Cu-catalysed arylation reactions devoted to the formation of C-C and C-heteroatom bonds (Ullmann-type couplings) have acquired great importance in the last decade. This review discusses the history and development of coupling reactions between aryl halides and various classes of nucleophiles, focusing mostly on the different mechanisms proposed through the years. Selected mechanistic investigations are treated more in depth than others. For example, evidence in favour or against radical mechanisms is discussed. Cu(I) and Cu(III) complexes involved in the Ullmann reaction and N/O selectivity in aminoalcohol arylation are discussed. A separate section has been dedicated to the synthesis of heterocyclic rings through intramolecular couplings. Finally, recent developments in green chemistry for these reactions, such as reactions in aqueous media and heterogeneous catalysis, have also been reviewed.

Recent development of conceptually new chiral bifunctional transition metal based catalysts for asymmetric reductive transformations is described. The chiral bifunctional molecular catalyst promoted reduction is now realized to be a powerful tool to access chiral compounds in organic synthetic procedures in both academia and industry. Based on structural investigation of the actual catalyst and its intermediates and a deep understanding of the reaction mechanism, this asymmetric reduction system can be widely used to produce valuable chiral alcohols and is now is applicable to commercial scale production.

Solubility prediction remains a critical challenge in drug development, synthetic route and chemical process design, extraction and crystallisation. Here we report a successful approach to solubility prediction in organic solvents and water using a combination of machine learning (ANN, SVM, RF, ExtraTrees, Bagging and GP) and computational chemistry. Rational interpretation of dissolution process into a numerical problem led to a small set of selected descriptors and subsequent predictions which are independent of the applied machine learning method. These models gave significantly more accurate predictions compared to benchmarked open-access and commercial tools, achieving accuracy close to the expected level of noise in training data (LogS ± 0.7). Finally, they reproduced physicochemical relationship between solubility and molecular properties in different solvents, which led to rational approaches to improve the accuracy of each models.

Transition-metal-catalyzed hydrogen-transfer reactions have been used for the conversion of alcohols into benzimidazoles and aldehydes into benzoxazoles and benzothiazoles.