ArticleProceedings of the National Academy of Sciences of the United States of America2022
Predicting relative efficiency of amide bond formation using multivariate linear regression.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 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
20 citing papers in PubMed.
- Transforming Molecular Synthesis With Large Language Models.Chemistry (Weinheim an der Bergstrasse, Germany) · 2026Review
- Evaluation of Machine Learning Models for Condition Optimization in Diverse Amide Coupling Reactions.ACS omega · 2026Article
- Chemodivergent aminocarbonylation enabled by oxygen vacancy-engineered Pd-doped InScience advances · 2026Article
- Predicting reaction conditions: a data-driven perspective.Chemical science · 2025Review
- Quantum Descriptor-Based Machine-Learning Modeling of Thermal Hazard of Cyclic Sulfamidates.Journal of chemical information and modeling · 2025Article
- Studying Noncovalent Interactions in Molecular Systems with Machine Learning.Chemical reviews · 2025Review
- Towards global reaction feasibility and robustness prediction with high throughput data and bayesian deep learning.Nature communications · 2025Article
- 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
- Rapid prediction of conformationally-dependent DFT-level descriptors using graph neural networks for carboxylic acids and alkyl amines.Digital discovery · 2025Article
- Applying statistical modeling strategies to sparse datasets in synthetic chemistry.Science advances · 2025Review
- Article
- Reaction-Agnostic Featurization of Bidentate Ligands for Bayesian Ridge Regression of Enantioselectivity.ACS catalysis · 2024Article
- Standardizing Substrate Selection: A Strategy toward Unbiased Evaluation of Reaction Generality.ACS central science · 2024Article
- A Slug Flow Platform with Multiple Process Analytics Facilitates Flexible Reaction Optimization.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- A genetic optimization strategy with generality in asymmetric organocatalysis as a primary target.Chemical science · 2024Article
- Quantitative Structure-Reactivity Relationships for Synthesis Planning: The Benzhydrylium Case.The journal of physical chemistry. A · 2024Article
- Dataset Design for Building Models of Chemical Reactivity.ACS central science · 2023Review
- The challenge of balancing model sensitivity and robustness in predicting yields: a benchmarking study of amide coupling reactions.Chemical science · 2023Article
- Turning high-throughput structural biology into predictive inhibitor design.Proceedings of the National Academy of Sciences of the United States of America · 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
5 authors.
Funding
Abstract
Amides are ubiquitous in biologically active natural products and commercial drugs. The most common strategy for introducing this functional group is the coupling of a carboxylic acid with an amine, which requires the use of a coupling reagent to facilitate elimination of water. However, the optimal reaction conditions often appear rather arbitrary to the specific reaction. Herein, we report the development of statistical models correlating measured rates to physical organic descriptors to enable the prediction of reaction rates for untested carboxylic acid/amine pairs. The key to the success of this endeavor was the development of an end-to-end data science–based workflow to select a set of coupling partners that are appropriately distributed in chemical space to facilitate statistical model development. By using a parameterization, dimensionality reduction, and clustering protocol, a training set was identified. Reaction rates for a range of carboxylic acid and primary alkyl amine couplings utilizing carbonyldiimidazole (CDI) as the coupling reagent were measured. The collected rates span five orders of magnitude, confirming that the designed training set encompasses a wide range of chemical space necessary for effective model development. Regressing these rates with high-level density functional theory (DFT) descriptors allowed for identification of a statistical model wherein the molecular features of the carboxylic acid are primarily responsible for the observed rates. Finally, out-of-sample amide couplings are used to determine the limitations and effectiveness of the model.
Indexed as
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.