Lemminggravesen8413

The successful rhodium-catalyzed asymmetric hydroformylation and hydroaminomethylation of α-substituted acrylamides is described using 1,3-phosphite-phosphoramidite ligands based on a sugar backbone. learn more A broad scope of chiral aldehydes and amines were afforded in high yields and excellent enantioselectivities (up to 99%). Furthermore, the synthetic potential of this method is demonstrated by the single-step synthesis of the brain imaging molecule RWAY.O-Acetylated sialic acid has been found in the Neisseria meningitidis serogroup W (NmW) capsular polysaccharide (CPS) and is a required structural component of clinically used NmW CPS-based polysaccharide and polysaccharide-conjugate vaccines. The role of sialic acid O-acetylation in NmW CPS, however, is not clearly understood. This is partially due to the lack of a precise control of the percentage and the location of O-acetylation which is labile and susceptible to migration. We explore chemoenzymatic synthetic strategies for preparing N-acetylated analogues of O-acetylated NmW CPS oligosaccharides which can serve as structurally stable probe mimics. Substrate specificity studies of NmW CPS polymerase (NmSiaDW) identified 4-azido-4-deoxy-N-acetylmannosamine (ManNAc4N3) and 6-azido-6-deoxy-N-acetylmannosamine (ManNAc6N3) as suitable chemoenzymatic synthons for synthesizing N-acetyl analogues of NmW CPS oligosaccharides containing 7-O-acetyl-N-acetylneuraminic acid (Neu5,7Ac2) and/or 9-O-acetyl-N-acetylneuraminic acid (Neu5,9Ac2). The synthesis was achieved by NmSiaDW-dependent sequential one-pot multienzyme (OPME) strategy with in situ generation of the corresponding sugar nucleotides from simple monosaccharides or derivatives to form N3-oligosaccharides which were converted to the desired NAc-oligosaccharides by an efficient one-step chemical transformation.A redox-neutral C2-selective methylation of heterocyclic N-oxides with sulfonium ylides is described herein. This report presents unprecedented findings for the utility of sulfonium ylides as the methylation source of N-heterocycles beyond the Corey-Chaykovsky reaction. Intriguingly, pyrrolidine plays a significant role in minimizing the reductive C2-methylation process. This method is characterized by its mild conditions, simplicity, and excellent site selectivity. The applicability of the developed protocol is showcased by the late-stage methylation and sequential transformations of complex drug molecules.A general, convenient, and friendly route for preparing a versatile building block of isocyanides from primary amines is developed. Difluorocarbene, generated in situ from decarboxylation of chlorodifluoroacetate, reacts efficiently with primary amines to produce isocyanides. Various primary amines are well tolerated, including aryl, heteroaryl, benzyl, and alkyl amines, as well as amine residues in amino acids and peptides. Late-stage functionalization of biologically active amines is demonstrated, showing its practical capacity in drug design and peptide modification.The protocol for simple, efficient, and mild synthesis of oxazolyl sulfonyl fluorides was developed through Rh2(OAc)4-catalyzed annulation of methyl-2-diazo-2-(fluorosulfonyl)acetate (MDF) or its ethyl ester derivative with nitriles. This practical method provides a general and direct route to a unique class of highly functionalized oxazolyl-decorated sulfonyl fluoride warheads with great potential in medicinal chemistry, chemical biology, and drug discovery.This work considers strategies to develop accurate and reliable graph neural networks (GNNs) for molecular property predictions. Prediction performance of GNNs is highly sensitive to the change in various parameters due to the inherent challenges in molecular machine learning, such as a deficient amount of data samples and bias in data distribution. Comparative studies with well-designed experiments are thus important to clearly understand which GNNs are powerful for molecular supervised learning. Our work presents a number of ablation studies along with a guideline to train and utilize GNNs for both molecular regression and classification tasks. First, we validate that using both atomic and bond meta-information improves the prediction performance in the regression task. Second, we find that the graph isomorphism hypothesis proposed by [Xu, K.; et al How powerful are graph neural networks? 2018, arXiv1810.00826. arXiv.org e-Print archive. https//arxiv.org/abs/1810.00826] is valid for the regression task. Surprisingly, however, the findings above do not hold for the classification tasks. Beyond the study on model architectures, we test various regularization methods and Bayesian learning algorithms to find the best strategy to achieve a reliable classification system. We demonstrate that regularization methods penalizing predictive entropy might not give well-calibrated probability estimation, even though they work well in other domains, and Bayesian learning methods are capable of developing reliable prediction systems. Furthermore, we argue the importance of Bayesian learning in virtual screening by showing that well-calibrated probability estimation may lead to a higher success rate.We present an optimized density-functional tight-binding (DFTB) parameterization for iron-based complexes based on the popular trans3d set of parameters. The transferability of the original and optimized parameterizations is assessed using a set of 50 iron complexes, which include carbonyl, cyanide, polypyridine, and cyclometalated ligands. DFTB-optimized structures predicted using the trans3d parameters show a good agreement with both experimental crystal geometries and density functional theory (DFT)-optimized structures for Fe-N bond lengths. Conversely, Fe-C bond lengths are systematically overestimated. We improve the accuracy of Fe-C interactions by truncating the Fe-O repulsive potential and reparameterizing the Fe-C repulsive potential using a training set of six isolated iron complexes. The new trans3d*-LANLFeC parameter set can produce accurate Fe-C bond lengths in both geometry optimizations and molecular dynamics (MD) simulations, without significantly affecting the accuracy of Fe-N bond lengths. Moreover, the potential energy curves of Fe-C interactions are considerably improved.