ArticleJournal of chemical information and modeling2014
Computational prediction and validation of an expert's evaluation of chemical probes.
Article in Journal of chemical information and modeling, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed.
- Chemical Probes Review: Choosing the Right Path Towards Pharmacological Targets in Drug Discovery, Challenges and Future Perspectives.Combinatorial chemistry & high throughput screening · 2024Review
- Will the chemical probes please stand up?RSC medicinal chemistry · 2021Article
- Novel C-2 Symmetric Molecules as α-Glucosidase and α-Amylase Inhibitors: Design, Synthesis, Kinetic Evaluation, Molecular Docking and Pharmacokinetics.Molecules (Basel, Switzerland) · 2019Article
- Data Mining and Computational Modeling of High-Throughput Screening Datasets.Methods in molecular biology (Clifton, N.J.) · 2018Article
- Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets.Molecular pharmaceutics · 2017Article
- Collaborative drug discovery for More Medicines for Tuberculosis (MM4TB).Drug discovery today · 2017Review
- Open Source Bayesian Models. 3. Composite Models for Prediction of Binned Responses.Journal of chemical information and modeling · 2016Article
- Predicting Mouse Liver Microsomal Stability with "Pruned" Machine Learning Models and Public Data.Pharmaceutical research · 2016Article
- Open Source Bayesian Models. 1. Application to ADME/Tox and Drug Discovery Datasets.Journal of chemical information and modeling · 2015Article
- Parallel worlds of public and commercial bioactive chemistry data.Journal of medicinal chemistry · 2015Article
- A brief review of recent Charcot-Marie-Tooth research and priorities.F1000Research · 2015Review
- Small molecules with antiviral activity against the Ebola virus.F1000Research · 2015Review
- Collaboration for rare disease drug discovery research.F1000Research · 2014Article
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
Abstract
In a decade with over half a billion dollars of investment, more than 300 chemical probes have been identified to have biological activity through NIH funded screening efforts. We have collected the evaluations of an experienced medicinal chemist on the likely chemistry quality of these probes based on a number of criteria including literature related to the probe and potential chemical reactivity. Over 20% of these probes were found to be undesirable. Analysis of the molecular properties of these compounds scored as desirable suggested higher pKa, molecular weight, heavy atom count, and rotatable bond number. We were particularly interested whether the human evaluation aspect of medicinal chemistry due diligence could be computationally predicted. We used a process of sequential Bayesian model building and iterative testing as we included additional probes. Following external validation of these methods and comparing different machine learning methods, we identified Bayesian models with accuracy comparable to other measures of drug-likeness and filtering rules created to date.
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.