Palladium-Catalyzed Dehydrogenative Coupling: An Efficient Synthetic Strategy for the Construction of the Quinoline Core.

Palladium-catalyzed dehydrogenative coupling is an efficient synthetic strategy for the construction of quinoline scaffolds, a privileged structure and prevalent motif in many natural and biologically active products, in particular in marine alkaloids. Thus, quinolines and 1,2-dihydroquinolines can be selectively obtained in moderate-to-good yields via intramolecular C-H alkenylation reactions, by choosing the reaction conditions. This methodology provides a direct method for the construction of this type of quinoline through an efficient and atom economical procedure, and constitutes significant advance over the existing procedures that require preactivated reaction partners.

Pd(II)-Catalyzed Fujiwara-Moritani Reactions for the Synthesis and Functionalization of Substituted Coumarins.

Highly substituted coumarins, privileged and versatile scaffolds for bioactive natural products and fluorescence imaging, are obtained via a Pd(II)-catalyzed direct C-H alkenylation reaction (Fujiwara-Moritani reaction), which has emerged as a powerful tool for the construction and functionalization of heterocyclic compounds because of its chemical versatility and its environmental advantages. Thus, a selective 6-endo cyclization led to 4-substituted coumarins in moderate yields. Selected examples have been further functionalized in C3 through a second intermolecular C-H alkenylation reaction to give coumarin-acrylate hybrids, whose fluorescence spectra have been measured.

MCDCalc: Markov Chain Molecular Descriptors Calculator for Medicinal Chemistry.

AIMS: Cheminformatics models are able to predict different outputs (activity, property, chemical reactivity) in single molecules or complex molecular systems (catalyzed organic synthesis, metabolic reactions, nanoparticles, etc.). BACKGROUND: Cheminformatics models are able to predict different outputs (activity, property, chemical reactivity) in single molecules or complex molecular systems (catalyzed organic synthesis, metabolic reactions, nanoparticles, etc.). OBJECTIVE: Cheminformatics prediction of complex catalytic enantioselective reactions is a major goal in organic synthesis research and chemical industry. Markov Chain Molecular Descriptors (MCDs) have been largely used to solve Cheminformatics problems. There are different types of Markov chain descriptors such as Markov-Shannon entropies (Shk), Markov Means (Mk), Markov Moments (πk), etc. However, there are other possible MCDs that have not been used before. In addition, the calculation of MCDs is done very often using specific software not always available for general users and there is not an R library public available for the calculation of MCDs. This fact, limits the availability of MCMDbased Cheminformatics procedures. METHODS: We studied the enantiomeric excess ee(%)[Rcat] for 324 α-amidoalkylation reactions. These reactions have a complex mechanism depending on various factors. The model includes MCDs of the substrate, solvent, chiral catalyst, product along with values of time of reaction, temperature, load of catalyst, etc. We tested several Machine Learning regression algorithms. The Random Forest regression model has R2 > 0.90 in training and test. Secondly, the biological activity of 5644 compounds against colorectal cancer was studied. RESULTS: We developed very interesting model able to predict with Specificity and Sensitivity 70-82% the cases of preclinical assays in both training and validation series. CONCLUSION: The work shows the potential of the new tool for computational studies in organic and medicinal chemistry.