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567results about "Cheminformatics programming languages" patented technology

Stochastic method to determine, in silico, the drug like character of molecules

A stochastic algorithm has been developed for predicting the drug-likeness of molecules. It is based on optimization of ranges for a set of descriptors. Lipinski's “rule-of-5”, which takes into account molecular weight, logP, and the number of hydrogen bond donor and acceptor groups for determining bioavailability, was previously unable to distinguish between drugs and non-drugs with its original set of ranges. The present invention demonstrates the predictive power of the stochastic approach to differentiate between drugs and non-drugs using only the same four descriptors of Lipinski, but modifying their ranges. However, there are better sets of 4 descriptors to differentiate between drugs and non-drugs, as many other sets of descriptors were obtained by the stochastic algorithm with more predictive power to differentiate between databases (drugs and non-drugs). A set of optimized ranges constitutes a “filter”. In addition to the “best” filter, additional filters (composed of different sets of descriptors) are used that allow a new definition of “drug-like” character by combining them into a “drug like index” or DLI. In addition to producing a DLI (drug-like index), which permits discrimination between populations of drug-like and non-drug-like molecules, the present invention may be extended to be combined with other known drug screening or optimizing methods, including but not limited to, high-throughput screening, combinatorial chemistry, scaffold prioritization and docking.
Owner:YISSUM RES DEV CO OF THE HEBREWUNIVERSITY OF JERUSALEM LTD

Method, device and equipment for identifying isomerism graph and property of molecular spatial structure

The invention discloses a method and a device for identifying isomerism graph and property corresponding to a molecular space structure, and machine equipment. The method comprises the following steps: carrying out feature description containing a topological structure on a heterogeneous graph to generate feature information; through sampling the isomerism graph and the feature information, generating a feature vector, corresponding to a key node, on a topological structure contained in the isomerism graph; performing aggregation processing on the feature vectors to generate graph representation vectors corresponding to the isomerism graphs; and according to the graph representation vector, performing classification processing on the isomerism graph to obtain a classification prediction result. Therefore, the identification of the graphical representation corresponding to the heterogeneous graph, such as the molecular spatial structure, can be realized through the neural network, the analysis and processing performance related to the isomerism graph is effectively improved, the neural network is not limited to the classification and identification of network data any more, and the application scene is greatly expanded.
Owner:TENCENT TECH (SHENZHEN) CO LTD +1
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