MDSVDNV: predicting microbe-drug associations by singular value decomposition and Node2vec.

Introduction: Recent researches have demonstrated that microbes are crucial for the growth and development of the human body, the movement of nutrients, and human health. Diseases may arise as a result of disruptions and imbalances in the microbiome. The pathological investigation of associated diseases and the advancement of clinical medicine can both benefit from the identification of drug-associated microbes. Methods: In this article, we proposed a new prediction model called MDSVDNV to infer potential microbe-drug associations, in which the Node2vec network embedding approach and the singular value decomposition (SVD) matrix decomposition method were first adopted to produce linear and non-linear representations of microbe interactions. Results and discussion: Compared with state-of-the-art competitive methods, intensive experimental results demonstrated that MDSVDNV could achieve the best AUC value of 98.51% under a 5-fold CV, which indicated that MDSVDNV outperformed existing competing models and may be an effective method for discovering latent microbe-drug associations in the future.

Formylation and Bromination of Pyrrolo[2,1-a]isoquinoline Derivatives with Bromoisobutyrate and Dimethyl Sulfoxide.

We have developed an efficient formylation of pyrroloisoquinolines using bromoisobutyrate and dimethyl sulfoxide as carbonyl reagent. Various formylated pyrroloisoquinolines could be prepared in good yields (up to 94%). This formylation process can be easily scaled up to gram scale with good yield. In most cases of pyrroloisoquinolines without methoxy groups, the combination of bromoisobutyrate and dimethyl sulfoxide could act as a bromination reagent, delivering brominated pyrroloisoquinolines in acceptable to good yields (up to 82%).