The invention belongs to the technical field of drug crystal analysis, and particularly relates to a drug crystal structure landscape analysis system and a landscape analysis method thereof. The drug crystal structure landscape analysis system calls a cloud computing interface to calculate the energy of input crystals through an algorithm deployed in the cloud in advance, and an energy-density space group landscape array diagram of the crystals is generated according to the computation results returned; and analysis is selectively carried out as needed, result reports arc analyzed and summarized as a final report, and the final report is converted into a text document. The drug crystal structure landscape analysis system and the landscape analysis method thereof satisfy the drug crystal structure analysis requirements in the new technology background, and can analyze a large quantity of crystals which are formed by a certain drug molecule and have different structures.

Disclosed is a target-based drug screening method using inverse quantitative structure-(drug)performance relationships (QSPR) analysis and molecular dynamics simulation. The method includes modeling a molecular structure of a test compound group against a target molecule, obtaining a quantitative structure-(drug)performance relationships (QSPR) of the test compound group, acquiring the optimal pharmacophore of a novel target-based drug through a numerical inversion of the QSPR, and selecting drug candidates having a molecular structure similar to the optimum pharmacophore from the test compound group.

Disclosed is a target-based drug screening method using inverse quantitative structure-(drug)performance relationships (QSPR) analysis and molecular dynamics simulation. The method includes modeling a molecular structure of a test compound group against a target molecule, obtaining a quantitative structure-(drug)performance relationships (QSPR) of the test compound group, acquiring the optimal pharmacophore of a novel target-based drug through a numerical inversion of the QSPR, and selecting drug candidates having a molecular structure similar to the optimum pharmacophore from the test compound group.

A chemical pattern recognition method for evaluating the quality of a traditional Chinese medicine based on medicine effect information, comprising: collecting chemical information of a traditional Chinese medicine sample, obtaining medicine effect information reflecting a clinical therapeutic effect thereof, performing spectrum-effect relationship analysis on the chemical information and the medicine effect information, and obtaining an index significantly related to the medicine effect as a feature chemical index; dividing the traditional Chinese medicine sample into a training set and a test set; using a pattern recognition method to extract a feature variable from samples of the training set by taking the feature chemical index as an input variable; building a pattern recognition model using the feature variable; and substituting feature variable values of samples of the test set into the model, and completing chemical pattern recognition evaluation of the quality of the traditional Chinese medicine. According to the method, chemical reference substances are not needed, the chemical pattern recognition model is built on the basis of the feature chemical index reflecting the medicine effect, the one-sidedness and the subjectivity of the existing standards are overcome, and a traditional Chinese medicine quality evaluation system capable of reflecting both the clinical therapeutic effect and overall chemical composition information is finally formed.

A chemical pattern recognition method for evaluating the quality of a traditional Chinese medicine based on medicine effect information, comprising: collecting chemical information of a traditional Chinese medicine sample, obtaining medicine effect information reflecting a clinical therapeutic effect thereof, performing spectrum-effect relationship analysis on the chemical information and the medicine effect information, and obtaining an index significantly related to the medicine effect as a feature chemical index; dividing the traditional Chinese medicine sample into a training set and a test set; using a pattern recognition method to extract a feature variable from samples of the training set by taking the feature chemical index as an input variable; building a pattern recognition model using the feature variable; and substituting feature variable values of samples of the test set into the model, and completing chemical pattern recognition evaluation of the quality of the traditional Chinese medicine. According to the method, chemical reference substances are not needed, the chemical pattern recognition model is built on the basis of the feature chemical index reflecting the medicine effect, the one-sidedness and the subjectivity of the existing standards are overcome, and a traditional Chinese medicine quality evaluation system capable of reflecting both the clinical therapeutic effect and overall chemical composition information is finally formed.

A method and system for diagnosing a condition of an air-breathing aircraft engine are described. The method comprises obtaining a sample of lubricating fluid from the engine, filtering the sample to obtain a plurality of particles from the lubricating fluid, obtaining chemical composition data for the plurality of particles, determining a quantity of volcanic ash in the lubricating fluid by considering each one of the particles as composed partially of volcanic ash and partially of at least one other material and determining a first percentage of surface area of the particles covered by the volcanic ash and a second percentage of the surface area of the particles covered by the at least one other material, the volcanic ash having associated thereto a predetermined chemical composition, and diagnosing a condition of the engine based on the quantity of volcanic ash found in the lubricating fluid.

Volatile organic compounds classification by receiving test data associated with detecting volatile organic compounds (VOCs), analyzing the test data according to a set of data features associated with known VOCs, determining a match between each feature of the test data and a corresponding feature of the set of data features, yielding a set of matches, defining a first degree of anomaly for the test data according to the set of matches, and classifying the test data according to the first degree of anomaly.

Volatile organic compounds classification by receiving test data associated with detecting volatile organic compounds (VOCs), analyzing the test data according to a set of data features associated with known VOCs, determining a match between each feature of the test data and a corresponding feature of the set of data features, yielding a set of matches, defining a first degree of anomaly for the test data according to the set of matches, and classifying the test data according to the first degree of anomaly.

A method may include: analyzing each of a group of inorganic particles to generate data about physicochemical properties of each of the inorganic particles; and generating a correlation between a reactivity index of each of the inorganic particles and the data.

A method may include: analyzing each of a group of inorganic particles to generate data about physicochemical properties of each of the inorganic particles; and generating a correlation between a reactivity index of each of the inorganic particles and the data.