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Creators/Authors contains: "Coley, Connor W"

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  1. ; ; ; ; ; ; (, Digital Discovery)
    DFT-level descriptor libraries were constructed to train 2D and 3D graph neural networks for on the-fly the prediction of carboxylic acid and alkyl amine descriptors suitable for statistical modeling of medicinally relevant molecules. 
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    Free, publicly-accessible full text available January 15, 2026
  2. ; ; ; ; (, Digital Discovery)
    An open-source fine-tuned large language model can extract reaction information from organic synthesis procedure text into structured data that follows the Open Reaction Database (ORD) schema. 
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    Free, publicly-accessible full text available September 11, 2025
  3. ; ; ; ; ; ; ; (, Journal of the American Chemical Society)
    Free, publicly-accessible full text available June 12, 2025
  4. ; ; ; ; ; ; ; ; (, Journal of the American Chemical Society)
    Free, publicly-accessible full text available June 5, 2025
  5. ; ; ; ; ; ; (, figshare)
    This Figshare repository contains the datasets and models for our paper titled: Rapid Prediction of Conformationally-Dependent DFT-Level Descriptors using Graph Neural Networks for Carboxylic Acids and Alkyl Amines. It is organized into 2D and 3D, which represent the modeling architectures used in building graph neural networks for molecular descriptors corresponding to acids and amines. Carboxylic acid, primary alkyl amine, and secondary alkyl amine (as well as a combined alkyl amine) libraries are provided in their entirety, including conformer properties. Additional test and external validation statistics for each library are also provided within this repository. 
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  6. ; ; (, Nature Computational Science)
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  7. ; ; (, Chemical Science)
    The field of predictive chemistry relates to the development of models able to describe how molecules interact and react. It encompasses the long-standing task of computer-aided retrosynthesis, but is far more reaching and ambitious in its goals. In this review, we summarize several areas where predictive chemistry models hold the potential to accelerate the deployment, development, and discovery of organic reactions and advance synthetic chemistry. 
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    Full Text Available 
  8. ; ; ; ; ; ; (, The Journal of Organic Chemistry)
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  9. ; ; ; ; ; ; (, Organic Letters)
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  10. ; ; ; ; ; ; ; ; ; (, Nature Chemical Biology)
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