34 results about "Quantitative structure–activity relationship" patented technology

The invention relates to the field of toxic substance prediction in an environment, in particular to a QSAR (Quantitative Structure Activity Relationship) toxicity prediction method for evaluating the health effect of nano-crystalline metal oxide. The method comprises the steps of predicting the toxicity endpoint of unknown metallic oxide according to a quantitative relation between structural features and cytotoxicity effect of the nano-crystalline metal oxide; building a nano-crystalline metal oxide prediction model by combining the physicochemical structure parameter and a special mechanism of toxication of the nano-crystalline metal oxide, and applying the nano-crystalline metal oxide prediction model to predict the toxicity endpoint of the unknown metallic oxide. According to the QSAR toxicity prediction method provided by the invention, based on a function model and the toxicity prediction method of the nano-crystalline metal oxide, and the nano-crystalline metal oxide prediction model can be built to predict an unknown toxicity value through the QSAR model method, therefore the toxicity endpoint prediction of various compounds lack of toxicity data can be completed quickly and simply with less dependency.

The invention relates to a method for predicting the properties / toxicity of polycyclic aromatic hydrocarbons by using an intelligent support vector machine. According to the measured molecular structure of polycyclic aromatic hydrocarbons, the method uses quantitative structure-activity relationship technology to establish a polycyclic aromatic hydrocarbon cyclization index prediction model and multiple The carcinogenicity prediction model of cycloaromatic hydrocarbons, using the support vector machine algorithm, realizes the processing of small sample, nonlinear and high-dimensional problems. The grid search method, genetic algorithm, and particle swarm optimization algorithm are used to optimize the model, which avoids the influence of parameters and further increases the accuracy of the model. The invention can quickly predict the properties and toxicity of unknown polycyclic aromatic hydrocarbons by using the intelligent optimization support vector machine, improves the test efficiency compared with the traditional toxicological test experiment, and improves the generalization ability compared with the traditional statistical prediction method. Compared with the normal algorithm, the influence of parameters is avoided. It realizes programming and can provide a reference decision-making basis for the environmental assessment of polycyclic aromatic hydrocarbons.