Synthesis of Chiral Bisoxazoline Ligands: (3aR,3a'R,8aS,8a'S)-2,2'-(cyclopropane-1,1-diyl)bis(3a,8a-dihydro-8H-indeno[1,2-d]oxazole)

A. Bis((3aR,8aS)-3a,8a-dihydro-8H-indeno[1,2-d]oxazol-2-yl)methane (3) . An oven-dried 2-L three-necked, round-bottomed flask equipped with a 6.5 cm × 2.0 cm Teflon-coated elliptical stir bar is fitted with a thermometer, a reflux condenser and a rubber septum. The system is connected to a continuous nitrogen flow and then charged with (1R,2S)-(+)-cis-1-amino-2-indanol (1, 22.2 g, 149 mmol, 2.1 equiv), diethyl malonimidate dihydrochloride (2, 16.4 g, 71 mmol, 1 equiv), and 1 L of dichloromethane (Note 2). The system is heated to 45 °C (internal temperature 43 °C) under an atmosphere of nitrogen in an oil bath for 18 h, stirring at 600 rpm. Reaction progress is monitored by ¹H NMR (Note 3) (Figure 1)

Enantioselective Electroreductive Coupling of Alkenyl and Benzyl Halides via Nickel Catalysis

An electrochemically driven enantioselective nickel-catalyzed reductive cross-coupling of alkenyl bromides and benzyl chlorides is reported. The reaction forms products bearing allylic stereogenic centers with good enantioselectivity under mild conditions in an undivided cell. Electrochemical activation and turnover of the catalyst mitigate issues posed by metal powder reductants. This report demonstrates that enantioselective Ni-catalyzed cross-electrophile couplings can be driven electrochemically

Multilabel Classification Models for the Prediction of Cross-Coupling Reaction Conditions

Machine-learned ranking models have been developed for the prediction of substrate-specific cross-coupling reaction conditions. Data sets of published reactions were curated for Suzuki, Negishi, and C–N couplings, as well as Pauson–Khand reactions. String, descriptor, and graph encodings were tested as input representations, and models were trained to predict the set of conditions used in a reaction as a binary vector. Unique reagent dictionaries categorized by expert-crafted reaction roles were constructed for each data set, leading to context-aware predictions. We find that relational graph convolutional networks and gradient-boosting machines are very effective for this learning task, and we disclose a novel reaction-level graph attention operation in the top-performing model

Graph Neural Networks for the Prediction of Substrate-Specific Organic Reaction Conditions

We present a systematic investigation using graph neural networks (GNNs) to model organic chemical reactions. To do so, we prepared a dataset collection of four ubiquitous reactions from the organic chemistry literature. We evaluate seven different GNN architectures for classification tasks pertaining to the identification of experimental reagents and conditions. We find that models are able to identify specific graph features that affect reaction conditions and lead to accurate predictions. The results herein show great promise in advancing molecular machine learning

Multilabel Classification Models for the Prediction of Cross-Coupling Reaction Conditions

Machine-learned ranking models have been developed for the prediction of substrate-specific cross-coupling reaction conditions. Data sets of published reactions were curated for Suzuki, Negishi, and C–N couplings, as well as Pauson–Khand reactions. String, descriptor, and graph encodings were tested as input representations, and models were trained to predict the set of conditions used in a reaction as a binary vector. Unique reagent dictionaries categorized by expert-crafted reaction roles were constructed for each data set, leading to context-aware predictions. We find that relational graph convolutional networks and gradient-boosting machines are very effective for this learning task, and we disclose a novel reaction-level graph attention operation in the top-performing model

Graph Neural Networks for the Prediction of Substrate-Specific Organic Reaction Conditions

We present a systematic investigation using graph neural networks (GNNs) to model organic chemical reactions. To do so, we prepared a dataset collection of four ubiquitous reactions from the organic chemistry literature. We evaluate seven different GNN architectures for classification tasks pertaining to the identification of experimental reagents and conditions. We find that models are able to identify specific graph features that affect reaction conditions and lead to accurate predictions. The results herein show great promise in advancing molecular machine learning.Comment: 23 pages, 10 tables, 13 figures, to appear in the ICML 2020 Workshop on Graph Representation Learning and Beyond (GRLB

Nickel-Catalyzed Asymmetric Reductive Cross-Coupling of a-Chloroesters with (Hetero)Aryl Iodides

An asymmetric reductive cross-coupling of alpha-chloroesters and (hetero)aryl iodides is reported. This nickel-catalyzed reaction proceeds with a chiral BiOX ligand under mild conditions, affording alpha-arylesters in good yields and enantioselectivities. The reaction is tolerant of a variety of functional groups, and the resulting products can be converted to pharmaceutically-relevant chiral building blocks. A multivariate linear regression model was developed to quantitatively relate the influence of the alpha-chloroester substrate and ligand on enantioselectivity

Calcium-Dependent Protein Kinase 5 Is Required for Release of Egress-Specific Organelles in Plasmodium falciparum

ABSTRACT The human malaria parasite Plasmodium falciparum requires efficient egress out of an infected red blood cell for pathogenesis. This egress event is highly coordinated and is mediated by several signaling proteins, including the plant-like P. falciparum calcium-dependent protein kinase 5 (PfCDPK5). Knockdown of PfCDPK5 results in an egress block where parasites are trapped inside their host cells. The mechanism of this PfCDPK5-dependent block, however, remains unknown. Here, we show that PfCDPK5 colocalizes with a specialized set of parasite organelles known as micronemes and is required for their discharge, implicating failure of this step as the cause of the egress defect in PfCDPK5-deficient parasites. Furthermore, we show that PfCDPK5 cooperates with the P. falciparum cGMP-dependent kinase (PfPKG) to fully activate the protease cascade critical for parasite egress. The PfCDPK5-dependent arrest can be overcome by hyperactivation of PfPKG or by physical disruption of the arrested parasite, and we show that both treatments facilitate the release of the micronemes required for egress. Our results define the molecular mechanism of PfCDPK5 function and elucidate the complex signaling pathway of parasite egress