ReviewACS central science2021
The Evolution of Data-Driven Modeling in Organic Chemistry.
Review in ACS central science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 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
60 citing papers in PubMed.
- A Practical Guide to Bayesian Optimization for Organic Chemists.Chem catalysis · 2026Article
- BONAFIDE: a Python framework for the calculation of local features for atoms and bonds in molecules.Journal of cheminformatics · 2026Article
- 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
- Origin of Scission Selectivity for Phosphoranyl Radicals in Photoredox Catalysis.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
- Yield Prediction of Organic Reactions in Biased Data Sets via Positive-Unlabeled Learning.Journal of the American Chemical Society · 2026Article
- Synergizing Chemical and AI Communities for Advancing Laboratories of the Future.ACS central science · 2026Review
- Illuminating the Interface of Blocc Chemistry and Data Science: Maximizing Function with ML-Guided Discovery and a Digital Molecule Maker.Journal of chemical education · 2026Article
- Stereoselective Generalizations over Diverse Sets of Chiral Acids Enabled by Buried Volume.Journal of the American Chemical Society · 2026Article
- Modelling and estimation of chemical reaction yields from high-throughput experiments.Communications chemistry · 2026Article
- Understanding and Enhancing Stereoselective Polymerization Using a Data Science Approach.Journal of the American Chemical Society · 2025Article
- Elucidating Ligand-Dependent Selectivities in Pd-Catalyzed C-H Activations.Angewandte Chemie (International ed. in English) · 2025Article
- Illuminating the Interface of Blocc Chemistry and Data Science: An Introduction to K‑Nearest Neighbor Analysis and K‑Medoids Clustering.Journal of chemical education · 2025Article
- Understanding DFT Uncertainties for More Reliable Reactivity Predictions by Advancing the Analysis of Error Sources.Journal of chemical theory and computation · 2025Article
- Considerations in Pursuing Reaction Scope Generality.Angewandte Chemie (International ed. in English) · 2025Review
- Quantum Descriptor-Based Machine-Learning Modeling of Thermal Hazard of Cyclic Sulfamidates.Journal of chemical information and modeling · 2025Article
- Discovery of NiJournal of the American Chemical Society · 2025Article
- Molecular Machine Learning Approach to Enantioselective C-H Bond Activation Reactions: From Generative AI to Experimental Validation.Chemical science · 2025Article
- Deep Reinforcement Learning-Based Self-Optimization of Flow Chemistry.ACS engineering Au · 2025Article
- Nickel-Mediated Radical Capture: Evidence for a Concerted Inner-Sphere Mechanism.Journal of the American Chemical Society · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Organic chemistry is replete with complex relationships: for example, how a reactant's structure relates to the resulting product formed; how reaction conditions relate to yield; how a catalyst's structure relates to enantioselectivity. Questions like these are at the foundation of understanding reactivity and developing novel and improved reactions. An approach to probing these questions that is both longstanding and contemporary is data-driven modeling. Here, we provide a synopsis of the history of data-driven modeling in organic chemistry and the terms used to describe these endeavors. We include a timeline of the steps that led to its current state. The case studies included highlight how, as a community, we have advanced physical organic chemistry tools with the aid of computers and data to augment the intuition of expert chemists and to facilitate the prediction of structure-activity and structure-property relationships.
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.