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https://www.selleckchem.com/products/abt-199.html To demonstrate the great prospects for further combining hyperspectral SIM with various spectral analysis methods, we also perform spectral unmixing of the hyperspectral SIM result while imaging the spectrally overlapped sample.The central challenge in automated synthesis planning is to be able to generate and predict outcomes of a diverse set of chemical reactions. In particular, in many cases, the most likely synthesis pathway cannot be applied due to additional constraints, which requires proposing alternative chemical reactions. With this in mind, we present Molecule Edit Graph Attention Network (MEGAN), an end-to-end encoder-decoder neural model. MEGAN is inspired by models that express a chemical reaction as a sequence of graph edits, akin to the arrow pushing formalism. We extend this model to retrosynthesis prediction (predicting substrates given the product of a chemical reaction) and scale it up to large data sets. We argue that representing the reaction as a sequence of edits enables MEGAN to efficiently explore the space of plausible chemical reactions, maintaining the flexibility of modeling the reaction in an end-to-end fashion and achieving state-of-the-art accuracy in standard benchmarks. Code and trained models are made available online at https//github.com/molecule-one/megan.The past 20 years have seen an extensive implementation of nickel in homogeneous catalysis through the development of unique reactivity not easily achievable by using noble transition metals. Many catalytic cycles propose Ni(I) complexes as potential reactive intermediates, yet the scarcity of nickel(I) precursors and the lack of a general, non-ligand-specific protocol for their synthesis have hampered progress in this field of research. This has in turn also limited the access to novel, well-defined Ni(I) species for the development of new catalytic reactions. Herein, we report a simple, general route to access a wide variety o

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