ReviewNature chemistry2024
Bridging the information gap in organic chemical reactions.
Review in Nature chemistry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- Structured HTE Data and Integrated Bayesian Optimization Accelerate Scalable Amidation Process Development.ACS organic & inorganic Au · 2026Article
- Article
- Yield Prediction of Organic Reactions in Biased Data Sets via Positive-Unlabeled Learning.Journal of the American Chemical Society · 2026Article
- One-Step Photocatalytic Oxidation of Cumene with Mesoporous Carbon Nitride.ACS catalysis · 2026Article
- NOCTIS: open-source toolkit that turns reaction data into actionable graph networks.Journal of cheminformatics · 2025Article
- A New Era of Artificial Intelligence (AI): Transforming Drug Discovery and Development.Journal of medicinal chemistry · 2025Article
- Carbohydrate Synthesis is Entering the Data-Driven Digital Era.Chemistry (Weinheim an der Bergstrasse, Germany) · 2025Review
- Transfer Learning-Enabled Ligand Prediction for Ni-Catalyzed Atroposelective Suzuki-Miyaura Cross-Coupling Based on Mechanistic Similarity: Leveraging Pd Knowledge for Ni Discovery.Journal of the American Chemical Society · 2025Article
- Artificial intelligence in drug development.Nature medicine · 2025Review
- Data-Driven Insights into the Transition-Metal-Catalyzed Asymmetric Hydrogenation of Olefins.The Journal of organic chemistry · 2024Article
- Catalysing (organo-)catalysis: Trends in the application of machine learning to enantioselective organocatalysis.Beilstein journal of organic chemistry · 2024Review
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
The varying quality of scientific reports is a well-recognized problem and often results from a lack of standardization and transparency in scientific publications. This situation ultimately leads to prominent complications such as reproducibility issues and the slow uptake of newly developed synthetic methods for pharmaceutical and agrochemical applications. In recent years, various impactful approaches have been advocated to bridge information gaps and to improve the quality of experimental protocols in synthetic organic publications. Here we provide a critical overview of these strategies and present the reader with a versatile set of tools to augment their standard procedures. We formulate eight principles to improve data management in scientific publications relating to data standardization, reproducibility and evaluation, and encourage scientists to go beyond current publication standards. We are aware that this is a substantial effort, but we are convinced that the resulting improved data situation will greatly benefit the progress of chemistry.
Identifiers
38548884What 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.