Unusually long thermal half-lives of perhalogenated cis-azobenzenes enabled their structural characterization and the first evidence of a crystal-to-crystal cis → trans azobenzene isomerization. Irradiation with visible light transforms a perhalogenated cis-azobenzene single crystal into a polycrystalline aggregate of its trans-isomer in a photomechanical transformation that involves a significant, controllable, and thermally irreversible change of crystal shape. This is the first demonstration of permanent photomechanical modification of crystal shape in an azobenzene.
Quantum-Guided Molecular Mechanics (Q2MM) can be used to derive transition state force fields (TSFFs) that allow the fast and accurate predictions of stereoselectivity for a wide range of catalytic enantioselective reactions. The basic ideas behind the derivation of TSFFs using Q2MM are discussed and the steps involved in obtaining a TSFF using the Q2MM code, publically available at github.com/q2mm, are shown. The applicability for a range of reactions, including several non-standard applications of Q2MM, is demonstrated. Future developments of the method are also discussed.
At
the early stages of the drug development process, thousands
of compounds are synthesized in order to attain the best possible
potency and pharmacokinetic properties. Once successful scaffolds
are identified, large libraries of analogues are made, which is a
challenging and time-consuming task. Recently, late stage functionalization
(LSF) has become increasingly prominent since these reactions selectively
functionalize C–H bonds, allowing to quickly produce analogues.
Classical electrophilic aromatic halogenations are a powerful type
of reaction in the LSF toolkit. However, the introduction of
an electrophile in a regioselective manner on a drug-like molecule
is a challenging task. Herein we present a machine learning model
able to predict the reactive site of an electrophilic aromatic substitution
with an accuracy of 93% (internal validation set). The model takes
as input a SMILES of a compound and uses six quantum mechanics descriptors
to identify its reactive site(s). On an external validation set, 90%
of all molecules were correctly predicted.
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