A systematic review of state-of-the-art strategies for machine learning-based protein function prediction.

New drug discovery is inseparable from the discovery of drug targets, and the vast majority of the known targets are proteins. At the same time, proteins are essential structural and functional elements of living cells necessary for the maintenance of all forms of life. Therefore, protein functions have become the focus of many pharmacological and biological studies.

A systematic review on the state-of-the-art strategies for protein representation.

The study of drug-target protein interaction is a key step in drug research. In recent years, machine learning techniques have become attractive for research, including drug research, due to their automated nature, predictive power, and expected efficiency. Protein representation is a key step in the study of drug-target protein interaction by machine learning, which plays a fundamental role in the ultimate accomplishment of accurate research.

Phase Ⅰ and phase Ⅱ metabolic studies of Citrus flavonoids based on electrochemical simulation and in vitro methods by EC-Q-TOF/MS and HPLC-Q-TOF/MS.

The oxidation products and metabolic pathways of five Citrus flavonoids were studied by online electrochemical/quadrupole time-of-flight mass spectrometry (EC/Q-TOF/MS). The simulated oxidation metabolism of target compounds in phase I and phase Ⅱ was carried out at boron-doped diamond (BDD) working electrode. The results obtained by EC-MS were compared with the conventional metabolism of rats and humans reported in previous literatures.

A Rapid and Sensitive Method for the Simultaneous Determination of Multipolar Compounds in Plant Tea by Supercritical Fluid Chromatography Coupled to Ion Mobility Quadrupole Time-of-Flight Mass Spectrometry.

In this study, matrix solid phase dispersion (MSPD) microextraction combined with supercritical fluid chromatography-ion mobility quadrupole time-of-flight mass spectrometry (SFC/IM-QTOF-MS) was used to analyze the multipolar compounds in plant tea. The parameters of stationary phase, mobile phase, make-up solution, temperature, and back pressure were optimized. The target analytes were gradient eluted in 8 min by supercritical CO on a Zorbax RX-SIL column.

Ion pair-based mobile phase additives to improve the separation of alkaloids in supercritical fluid chromatography.

In this study, a supercritical fluid chromatography (SFC) method based on ion pair reagents was used to separate alkaloids. The chromatographic parameters, including the stationary phase, additive type, additive concentration, outlet pressure, temperature and flow rate, were optimized. Baseline separation was completed in 20 min on an Agilent Pursuit 5 PFP column (4.

Boron nitride nanosheet-assisted matrix solid-phase dispersion microextraction of alkaloids from lotus plumule by high-performance liquid chromatography coupled with ultraviolet detection and ion mobility quadrupole time-of-flight mass spectrometry.

A boron nitride nanosheet (BNNS)-assisted matrix solid-phase dispersion method was established to microextract alkaloids from medicinal plants. The target compounds were identified by high-performance liquid chromatography coupled with ultraviolet detection and ion mobility quadrupole time-of-flight mass spectrometry. During the experimental process, several important parameters, including the type of dispersant, the amount of dispersant, the grinding time, and the type of elution solvent, were optimized.

Carbonized biosorbent assisted matrix solid-phase dispersion microextraction for active compounds from functional food.

In this study, mangosteen peel based activated carbon was prepared and first applied as adsorbent in matrix solid-phase dispersion (MSPD) for simultaneously extraction of flavonoids from Dendrobium huoshanense prior to their separation and determination by ultra-high performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry (UHPLC-Q-TOF/MS). The MSPD-UHPLC-Q-TOF/MS method was validated exhaustively. Good linearities (r ≥ 0.