ArticlePloS one2013
Comparability of mixed IC₅₀ data - a statistical analysis.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 144 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
144 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Meta-Analysis and Topological Perturbation in Interactomic Network for Antiopioid Addiction Drug Repurposing.Journal of chemical information and modeling · 2025Pooled it
- CAPRICHO: Interpretable Quality Flagging and Flexible ChEMBL Bioactivity Curation for QSAR Modeling.Journal of chemical information and modeling · 2026Article
- Pharmacological stratification of public bioactivity databases: a reusable, OECD-anchored curation and benchmarking framework demonstrated for opioid receptors.Digital discovery · 2026Article
- Physical priors improve performance of structure-based binding affinity models.bioRxiv : the preprint server for biology · 2026Article
- Development of an Interpretable QSAR Model for Predicting Coagulation Factor XIIa Inhibitors Using Ensemble Machine Learning.Pharmaceuticals (Basel, Switzerland) · 2026Article
- An Explainable Machine Learning-Based QSAR Framework for Predicting Thrombin Inhibitory Activity.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Artificial intelligence in drug discovery - what it is, where we stand and the path forward.Nature reviews. Drug discovery · 2026Review
- Machine learning-based prediction of SARS-CoV-2 bioactivity: integrating ICBMC bioinformatics · 2026Article
- How Advanced Artificial Intelligence Technologies Shape Drug-Drug and Drug-Target Interaction Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Balancing Data Quantity and Quality: Evaluating Curation Strategies for Bioactivity Prediction in Lead Optimization.Journal of chemical information and modeling · 2026Article
- De novo design of anticancer 4-thiazolidinone derivatives: a generative framework shaped by activity cliffs.Journal of cheminformatics · 2026Article
- Computational framework to quantify synergistic ligand activity in insulin secretion and resistance pathways in type 2 diabetes.Journal of computer-aided molecular design · 2026Article
- A Computational Community Blind Challenge on Pan-Coronavirus Drug Discovery Data.Journal of chemical information and modeling · 2026Article
- Drug-Target Interaction Prediction with PIGLET.bioRxiv : the preprint server for biology · 2026Article
- Integrating artificial intelligence and manual curation to enhance bioassay annotations in ChEMBL.Journal of cheminformatics · 2026Article
- Benchmarking deep learning models for predicting anticancer drug potency (ICCommunications chemistry · 2026Article
- A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein-Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases.Journal of chemical information and modeling · 2026Article
- Machine Learning-Driven Bioprospecting of Cholinergic Medicinal Plants for Putative Leads Against Butyrylcholinesterase Towards Alzheimer's Care.Biochemistry research international · 2026Article
- Machine Learning-Guided Repositioning of a SARS-CoV-2-Targeting Molecular Series as Cruzain Inhibitors.ChemMedChem · 2026Article
- Machine Learning-Based QSAR Screening of Colombian Medicinal Flora for Potential Antiviral Compounds Against Dengue Virus: An In Silico Drug Discovery Approach.Pharmaceuticals (Basel, Switzerland) · 2025Article
84 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The biochemical half maximal inhibitory concentration (IC50) is the most commonly used metric for on-target activity in lead optimization. It is used to guide lead optimization, build large-scale chemogenomics analysis, off-target activity and toxicity models based on public data. However, the use of public biochemical IC50 data is problematic, because they are assay specific and comparable only under certain conditions. For large scale analysis it is not feasible to check each data entry manually and it is very tempting to mix all available IC50 values from public database even if assay information is not reported. As previously reported for Ki database analysis, we first analyzed the types of errors, the redundancy and the variability that can be found in ChEMBL IC50 database. For assessing the variability of IC50 data independently measured in two different labs at least ten IC50 data for identical protein-ligand systems against the same target were searched in ChEMBL. As a not sufficient number of cases of this type are available, the variability of IC50 data was assessed by comparing all pairs of independent IC50 measurements on identical protein-ligand systems. The standard deviation of IC50 data is only 25% larger than the standard deviation of Ki data, suggesting that mixing IC50 data from different assays, even not knowing assay conditions details, only adds a moderate amount of noise to the overall data. The standard deviation of public ChEMBL IC50 data, as expected, resulted greater than the standard deviation of in-house intra-laboratory/inter-day IC50 data. Augmenting mixed public IC50 data by public Ki data does not deteriorate the quality of the mixed IC50 data, if the Ki is corrected by an offset. For a broad dataset such as ChEMBL database a Ki- IC50 conversion factor of 2 was found to be the most reasonable.
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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.