ReviewNature chemistry2022
Making the collective knowledge of chemistry open and machine actionable.
Review in Nature chemistry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 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
33 citing papers in PubMed.
- General-Purpose Models for the Chemical Sciences: LLMs and Beyond.Chemical reviews · 2026Review
- Accelerating compound synthesis in drug discovery: the role of digitalisation and automation.RSC medicinal chemistry · 2025Article
- Synthetic Aspects and Characterization Needs in MOF Chemistry - from Discovery to Applications.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- Automating structure-activity analysis for electrochemical nitrogen reduction catalyst design through multi-agent collaborations.National science review · 2025Article
- Toward Reliable and Reproducible Research in Organic Photocatalysis by Carbon Nitride.ACS catalysis · 2025Review
- Probing the limitations of multimodal language models for chemistry and materials research.Nature computational science · 2025Article
- Negative chemical data boosts language models in reaction outcome prediction.Science advances · 2025Article
- Carbohydrate Synthesis is Entering the Data-Driven Digital Era.Chemistry (Weinheim an der Bergstrasse, Germany) · 2025Review
- Discovering organic reactions with a machine-learning-powered deciphering of tera-scale mass spectrometry data.Nature communications · 2025Article
- A Holistic Data-Driven Approach to Synthesis Predictions of Colloidal Nanocrystal Shapes.Journal of the American Chemical Society · 2025Article
- What I Learned from Analyzing Accurate Mass Data of 3000 Supporting Information Files.Organic letters · 2025Article
- Assessment of fine-tuned large language models for real-world chemistry and material science applications.Chemical science · 2025Article
- Simple User-Friendly Reaction Format.Molecular informatics · 2025Article
- Recent advances in artificial intelligent strategies for tissue engineering and regenerative medicine.Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) · 2024Review
- Reaction rebalancing: a novel approach to curating reaction databases.Journal of cheminformatics · 2024Article
- Advancing materials science through next-generation machine learning.Current opinion in solid state & materials science · 2024Article
- Digital Futures of Learning Pharmacology and Medicinal and Organic Chemistry.ACS pharmacology & translational science · 2024Article
- Leveraging Text-to-Text Pretrained Language Models for Question Answering in Chemistry.ACS omega · 2024Article
- When Yield Prediction Does Not Yield Prediction: An Overview of the Current Challenges.Journal of chemical information and modeling · 2024Review
- 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
3 authors.
Funding
Abstract
Large amounts of data are generated in chemistry labs-nearly all instruments record data in a digital form, yet a considerable proportion is also captured non-digitally and reported in ways non-accessible to both humans and their computational agents. Chemical research is still largely centred around paper-based lab notebooks, and the publication of data is often more an afterthought than an integral part of the process. Here we argue that a modular open-science platform for chemistry would be beneficial not only for data-mining studies but also, well beyond that, for the entire chemistry community. Much progress has been made over the past few years in developing technologies such as electronic lab notebooks that aim to address data-management concerns. This will help make chemical data reusable, however it is only one step. We highlight the importance of centring open-science initiatives around open, machine-actionable data and emphasize that most of the required technologies already exist-we only need to connect, polish and embrace them.
Identifiers
35379967What 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.