.Scientific reports · 2026Article
- TPDdb: the comprehensive database of targeted protein degrader.Nucleic acids research · 2026Article
- EMBL's European Bioinformatics Institute (EMBL-EBI) in 2025.Nucleic acids research · 2026Review
- ChEBI: re-engineered for a sustainable future.Nucleic acids research · 2026Article
- Computational Phenotypic Drug Discovery for Anticancer Chemotherapy: PTML Modeling of Multi-Cell Inhibitors of Colorectal Cancer Cell Lines.International journal of molecular sciences · 2025Article
- High-throughput electronic property prediction of cyclic molecules with 3D-enhanced machine learning.Chemical science · 2025Article
- BIOPTIC B1 Ultra-High-Throughput Virtual Screening System Discovers LRRK2 Ligands in Vast Chemical Space.Journal of chemical information and modeling · 2025Article
- Subgrapher: visual fingerprinting of chemical structures.Journal of cheminformatics · 2025Article
- Identification of a Novel Indolizine RORγT Inverse Agonist Using the AI-Driven Drug Design Platform.ACS medicinal chemistry letters · 2025Article
- BioRels' data infrastructure: a scientific schema and exchange standard to transform and enhance biological data sciences.Nucleic acids research · 2025Article
- Self-awareness of retrosynthesis via chemically inspired contrastive learning for reinforced molecule generation.Briefings in bioinformatics · 2025Article
- In Silico Approach for Antibacterial Discovery: PTML Modeling of Virtual Multi-Strain Inhibitors AgainstPharmaceuticals (Basel, Switzerland) · 2025Article
34 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
SureChEMBL is a publicly available large-scale resource containing compounds extracted from the full text, images and attachments of patent documents. The data are extracted from the patent literature according to an automated text and image-mining pipeline on a daily basis. SureChEMBL provides access to a previously unavailable, open and timely set of annotated compound-patent associations, complemented with sophisticated combined structure and keyword-based search capabilities against the compound repository and patent document corpus; given the wealth of knowledge hidden in patent documents, analysis of SureChEMBL data has immediate applications in drug discovery, medicinal chemistry and other commercial areas of chemical science. Currently, the database contains 17 million compounds extracted from 14 million patent documents. Access is available through a dedicated web-based interface and data downloads at: https://www.surechembl.org/.
Indexed as
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What OpenQuestion holds
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.