ArticleJournal of chemical information and modeling2018
Predicted Biological Activity of Purchasable Chemical Space.
Article in Journal of chemical information and modeling, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 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
22 citing papers in PubMed.
- Integrated Experimental and Computational Investigation of Azobenzenoid Derivatives Against Trypanosomatid: RNA Editing Ligase 1 as a Potential Molecular Target.Acta parasitologica · 2026Article
- Review
- Multi-Property De Novo Drug Design Using Deep Learning-Based Knowledge Distillation and Reinforcement Learning.International journal of molecular sciences · 2026Article
- Refining Drug-Induced Cholestasis Prediction: An Explainable Consensus Model Integrating Chemical and Biological Fingerprints.Journal of chemical information and modeling · 2025Article
- Comprehensive network pharmacology and experimental study to investigate the effect and mechanism of solasonine on breast carcinoma treatment.Cancer cell international · 2025Article
- Navigating a 1E+60 Chemical Space of Peptide/Peptoid Oligomers.Molecular informatics · 2025Article
- Review of Personalized Medicine and Pharmacogenomics of Anti-Cancer Compounds and Natural Products.Genes · 2024Review
- Using Generative Modeling to Endow with Potency Initially Inert Compounds with Good Bioavailability and Low Toxicity.Journal of chemical information and modeling · 2024Article
- Evaluating Scalable Supervised Learning for Synthesize-on-Demand Chemical Libraries.Journal of chemical information and modeling · 2023Article
- The Time and Place for Nature in Drug Discovery.JACS Au · 2022Review
- Random-forest model for drug-target interaction prediction via Kullbeck-Leibler divergence.Journal of cheminformatics · 2022Article
- Discovery of Kinase and Carbonic Anhydrase Dual Inhibitors by Machine Learning Classification and Experiments.Pharmaceuticals (Basel, Switzerland) · 2022Article
- Identification of potent inhibitors against transmembrane serine protease 2 for developing therapeutics against SARS-CoV-2.Journal of biomolecular structure & dynamics · 2022Article
- Photochemical synthesis of an epigenetic focused tetrahydroquinoline library.RSC medicinal chemistry · 2021Article
- Application of a High-Content Screening Assay Utilizing Primary Human Lung Fibroblasts to Identify Antifibrotic Drugs for Rapid Repurposing in COVID-19 Patients.SLAS discovery : advancing life sciences R & D · 2021Article
- Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions.Medicinal research reviews · 2021Review
- Extending the small-molecule similarity principle to all levels of biology with the Chemical Checker.Nature biotechnology · 2020Article
- Phenotypic Screening of Chemical Libraries Enriched by Molecular Docking to Multiple Targets Selected from Glioblastoma Genomic Data.ACS chemical biology · 2020Article
- Effect of Gallic acid and Myricetin on ovarian cancer models: a possible alternative antitumoral treatment.BMC complementary medicine and therapies · 2020Article
- Predicting kinase inhibitors using bioactivity matrix derived informer sets.PLoS computational biology · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Whereas 400 million distinct compounds are now purchasable within the span of a few weeks, the biological activities of most are unknown. To facilitate access to new chemistry for biology, we have combined the Similarity Ensemble Approach (SEA) with the maximum Tanimoto similarity to the nearest bioactive to predict activity for every commercially available molecule in ZINC. This method, which we label SEA+TC, outperforms both SEA and a naïve-Bayesian classifier via predictive performance on a 5-fold cross-validation of ChEMBL's bioactivity data set (version 21). Using this method, predictions for over 40% of compounds (>160 million) have either high significance (pSEA ≥ 40), high similarity (ECFP4MaxTc ≥ 0.4), or both, for one or more of 1382 targets well described by ligands in the literature. Using a further 1347 less-well-described targets, we predict activities for an additional 11 million compounds. To gauge whether these predictions are sensible, we investigate 75 predictions for 50 drugs lacking a binding affinity annotation in ChEMBL. The 535 million predictions for over 171 million compounds at 2629 targets are linked to purchasing information and evidence to support each prediction and are freely available via https://zinc15.docking.org and https://files.docking.org .
Indexed as
Identifiers
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.