ABSTRACT
Nucleophilic index (NNu) as a significant parameter plays a crucial role in screening of amine catalysts. Indeed, the quantity and variety of amines are extensive. However, only limited amines exhibit an NNu value exceeding 4.0 eV, rendering them potential nucleophiles in chemical reactions. To address this issue, we proposed a computational method to quickly identify amines with high NNu values by using Machine Learning (ML) and high-throughput Density Functional Theory (DFT) calculations. Our approach commenced by training ML models and the exploration of Molecular Fingerprint methods as well as the development of quantitative structure-activity relationship (QSAR) models for the well-known amines based on NNu values derived from DFT calculations. Utilizing explainable Shapley Additive Explanation plots, we were able to determine the five critical substructures that significantly impact the NNu values of amine. The aforementioned conclusion can be applied to produce and cultivate 4920 novel hypothetical amines with high NNu values. The QSAR models were employed to predict the NNu values of 259 well-known and 4920 hypothetical amines, resulting in the identification of five novel hypothetical amines with exceptional NNu values (>4.55 eV). The enhanced NNu values of these novel amines were validated by DFT calculations. One novel hypothetical amine, H1, exhibits an unprecedentedly high NNu value of 5.36 eV, surpassing the maximum value (5.35 eV) observed in well-established amines. Our research strategy efficiently accelerates the discovery of the high nucleophilicity of amines using ML predictions, as well as the DFT calculations.
Subject(s)
Amines , Density Functional Theory , High-Throughput Screening Assays , Machine Learning , Quantitative Structure-Activity Relationship , Amines/chemistry , Models, MolecularABSTRACT
Postoperative pancreatic fistula (POPF) is a common and dreaded complication after pancreaticoduodenectomy (PD). The gut microbiota has been considered as an crucial mediator of postoperative complications, however, the precise roles of gut microbiota in POPF are unclear. A prospective study was developed to explore the effects of somatostatin on gut microbiota and we aim to identify the microbial alterations in the process of POPF. A total of 45 patients were randomly divided into PD group or additional somatostatin therapy group. The fecal sample of each patient was collected preoperatively and postoperatively and the gut microbiota was analyzed by 16S rRNA sequencing. Our study found that somatostatin therapy was independent risk factor for the occurrence of POPF, and it reduced the microbial diversity and richness in patients. At genus level, somatostatin therapy led to a decreased abundance in Bifidobacterium, Subdoligranulum and Dubosiella, whereas the abundance of Akkermansia, Enterococcus and Enterobacter were increased. The abundance levels of certain bacteria in the gut microbiota have significantly shifted in patients with POPF. The LEfSe analysis revealed that Ruminococcaceae could be used as microbial markers for distinguishing patients with high risk of POPF. Furthermore, Verrucomicrobia and Akkermansia could be used as preoperative biomarkers for identifying patients without POPF. Our prospective study highlights the specific communities related with somatostatin therapy and discovers POPF-associated microbial marker, which suggests that gut microbiota may become a diagnostic biomarker and potential therapeutic target for POPF.
ABSTRACT
[This corrects the article DOI: 10.1038/s41420-020-00329-4.].