ArticleJournal of computer-aided molecular design2010
Making priors a priority.
Article in Journal of computer-aided molecular design, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Mode of Action Analyses of Neferine, a Bisbenzylisoquinoline Alkaloid of Lotus (Frontiers in pharmacology · 2017Article
- The challenges of making decisions using uncertain data.Journal of computer-aided molecular design · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
When we build a predictive model of a drug property we rigorously assess its predictive accuracy, but we are rarely able to address the most important question, "How useful will the model be in making a decision in a practical context?" To answer this requires an understanding of the prior probability distribution ("the prior") and hence prevalence of negative outcomes due to the property being assessed. In this perspective, we illustrate the importance of the prior to assess the utility of a model in different contexts: to select or eliminate compounds, to prioritise compounds for further investigation using more expensive screens, or to combine models for different properties to select compounds with a balance of properties. In all three contexts, a better understanding of the prior probabilities of adverse events due to key factors will improve our ability to make good decisions in drug discovery, finding higher quality molecules more efficiently.
Indexed as
Identifiers
20953670What 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.