SeO₂-Mediated Oxidative Transposition of Pauson–Khand Products

Oxidative transpositions of bicyclic cyclopentenones mediated by selenium dioxide (SeO₂) are disclosed. Treatment of Pauson–Khand reaction (PKR) products with SeO₂ in the presence or absence of water furnishes di- and trioxidized cyclopentenones, respectively. Mechanistic investigations reveal multiple competing oxidation pathways that depend on substrate identity and water concentration. Functionalization of the oxidized products via cross-coupling methods demonstrates their synthetic utility. These transformations allow rapid access to oxidatively transposed cyclopentenones from simple PKR products

3D Computer Vision Models Predict DFT-Level HOMO-LUMO Gap Energies from Force-Field-Optimized Geometries

We investigate 3D deep learning methods for predicting quantum mechanical energies at high-theory-level accuracy from inexpensive, rapidly computed molecular geometries. Using space-filled volumetric representations (voxels), we explore the effects of radial decay from atom centers and rotational data augmentation on learnability. We test several published computer vision models for 3D shape learning, and construct our own architecture based on 3D inception networks with physically meaningful kernels. We provide a framework for further studies and propose a modeling challenge for the computer vision and molecular machine learning communities

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

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

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

Genomic and Proteomic Studies on the Mode of Action of Oxaboroles against the African Trypanosome

SCYX-7158, an oxaborole, is currently in Phase I clinical trials for the treatment of human African trypanosomiasis. Here we investigate possible modes of action against Trypanosoma brucei using orthogonal chemo-proteomic and genomic approaches. SILAC-based proteomic studies using an oxaborole analogue immobilised onto a resin was used either in competition with a soluble oxaborole or an immobilised inactive control to identify thirteen proteins common to both strategies. Cell-cycle analysis of cells incubated with sub-lethal concentrations of an oxaborole identified a subtle but significant accumulation of G2 and >G2 cells. Given the possibility of compromised DNA fidelity, we investigated long-term exposure of T. brucei to oxaboroles by generating resistant cell lines in vitro. Resistance proved more difficult to generate than for drugs currently used in the field, and in one of our three cell lines was unstable. Whole-genome sequencing of the resistant cell lines revealed single nucleotide polymorphisms in 66 genes and several large-scale genomic aberrations. The absence of a simple consistent mechanism among resistant cell lines and the diverse list of binding partners from the proteomic studies suggest a degree of polypharmacology that should reduce the risk of resistance to this compound class emerging in the field. The combined genetic and chemical biology approaches have provided lists of candidates to be investigated for more detailed information on the mode of action of this promising new drug clas

Hexose-6-phosphate Dehydrogenase Modulates 11β-Hydroxysteroid Dehydrogenase Type 1-Dependent Metabolism of 7-keto- and 7β-hydroxy-neurosteroids

BACKGROUND: The role of 11beta-hydroxysteroid dehydrogenase type 1 (11beta-HSD1) in the regulation of energy metabolism and immune system by locally reactivating glucocorticoids has been extensively studied. Experiments determining initial rates of enzyme activity revealed that 11beta-HSD1 can catalyze both the reductase and the dehydrogenase reaction in cell lysates, whereas it predominantly catalyzes the reduction of cortisone to cortisol in intact cells that also express hexose-6-phosphate dehydrogenase (H6PDH), which provides cofactor NADPH. Besides its role in glucocorticoid metabolism, there is evidence that 11beta-HSD1 is involved in the metabolism of 7-keto- and 7-hydroxy-steroids; however the impact of H6PDH on this alternative function of 11beta-HSD1 has not been assessed. METHODOLOGY: We investigated the 11beta-HSD1-dependent metabolism of the neurosteroids 7-keto-, 7alpha-hydroxy- and 7beta-hydroxy-dehydroepiandrosterone (DHEA) and 7-keto- and 7beta-hydroxy-pregnenolone, respectively, in the absence or presence of H6PDH in intact cells. 3D-structural modeling was applied to study the binding of ligands in 11beta-HSD1. PRINCIPAL FINDINGS: We demonstrated that 11beta-HSD1 functions in a reversible way and efficiently catalyzed the interconversion of these 7-keto- and 7-hydroxy-neurosteroids in intact cells. In the presence of H6PDH, 11beta-HSD1 predominantly converted 7-keto-DHEA and 7-ketopregnenolone into their corresponding 7beta-hydroxy metabolites, indicating a role for H6PDH and 11beta-HSD1 in the local generation of 7beta-hydroxy-neurosteroids. 3D-structural modeling offered an explanation for the preferred formation of 7beta-hydroxy-neurosteroids. CONCLUSIONS: Our results from experiments determining the steady state concentrations of glucocorticoids or 7-oxygenated neurosteroids suggested that the equilibrium between cortisone and cortisol and between 7-keto- and 7-hydroxy-neurosteroids is regulated by 11beta-HSD1 and greatly depends on the coexpression with H6PDH. Thus, the impact of H6PDH on 11beta-HSD1 activity has to be considered for understanding both glucocorticoid and neurosteroid action in different tissues

RNA interference-mediated c-MYC inhibition prevents cell growth and decreases sensitivity to radio- and chemotherapy in childhood medulloblastoma cells

BACKGROUND: With current treatment strategies, nearly half of all medulloblastoma (MB) patients die from progressive tumors. Accordingly, the identification of novel therapeutic strategies remains a major goal. Deregulation of c-MYC is evident in numerous human cancers. In MB, over-expression of c-MYC has been shown to cause anaplasia and correlate with unfavorable prognosis. METHODS: To study the role of c-MYC in MB biology, we down-regulated c-MYC expression by using small interfering RNA (siRNA) and investigated changes in cellular proliferation, cell cycle analysis, apoptosis, telomere maintenance, and response to ionizing radiation (IR) and chemotherapeutics in a representative panel of human MB cell lines expressing different levels of c-MYC (DAOY wild-type, DAOY transfected with the empty vector, DAOY transfected with c-MYC, D341, and D425). RESULTS: siRNA-mediated c-MYC down-regulation resulted in an inhibition of cellular proliferation and clonogenic growth, inhibition of G1-S phase cell cycle progression, and a decrease in human telomerase reverse transcriptase (hTERT) expression and telomerase activity. On the other hand, down-regulation of c-MYC reduced apoptosis and decreased the sensitivity of human MB cells to IR, cisplatin, and etoposide. This effect was more pronounced in DAOY cells expressing high levels of c-MYC when compared with DAOY wild-type or DAOY cells transfected with the empty vector. CONCLUSION: In human MB cells, in addition to its roles in growth and proliferation, c-MYC is also a potent inducer of apoptosis. Therefore, targeting c-MYC might be of therapeutic benefit when used sequentially with chemo- and radiotherapy rather than concomitantly

The CMS Phase-1 pixel detector upgrade

The CMS detector at the CERN LHC features a silicon pixel detector as its innermost subdetector. The original CMS pixel detector has been replaced with an upgraded pixel system (CMS Phase-1 pixel detector) in the extended year-end technical stop of the LHC in 2016/2017. The upgraded CMS pixel detector is designed to cope with the higher instantaneous luminosities that have been achieved by the LHC after the upgrades to the accelerator during the first long shutdown in 2013–2014. Compared to the original pixel detector, the upgraded detector has a better tracking performance and lower mass with four barrel layers and three endcap disks on each side to provide hit coverage up to an absolute value of pseudorapidity of 2.5. This paper describes the design and construction of the CMS Phase-1 pixel detector as well as its performance from commissioning to early operation in collision data-taking.Peer reviewe