ReviewScience advances2025
Applying statistical modeling strategies to sparse datasets in synthetic chemistry.
Review in Science advances, 2025. 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
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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.
- Catalytic Enantioselective Aziridination of Z-Disubstituted Alkenes with Aspartyl β-Turn-Based Dirhodium(II) Metallopeptides and Assessment of Catalyst Scaffold Versatility.Journal of the American Chemical Society · 2026Article
- Synthesis and machine learning techniques to enable data-driven investigation of supramolecular host-guest interactions.Chemical science · 2026Article
- Organic Chemistry as a Catalyst for AI Innovation: Challenges, Methods, and Emerging Paradigms.Chemical reviews · 2026Review
- Assessment of Complementary Catalysts in an Uncharted Enantioselective Reaction of Sulfondiimines.Journal of the American Chemical Society · 2026Article
- Best Practices and Considerations for Applying Multiple Linear Regression in Organic Chemistry Research.The Journal of organic chemistry · 2026Review
- Stereoselective Generalizations over Diverse Sets of Chiral Acids Enabled by Buried Volume.Journal of the American Chemical Society · 2026Article
- Machine Learning in Homogeneous Catalysis: Basic Concepts and Best Practices.ACS catalysis · 2026Article
- Understanding and Enhancing Stereoselective Polymerization Using a Data Science Approach.Journal of the American Chemical Society · 2025Article
- Considerations in Pursuing Reaction Scope Generality.Angewandte Chemie (International ed. in English) · 2025Review
- Machine learning workflows beyond linear models in low-data regimes.Chemical science · 2025Article
- Computational tools for the prediction of site- and regioselectivity of organic reactions.Chemical science · 2025Review
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
The application of statistical modeling in organic chemistry is emerging as a standard practice for probing structure-activity relationships and as a predictive tool for many optimization objectives. This review is aimed as a tutorial for those entering the area of statistical modeling in chemistry. We provide case studies to highlight the considerations and approaches that can be used to successfully analyze datasets in low data regimes, a common situation encountered given the experimental demands of organic chemistry. Statistical modeling hinges on the data (what is being modeled), descriptors (how data are represented), and algorithms (how data are modeled). Herein, we focus on how various reaction outputs (e.g., yield, rate, selectivity, solubility, stability, and turnover number) and data structures (e.g., binned, heavily skewed, and distributed) influence the choice of algorithm used for constructing predictive and chemically insightful statistical models.
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.