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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. 

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. 

Nonaqueous redox flow batteries (NARFBs) offer a promising solution for large-scale storage of renewable energy. However, crossover of redox active molecules between the two sides of the cell is a major factor limiting their development, as most selective separators are designed for deployment in water, rather than organic solvents. This report describes a systematic investigation of the crossover rates of redox active organic molecules through an anion exchange separator under RFB-relevant non-aqueous conditions (in acetonitrile/KPF6) using a combination of experimental and computational methods. A structurally diverse set of neutral and cationic molecules was selected, and their rates of crossover were determined experimentally with the organic solvent-compatible anion exchange separator Fumasep FAP-375-PP. The resulting data were then fit to various descriptors of molecular size, charge, and hydrophobicity (overall charge, solution diffusion coefficient, globularity, dynamic volume, dynamic surface area, clogP). This analysis resulted in multiple statistical models of crossover rates for this separator. These models were then used to predict tether groups that dramatically slow the crossover of small organic molecules in this system. 

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. 

The synthesis of aryl amines from 3-alkynyl-2-pyrones and various amines is described. 

Cross-electrophile coupling has emerged as an attractive and efficient method for the synthesis of C(sp2)–C(sp3) bonds. These reactions are most often catalyzed by nickel complexes of nitrogenous ligands, especially 2,2’-bipyridines. Precise prediction, selection, and design of optimal ligands remains challenging, despite significant increases in reaction scope and mechanistic understanding. Molecular parame-terization and statistical modeling provide a path to the development of improved bipyridine ligands that will enhance the selectivity of existing reactions and broaden the scope of electrophiles that can be coupled. Herein, we describe the generation of a computational lig-and library, correlation of observed reaction outcomes with features of the ligands, and in silico design of improved bipyridine ligands for Ni-catalyzed cross-electrophile coupling. The new nitrogen-substituted ligands display a fivefold increase in selectivity for product formation versus homodimerization when compared to the current state of the art. This increase in selectivity and yield was general for several cross-electrophile couplings, including the challenging coupling of an aryl chloride with an N-alkylpyridinium salt.