Au(I) Catalyzed HF Transfer: Tandem Alkyne Hydrofluorination and Perfluoroarene Functionalisation

HF transfer reactions between organic substrates are an incredibly rare class of transformation. Such reactions require the development of new catalytic systems that can promote both defluorination and fluorination steps in a single reaction sequence. Herein, we report a novel catalytic protocol in which an equivalent of HF is generated from a perfluoroarene | nucleophile pair and transferred directly to an alkyne. The reaction is catalysed by [Au(IPr)NiPr2] (IPr = N,N’-1,3-Bis(2,6-diisopropylphenyl)imidazol-2-ylidene) and is 100 % atom efficient. HF transfer generates two useful products in the form of functionalised fluoroarenes and fluoroalkenes. Mechanistic studies (rate laws, KIEs, DFT calculations, competition experiments) are consistent with the Au(I) catalyst facilitating a catalytic network involving both concerted SNAr and hydrofluorination steps. The nature of the nucleophile impacts the turnover-limiting step. The cSNAr step is turnover-limiting for phenol-based nucleophiles while proteodeauration likely becomes turnover-limiting for aniline-based nucleophiles. The new approach removes the need for direct handling of HF reagents in hydrofluorination and offers new possibilities to manipulate the fluorine content of organic molecules through catalysis.

Kernel Based Data-Adaptive Support Vector Machines for Multi-Class Classification

Imbalanced data exist in many classification problems. The classification of imbalanced data has remarkable challenges in machine learning. The support vector machine (SVM) and its variants are popularly used in machine learning among different classifiers thanks to their flexibility and interpretability. However, the performance of SVMs is impacted when the data are imbalanced, which is a typical data structure in the multi-category classification problem. In this paper, we employ the data-adaptive SVM with scaled kernel functions to classify instances for a multi-class population. We propose a multi-class data-dependent kernel function for the SVM by considering class imbalance and the spatial association among instances so that the classification accuracy is enhanced. Simulation studies demonstrate the superb performance of the proposed method, and a real multi-class prostate cancer image dataset is employed as an illustration. Not only does the proposed method outperform the competitor methods in terms of the commonly used accuracy measures such as the F-score and G-means, but also successfully detects more than 60% of instances from the rare class in the real data, while the competitors can only detect less than 20% of the rare class instances. The proposed method will benefit other scientific research fields, such as multiple region boundary detection.

RiskLogitboost Regression for Rare Events in Binary Response: An Econometric Approach

A boosting-based machine learning algorithm is presented to model a binary response with large imbalance, i.e., a rare event. The new method (i) reduces the prediction error of the rare class, and (ii) approximates an econometric model that allows interpretability. RiskLogitboost regression includes a weighting mechanism that oversamples or undersamples observations according to their misclassification likelihood and a generalized least squares bias correction strategy to reduce the prediction error. An illustration using a real French third-party liability motor insurance data set is presented. The results show that RiskLogitboost regression improves the rate of detection of rare events compared to some boosting-based and tree-based algorithms and some existing methods designed to treat imbalanced responses.

Heterologous expression of a single fungal HR-PKS leads to the formation of diverse 2-alkenyl-tetrahydropyrans in model fungi

2-Alkenyl-tetrahydropyrans belong to a rare class of natural products that exhibit broad antifungal activities. The structural instability and rareness in nature restrained their discovery and drug development. In this study,...

Inclusion of Hydroxycinnamic Acids in Methylated Cyclodextrins: Host-Guest Interactions and Effects on Guest Thermal Stability

There is ongoing interest in exploiting the antioxidant activity and other medicinal properties of natural monophenolic/polyphenolic compounds, but their generally low aqueous solubility limits their applications. Numerous studies have been undertaken to solubilize such compounds via supramolecular derivatization with co-crystal formation with biocompatible coformer molecules and cyclodextrin (CD) complexation being two successful approaches. In this study, eight new crystalline products obtained by complexation between methylated cyclodextrins and the bioactive phenolic acids (ferulic, hydroferulic, caffeic, and p-coumaric acids) were investigated using thermal analysis (hot stage microscopy, thermogravimetry, differential scanning calorimetry) and X-ray diffraction. All of the complexes crystallized as ternary systems containing the host CD, a phenolic acid guest, and water. On heating each complex, the primary thermal events were dehydration and liberation of the respective phenolic acid component, the mass loss for the latter step enabling determination of the host-guest stoichiometry. Systematic examination of the X-ray crystal structures of the eight complexes enabled their classification according to the extent of inclusion of each guest molecule within the cavity of its respective CD molecule. This revealed three CD inclusion compounds with full guest encapsulation, three with partial guest inclusion, and two that belong to the rare class of ‘non-inclusion’ compounds.

Review of Oxepine-Pyrimidinone-Ketopiperazine Type Nonribosomal Peptides

Recently, a rare class of nonribosomal peptides (NRPs) bearing a unique Oxepine-Pyrimidinone-Ketopiperazine (OPK) scaffold has been exclusively isolated from fungal sources. Based on the number of rings and conjugation systems on the backbone, it can be further categorized into three types A, B, and C. These compounds have been applied to various bioassays, and some have exhibited promising bioactivities like antifungal activity against phytopathogenic fungi and transcriptional activation on liver X receptor α. This review summarizes all the research related to natural OPK NRPs, including their biological sources, chemical structures, bioassays, as well as proposed biosynthetic mechanisms from 1988 to March 2020. The taxonomy of the fungal sources and chirality-related issues of these products are also discussed.