N-Acyl amino acids can be converted by oxidative decarboxylation induced by Ag + /Cu 2+ /S 2 O 8 2-at room temperature in water into imides in 24-89% yields. Both N-benzoylvaline and Nbenzoylleucine gave N-formylbenzamide, which possibly results from oxidative cleavage of an enamide intermediate.
Among
the physical properties characterizing cocrystals, melting
temperature is one of the primary properties. Its prediction has been
done by researchers, but in the known prediction models, there are
some limitations. In order to adapt to the requirements of data development
further and improve the quality of prediction, two prediction models
of cocrystal melting temperature, the artificial neural network (ANN(I))
and the message passing neural network (MPNN), are proposed in this
paper with molecular descriptors and molecular graphs as the inputs.
As for ANN(I), molecular descriptors are applied to the prediction
of cocrystal melting temperature for the first time, and the importance
of features is evaluated by SHapley Additive explanation. For dataset
1 established in this paper, the prediction accuracy of ANN(I) is
95.75% on the training set and 95.67% on the test set. The feature
evaluation of ANN(I) reveals that GATS2v, AMR, BCUTc-1h, MLFER_BH,
BCUTp-1l, and ATSC1c play major roles in predicting the melting temperature.
The ANN(I) model based on molecular descriptors could explain the
relationship between the molecular structure and melting temperature
to some extent, but its prediction accuracy is relatively poor. Thus,
the message passing neural network (MPNN), a deep learning model based
on molecular graphs, is proposed. For the test sets of two datasets
used in this paper, the prediction accuracy of MPNN is 99.84 and 99.63%,
respectively. Also, in the data pre-processing, the MPNN extracts
the structural features of molecules through molecular graphs, which
could save time and workload a lot. The excellent performance suggests
that the MPNN model might be a more suitable tool for the prediction
of cocrystal properties in the future.
An enantioselective synthesis of the ABC-tricyclic furanochroman core of phomactin A has been accomplished by a γ-hydroxylation approach. The C ring was established by γ-hydroxylation of an α-enone. The regioselectivity was optimized by using a strong base with an oxophilic cation (t-BuLi) and a bulky oxygen donor (Davis reagent), which afforded the γ-hydroxylation product selectively in 63% yield.
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