J Chem Theory Comput
November 2018
Current neural networks for predictions of molecular properties use quantum chemistry only as a source of training data. This paper explores models that use quantum chemistry as an integral part of the prediction process. This is done by implementing self-consistent-charge Density-Functional-Tight-Binding (DFTB) theory as a layer for use in deep learning models.
View Article and Find Full Text PDFThe introduction of electron donor and acceptor groups at strategic locations on a fluorogenic cyanine dye allows fine-tuning of the absorption and emission spectra while preserving the ability of the dye to bind to biomolecular hosts such as double-stranded DNA and a single-chain antibody fragment originally selected for binding to the parent unsubstituted dye, thiazole orange (TO). The observed spectral shifts are consistent with calculated HOMO-LUMO energy gaps and reflect electron density localization on the quinoline half of TO in the LUMO. A dye bearing donating methoxy and withdrawing trifluoromethyl groups on the benzothiazole and quinoline rings, respectively, shifts the absorption spectrum to sufficiently longer wavelengths to allow excitation at green wavelengths as opposed to the parent dye, which is optimally excited in the blue.
View Article and Find Full Text PDFThe fluorescence of the SKC-513 ((E)-N-(9-(4-(1,4,7,10,13-pentaoxa-16-azacyclooctadecan-16-yl)phenyl)-6-(butyl(3-sulfopropyl)amino)-3H-xanthen-3-ylidene)-N-(3-sulfopropyl)butan-1-aminium) dye is shown experimentally to have high sensitivity to binding of the K(+) ion. Computations are used to explore the potential origins of this sensitivity and to make some suggestions regarding structural improvements. In the absence of K(+), excitation is to two nearly degenerate states, a neutral (N) excited state with a high oscillator strength, and a charge-transfer (CT) state with a lower oscillator strength.
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