Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining Practical Machine Learning Tools and Techniques

The Huisgen 1,3-dipolar cycloaddition reaction of organic azides and alkynes has gained considerable attention in recent years due to the introduction in 2001 of Cu(1) catalysis by Tornoe and Meldal, leading to a major improvement in both rate and regioselectivity of the reaction, as realized independently by the Meldal and the Sharpless laboratories. The great success of the Cu(1) catalyzed reaction is rooted in the fact that it is a virtually quantitative, very robust, insensitive, general, and orthogonal ligation reaction, suitable for even biomolecular ligation and in vivo tagging or as a polymerization reaction for synthesis of long linear polymers. The triazole formed is essentially chemically inert to reactive conditions, e.g. oxidation, reduction, and hydrolysis, and has an intermediate polarity with a dipolar moment of ∼5 D. The basis for the unique properties and rate enhancement for triazole formation under Cu(1) catalysis should be found in the high ∆G of the reaction in combination with the low character of polarity of the dipole of the noncatalyzed thermal reaction, which leads to a considerable activation barrier. In order to understand the reaction in detail, it therefore seems important to spend a moment to consider the structural and mechanistic aspects of the catalysis. The reaction is quite insensitive to reaction conditions as long as Cu(1) is present and may be performed in an aqueous or organic environment both in solution and on solid support.

Cu-catalyzed azide-alkyne cycloaddition.

KEGG(Kyoto Encyclopedia of Genes and Genomes)〔和文〕 (特集 ゲノム医学の現在と未来--基礎と臨床) -- (データベース)

Degeneration and regeneration of the nervous system

The development of mild, efficient, and enantioselective methods for preparing chiral building blocks from simple, renewable carbon units has been a long-term goal of the sustainable chemical industry. Mandelate derivatives are valuable pharmaceutical intermediates and chiral resolving agents, but their manufacture relies heavily on highly toxic cyanide. Herein, we report (S)-4-hydroxymandelate synthase (HmaS)-centered biocatalytic cascades for the synthesis of mandelates from benzaldehydes and glycine. We show that HmaS can be engineered to perform (R)-selective hydroxylation by single-point mutation, empowering the stereo-divergent synthesis of both (S)- and (R)-mandelate derivatives. We demonstrated that these biocatalytic cascades enabled the production of various mandelate derivatives with great atom economy, excellent yields (up to 98%) and enantiomeric excess (up to >99%). This methodology offers an effective cyanide-free technology for greener and sustainable production of mandelate derivatives.

Asymmetric C1 Extension of Aldehydes through Biocatalytic Cascades for Stereodivergent Synthesis of Mandelic Acids.

Aldehyde oxidase (AOX) is a molybdoenzyme that is primarily expressed in the liver and is involved in the metabolism of drugs and other xenobiotics. AOX-mediated metabolism can result in unexpected outcomes, such as the production of toxic metabolites and high metabolic clearance, which can lead to the clinical failure of novel therapeutic agents. Computational models can assist medicinal chemists in rapidly evaluating the AOX metabolic risk of compounds during the early phases of drug discovery and provide valuable clues for manipulating AOX-mediated metabolism liability. In this study, we developed a novel graph neural network called AOMP for predicting AOX-mediated metabolism. AOMP integrated the tasks of metabolic substrate/non-substrate classification and metabolic site prediction, while utilizing transfer learning from 13C nuclear magnetic resonance data to enhance its performance on both tasks. AOMP significantly outperformed the benchmark methods in both cross-validation and external testing. Using AOMP, we systematically assessed the AOX-mediated metabolism of common fragments in kinase inhibitors and successfully identified four new scaffolds with AOX metabolism liability, which were validated through in vitro experiments. Furthermore, for the convenience of the community, we established the first online service for AOX metabolism prediction based on AOMP, which is freely available at https://aomp.alphama.com.cn.

/pdf/transfer-learning-enhanced-graph-neural-network-for-aldehyde-3uqxvtqk.pdf

Transfer Learning Enhanced Graph Neural Network for Aldehyde Oxidase Metabolism Prediction and Its Experimental Application

Nitrene-Mediated Multicomponent Couplings and Macrocyclization by CH-Functionalization


 DNA accumulation is associated with the development of autoimmune inflammatory diseases. However, the pathological role and underlying mechanism of cytoplasmic DNA accumulation in CD4+ T cells have not been well established. Here, we show that Trex1 deficiency-induced endogenous DNA accumulation in CD4+ T cells greatly promoted their induction of autoimmune inflammation in a lupus-like mouse model. Mechanistically, the accumulated DNA in CD4+ T cells was sensed by the KU complex, then triggered the activation of DNA-PKcs and ZAK and further facilitated the activation of AKT, which exacerbated glycolysis, thereby promoting the inflammatory responses. Accordingly, blocking the DNA sensing pathway in CD4+ T cells by genetic knockout of Zak or using our newly developed ZAK inhibitor iZAK2 attenuated all pathogenic characteristics in a lupus-like inflammation mouse model induced with Trex1-deficient CD4+ T cells. Overall, our study demonstrated a causal link between DNA-sensing and metabolic reprogramming in CD4+ T cells for inflammatory induction and suggested inhibition of the DNA sensing pathway may be a potential therapy for the treatment of inflammatory diseases.

/pdf/cytoplasmic-dna-sensing-boosts-cd4-t-cell-metabolism-for-3pv27qqw.pdf

Mingyue Zheng

Papers

Asymmetric C1 Extension of Aldehydes through Biocatalytic Cascades for Stereodivergent Synthesis of Mandelic Acids.

Transfer Learning Enhanced Graph Neural Network for Aldehyde Oxidase Metabolism Prediction and Its Experimental Application

Nitrene-Mediated Multicomponent Couplings and Macrocyclization by CH-Functionalization

Cytoplasmic DNA sensing boosts CD4+ T cell metabolism for inflammatory induction

Popular Music Production Trend Analysis and Prediction Research