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.

A non-transitory computer-readable recording medium stores a learning program that causes a computer to execute a machine learning process for graph data. The machine learning process includes: generating, from graph data to be subjected to learning, extended graph data where at least some of nodes included in the graph data have a value of the nodes and a value corresponding to presence or absence of an indefinite element at the nodes; and obtaining input tensor data by performing tensor decomposition of the generated extended graph data, performing deep learning with a neural network by inputting the input tensor data into the neural network upon deep learning, and learning a method of the tensor decomposition.

A non-transitory computer-readable recording medium stores a learning program that causes a computer to execute a machine learning process for graph data. The machine learning process includes: generating, from graph data to be subjected to learning, extended graph data where at least some of nodes included in the graph data have a value of the nodes and a value corresponding to presence or absence of an indefinite element at the nodes; and obtaining input tensor data by performing tensor decomposition of the generated extended graph data, performing deep learning with a neural network by inputting the input tensor data into the neural network upon deep learning, and learning a method of the tensor decomposition.

In a method of molecular scaffold hopping an interface of a scheduler computer sends instructions, prepared by the scheduler computer, to a job runner computer to perform a plurality of separate computational tasks. Each of the separate computational tasks includes calculating one or more chemical properties for a query molecule or molecules in a library of molecules. One or more of the plurality of separate computational tasks performed on the job runner computer are preemptible computing instances. Status indicators sent from the job runner computer are received by the interface for each of the plurality of separate computational tasks. The indicators are one of: incomplete, completed, or failed computing instances. The interface resends the instructions to the job runner computer that correspond to the separate computational tasks having the failed computing instance indicator to increase fault-tolerance against the separate computational tasks not attaining the completed computing instance indicator.

A computer system and computational method for determining optimal solid-state methods for synthesis of an inorganic material that results in an output of recommended synthetic methods that can be implemented based on the recommendation. The method involves inputting a target inorganic material, querying structural data and thermodynamic data for the target inorganic material and reactant inorganic materials that can be used for its synthesis, enumerating possible synthetic reactions to construct a synthesis reaction database with a viable subset of the possible synthetic methods. The program generates a nucleation barrier metric and a competition metric that are combined to provide a recommendation of the synthetic procedures to the target inorganic material.

A computer system and computational method for determining optimal solid-state methods for synthesis of an inorganic material that results in an output of recommended synthetic methods that can be implemented based on the recommendation. The method involves inputting a target inorganic material, querying structural data and thermodynamic data for the target inorganic material and reactant inorganic materials that can be used for its synthesis, enumerating possible synthetic reactions to construct a synthesis reaction database with a viable subset of the possible synthetic methods. The program generates a nucleation barrier metric and a competition metric that are combined to provide a recommendation of the synthetic procedures to the target inorganic material.

A method and apparatus for monitoring and evaluation of a production of a functional material, wherein an assessment of steps taken by users based on a data basis results in reporting to the user of the extent to which predetermined properties of a functional material produced are attained in the event of variances in the steps taken.

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 sequencing method, system and kit of low molecular weight heparin (LMWH) oligosaccharides are provided. The sequencing method includes: a sample preparation step: isolating or preparing a group of LMWH oligosaccharide mixture samples; a sample treatment step: performing complete enzymatic digestion and nitrous acid degradation on the LMWH oligosaccharide mixture samples to obtain an enzymatically digested eight-common-heparin-disaccharide array, a 3-O-sulfate group array, a 1,6-anhydro structure array, a nitrous acid degradation array, respectively; a data processing step: obtaining a disaccharide isomeric unit array according to the enzymatically digested eight-common-heparin-disaccharide array and the nitrous acid degradation array; a sequence database building step: building a sequence database according to the degree of polymerization of the oligosaccharide mixture, the disaccharide isomeric unit array, the 3-O-sulfate group array, and the 1,6-anhydro structure array; and a specific result output step: screening the sequence database according to input qualification information and then outputting a specific result file.