ReviewJournal of chemical information and modeling2023
Data Sharing in Chemistry: Lessons Learned and a Case for Mandating Structured Reaction Data.
Review in Journal of chemical information and modeling, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed.
- An accessible property classification framework to predict the solubility of functionalised naphthalenes and rylenes in organic solvents.Digital discovery · 2026Article
- ReactionSeek: LLM-powered literature data mining and knowledge discovery in organic synthesis.Nature communications · 2026Article
- Review
- Predicting reaction conditions: a data-driven perspective.Chemical science · 2025Review
- Advancing Organic Chemistry Using High-Throughput Experimentation.Angewandte Chemie (International ed. in English) · 2025Review
- Studying Noncovalent Interactions in Molecular Systems with Machine Learning.Chemical reviews · 2025Review
- Representation of chemistry transport models simulations using knowledge graphs.Journal of cheminformatics · 2025Article
- Simple User-Friendly Reaction Format.Molecular informatics · 2025Article
- Data accessibility in the chemical sciences: an analysis of recent practice in organic chemistry journals.Beilstein journal of organic chemistry · 2025Article
- Reproducibility in chemistry research.Heliyon · 2024Review
- Fine-tuning large language models for chemical text mining.Chemical science · 2024Article
- Standardizing Substrate Selection: A Strategy toward Unbiased Evaluation of Reaction Generality.ACS central science · 2024Article
- Rxn-INSIGHT: fast chemical reaction analysis using bond-electron matrices.Journal of cheminformatics · 2024Article
- Enhancing chemical synthesis: a two-stage deep neural network for predicting feasible reaction conditions.Journal of cheminformatics · 2024Article
- Machine learning-guided strategies for reaction conditions design and optimization.Beilstein journal of organic chemistry · 2024Review
- Identifying opportunities for late-stage C-H alkylation with high-throughput experimentation and in silico reaction screening.Communications chemistry · 2023Article
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
No grant is acknowledged in the PubMed record.
Abstract
The past decade has seen a number of impressive developments in predictive chemistry and reaction informatics driven by machine learning applications to computer-aided synthesis planning. While many of these developments have been made even with relatively small, bespoke data sets, in order to advance the role of AI in the field at scale, there must be significant improvements in the reporting of reaction data. Currently, the majority of publicly available data is reported in an unstructured format and heavily imbalanced toward high-yielding reactions, which influences the types of models that can be successfully trained. In this Perspective, we analyze several data curation and sharing initiatives that have seen success in chemistry and molecular biology. We discuss several factors that have contributed to their success and how we can take lessons from these case studies and apply them to reaction data. Finally, we spotlight the Open Reaction Database and summarize key actions the community can take toward making reaction data more findable, accessible, interoperable, and reusable (FAIR), including the use of mandates from funding agencies and publishers.
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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.