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  1. Abstract

    A synthetic method for the palladium‐catalyzed cyanation of aryl boronic acids using bench stable and non‐toxicN‐cyanosuccinimide has been developed. High‐throughput experimentation facilitated the screen of 90 different ligands and the resultant statistical data analysis identified that ligand σ‐donation, π‐acidity and sterics are key drivers that govern yield. Categorization into three ligand groups – monophosphines, bisphosphines and miscellaneous – was performed before the analysis. For the monophosphines, the yield of the reaction increases for strong σ‐donating, weak π‐accepting ligands, with flexible pendant substituents. For the bisphosphines, the yield predominantly correlates with ligand lability. The applicability of the designed reaction to a wider substrate scope was investigated, showing good functional group tolerance in particular with boronic acids bearing electron‐withdrawing substituents. This work outlines the development of a novel reaction, coupled with a fast and efficient workflow to gain understanding of the optimal ligand properties for the design of improved palladium cross‐coupling catalysts.

     
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  2. Free, publicly-accessible full text available December 15, 2024
  3. Free, publicly-accessible full text available September 1, 2024
  4. Free, publicly-accessible full text available April 12, 2024
  5. The lack of publicly available, large, and unbiased datasets is a key bottleneck for the application of machine learning (ML) methods in synthetic chemistry. Data from electronic laboratory notebooks (ELNs) could provide less biased, large datasets, but no such datasets have been made publicly available. The first real-world dataset from the ELNs of a large pharmaceutical company is disclosed and its relationship to high-throughput experimentation (HTE) datasets is described. For chemical yield predictions, a key task in chemical synthesis, an attributed graph neural network (AGNN) performs as well as or better than the best previous models on two HTE datasets for the Suzuki–Miyaura and Buchwald–Hartwig reactions. However, training the AGNN on an ELN dataset does not lead to a predictive model. The implications of using ELN data for training ML-based models are discussed in the context of yield predictions. 
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  6. This perspective describes Auto-QChem, an automatic, high-throughput and end-to-end DFT calculation workflow that computes chemical descriptors for organic molecules. Tailored toward users without extensive programming experience, Auto-QChem has facilitated more than 38 000 DFT calculations for 17 000 molecules as of January 2022. Starting from string representations of molecules, Auto-QChem automatically (a) generates conformational ensembles, (b) submits and manages DFT calculations on a high-performance computing (HPC) cluster, (c) extracts production-ready features that are suitable for statistical analysis and machine learning model development, and (d) stores resulting calculations in a cloud-hosted and web-accessible database. We describe in detail the design and implementation of Auto-QChem, as well as its current functionalities. We also review three case studies where Auto-QChem was applied to our recent efforts in combining data science approaches in organic chemistry methodology development: (a) the design of a diverse and unbiased aryl bromide substrate scope for a Ni/photoredox catalyzed alkylation reaction, (b) mechanistic studies on the effect of bioxazoline (BiOx) and biimidazoline (BiIm) ligands on enantioselectivity in a Ni/photoredox catalyzed cross-electrophile coupling of epoxides and aryl iodides, (c) the development of a reaction condition optimization framework using Bayesian optimization. In addition, we discuss limitations and future directions of Auto-QChem and similar automated DFT calculation systems. 
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