Machine learning (ML) algorithms are gaining importance in the processing of chemical information and modeling of chemical reactivity problems. In this work, we have developed a perturbation-theory and machine learning (PTML) model combining perturbation theory (PT) and ML algorithms for predicting the yield of a given reaction. For this purpose, we have selected Parham cyclization, which is a general and powerful tool for the synthesis of heterocyclic and carbocyclic compounds. This reaction has both structural (substitution pattern on the substrate, internal electrophile, ring size, etc.) and operational variables (organolithium reagent, solvent, temperature, time, etc.), so predicting the effect of changes on substrate design (internal elelctrophile, halide, etc.) or reaction conditions on the yield is an important task that could help to optimize the reaction design. The PTML model developed uses PT operators to account for perturbations under experimental conditions and/or structural variables of all the molecules involved in a query reaction, compared to a reaction of reference. Thus, a dataset of >100 reactions has been collected for different substrates and internal electrophiles, under different reaction conditions, with a wide range of yields (0-98%). The best PTML model found using General Linear Regression (GLR) has R = 0.88 in training and R = 0.83 in external validation series for 10 000 pairs of query and reference reactions. The PTML model has a final R = 0.95 for all reactions using multiple reactions of reference. We also report a comparative study of linear versus nonlinear PTML models based on artificial neural network (ANN) algorithms. PTML-ANN models (LNN, MLP, RBF) with R ≈ 0.1-0.8 do not outperform the first PMTL model. This result confirms the validity of the linearity of the model. Next, we carried out an experimental and theoretical study of nonreported Parham reactions to illustrate the practical use of the PTML model. A 500 000-point simulation and a Hammett analysis of the reactivity space of Parham reactions are also reported.

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.

Parham reaction is very important route for the synthesis of heterocyclic compounds, which consists of the intramolecular reaction of aryllithiums generated by lithium-halogen exchange with different types of internal electrophiles. 1 In this paper we collected a dataset of >100 reactions for many substrates and internal electrophiles (mainly, amides and esters) with a wide range of reaction yields (0 -99%). The reactions have been carried out in many different experimental conditions with different values non-structural variables (δ k