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Abstract The term “generality” has recently been popularized in synthetic chemistry, owing largely to the increasing use of high‐throughput technology for producing vast quantities of data and the emergence of data science tools to plan and interpret these experiments. Despite this, the term has not been clearly defined, and there is no standardized approach toward developing a method with a diverse (general) scope. This minireview will examine different emerging strategies toward achieving generality using selected examples and aims to give the reader an overview of modern workflows that have been used to expedite this pursuit. 

Abstract A key challenge in synthetic chemistry is the selection of high‐performing ligands for cross‐coupling reactions. To address this challenge, this work presents a classification workflow to identify physicochemical descriptors that bin monophosphine ligands as active or inactive in Ni‐catalyzed Suzuki‐Miyaura coupling reactions. Using five previously published high‐throughput experimentation datasets for training, we found that a binary classifier using a phosphine's minimum buried volume and Boltzmann‐averaged minimum electrostatic potential is most effective at distinguishing high and low‐yielding ligands. Experimental validations are also presented. Using the two physicochemical descriptors from the binary classifier to represent the chemical space of monophosphine ligands leads to a more predictive guide for structure‐reactivity relationships compared with classic chemical space representations. 

Abstract Skeletal modifications enable elegant and rapid access to various derivatives of a compound that would otherwise be difficult to prepare. They are therefore a powerful tool, especially in the synthesis of natural products or drug discovery, to explore different natural products or to improve the properties of a drug candidate starting from a common intermediate. Inspired by the biosynthesis of the cephalotane natural products, we report here a single-atom insertion into the framework of the benzenoid subfamily, providing access to the troponoid congeners — representing the reverse of the proposed biosynthesis (i.e., a contra-biosynthesis approach). Computational evaluation of our designed transformation prompted us to investigate a Büchner–Curtius–Schlotterbeck reaction of ap-quinol methylether, which ultimately results in the synthesis of harringtonolide in two steps from cephanolide A, which we had previously prepared. Additional computational studies reveal that unconventional selectivity outcomes are driven by the choice of a Lewis acid and the nucleophile, which should inform further developments of these types of reactions. 

Abstract Transformer-based large language models are making significant strides in various fields, such as natural language processing^1–5, biology^6,7, chemistry^8–10and computer programming^11,12. Here, we show the development and capabilities of Coscientist, an artificial intelligence system driven by GPT-4 that autonomously designs, plans and performs complex experiments by incorporating large language models empowered by tools such as internet and documentation search, code execution and experimental automation. Coscientist showcases its potential for accelerating research across six diverse tasks, including the successful reaction optimization of palladium-catalysed cross-couplings, while exhibiting advanced capabilities for (semi-)autonomous experimental design and execution. Our findings demonstrate the versatility, efficacy and explainability of artificial intelligence systems like Coscientist in advancing research. 

Abstract “How strong is this Lewis acid?” is a question researchers often approach by calculating its fluoride ion affinity (FIA) with quantum chemistry. Here, we present FIA49k, an extensive FIA dataset with 48,986 data points calculated at the RI‐DSD‐BLYP‐D3(BJ)/def2‐QZVPP//PBEh‐3c level of theory, including 13 differentp‐block atoms as the fluoride accepting site. The FIA49k dataset was used to train FIA‐GNN, two message‐passing graph neural networks, which predict gas and solution phase FIA values of molecules excluded from training with a mean absolute error of 14 kJ mol⁻¹(r²=0.93) from the SMILES string of the Lewis acid as the only input. The level of accuracy is notable, given the wide energetic range of 750 kJ mol⁻¹spanned by FIA49k. The model's value was demonstrated with four case studies, including predictions for molecules extracted from the Cambridge Structural Database and by reproducing results from catalysis research available in the literature. Weaknesses of the model are evaluated and interpreted chemically. FIA‐GNN and the FIA49k dataset can be reached via a free web app (www.grebgroup.de/fia‐gnn). 

Abstract Molecular quantum mechanical modeling, accelerated by machine learning, has opened the door to high‐throughput screening campaigns of complex properties, such as the activation energies of chemical reactions and absorption/emission spectra of materials and molecules;in silico. Here, we present an overview of the main principles, concepts, and design considerations involved in such hybrid computational quantum chemistry/machine learning screening workflows, with a special emphasis on some recent examples of their successful application. We end with a brief outlook of further advances that will benefit the field. 

Pd catalysis is used to convert electron-deficient (hetero)aryl sulfonyl fluorides to the corresponding (hetero)aryl fluorides with extrusion of SO₂.