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

The present invention relates to a fresh water acute criteria prediction method based on a quantitative structure-activity relationship for metals. An unknown toxic endpoint of a metal is predicted according to a quantitative relationship between structural characteristics of heavy metal ions and acute toxicity effects of aquatic organisms, and hazard concentrations for protecting the aquatic organisms of different proportions are derived from sensitivity distribution analysis on different species. The fresh water acute criteria prediction method is a method for establishing a metal toxicity predictive model by integrating physicochemical structural parameters of heavy metals and toxic mechanisms of different aquatic organisms and applying the metal toxicity predictive model to prediction of an unknown criteria reference value.

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 polycyclic aromatic hydrocarbon property / toxicity prediction method using an intelligent support vector machine. The method, based on measured polycyclic aromatic hydrocarbon molecule structures and the quantitative structure-activity relationship technology, establishes a polycyclic aromatic hydrocarbon environmental index prediction module and a polycyclic aromatic hydrocarbon carcinogenicity prediction module and employs the support vector machine algorithm, thereby realizing handing of problems of small samples, nonlinearity and high dimension. The method also optimizes the models by using the grid search method, the genetic algorithm, and the particle swarm algorithm, thereby preventing parameter influence and further improving the accuracy of the models. The method can predict the property and toxicity of unknown polycyclic aromatic hydrocarbons rapidly by using intelligent optimized support vector machine; compared with conventional toxic test experiments, the method increases the test efficiency; compared with the conventional statistical prediction method, the method improves the generalization; compared with normal algorithms, the method prevents parameter influence, realizes programming and can provide referable decisional proof for environmental evaluation of polycyclic aromatic hydrocarbons.

The invention discloses a method for distinguishing the activities of a complete agonist, a partial agonist and an antagonist of a peroxisome proliferator-activated receptor gamma. The method comprises: modeling of the receptor and a ligand; molecular docking; simulation of conventional molecular dynamics; simulation of well-tempered meta-dynamic molecular dynamics; and construction of a pharmacophore. The method disclosed by the invention can quickly distinguish the activities of the PPARs gamma of different structural compounds, so that use of chemical drugs and cells in a conventional toxicity experiment process in a laboratory is greatly reduced, the workload in the laboratory is relieved, and expenses on the laboratory are saved; therefore, the activities of the PPARs gamma of the compounds are distinguished before QSAR (quantitative structure-activity relationship) modeling, and a QSAR model result is closer to an actual model; and a traditional QSAR is used more widely.

The invention discloses a detection method of the self-accelerating decomposition temperature of organic peroxide. The detection method comprises the following steps: (1) collecting and classifying common organic peroxide; (2) optimizing the structure of the same type of organic peroxide, and calculating the chemical structure parameter of the organic peroxide; (3) constructing a SADT (Self Accelerating Decomposition Temperature) prediction model and the evaluation of the model; (4) carrying out internal authentication on the constructed model; (5) defining the optimal applicable range of the model; and (6) carrying out quick analysis and prediction on the organic peroxide at an unknown self-accelerating decomposition temperature. Through a QSAR (Quantitative Structure Activity Relationship) model method, the self-accelerating decomposition temperature of the unknown organic peroxide is detected, the situation of parameter shortage is solved, the deficiency of the high danger coefficient of the traditional method is overcome, and the accuracy of the obtained self-accelerating decomposition temperature parameter is improved. The method is safe, simple in operation, short in period, uniform in period and low in dependency and greatly reduces the loss of financial resources and material resources.

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.

The invention relates to a method for predicting affinity of the interaction between a biomarker p53 and an organic phosphate fire retardant. The method includes the following steps of establishing a quantitative structure-activity relationship QSAR model of the affinity KD and training set compound molecule descriptors through a partial least square method so as to obtain an affinity KD value, and predicting the affinity according to the affinity KD value. By the adoption of the method, interaction between the organic phosphate fire retardant and the biomarker p53 which are of different structures can be predicted, the method is low in cost and easy, convenient and rapid to achieve, and a large amount of labor, a large number of expenses and a large amount of time which are needed by experimental tests can be saved; the prediction result obtained through the method can provide the important data support for organic chemical ecological risk assessment and management, and the method has important theoretical and practical significance.

The invention discloses a model method for predicting reaction rate constant (kOH) of organic matter in atmosphere and hydroxyl. On the basic of obtaining a compound molecular structure, through a multiple linear regression method, a kOH model with 298K and temperature adherence is built, and a kOH value of organic compounds under 298K or other temperature can be predicted rapidly and efficiently; the goodness of fit, robustness and prediction capacity of the model are represented according to the guide rule, about the building and verification of the QSAR model, of the OECD. The application area of the model is definite, and compounds of nearly 900 different kinds are included; the prediction model is simple and is programmed easily, and significance data support can be provided for evaluation of environment durability of organic chemicals.

The invention relates to a building method of an anthocyanin antioxidant activity three-dimensional quantitative structure-activity relationship model. An anthocyanin antioxidant with a known activity is taken as a study object, a technology of three-dimensional quantitative structure-activity relationship is used, an anthocyanin antioxidant three-dimensional quantitative structure-activity relationship model is built, an accurate structure-activity relationship model is built through a molecular force field method and a molecular similarity coefficient analytical method for analyzing technologies of molecular conformation optimization, parameter optimization and the like. The building method of the anthocyanin antioxidant activity three-dimensional quantitative structure-activity relationship model can quickly predict activity value of an unknown activity compound, and can reasonably interpret the relation between the size of the nthocyanin antioxidant activity and the structure characteristics of the nthocyanin antioxidant activity.

The invention discloses a novel nonlinear high-efficient model building method and application of phenols for toxicity prediction and evaluation of photogenic bacteria. A non-linear SVR (Sustained Virological Response) technology is utilized to carry out QSAR (Quantitative Structure Activity Relationship) research on the toxicity of the photogenic bacteria represented as logEC50 through eighteen phenol compounds in the prior art. The work aims to look for a more reliable QSAR model with more reasonable characteristics through a nonlinear chemometric tool based on low-dimension characteristic data and high-dimension characteristic data, and analyze in detail the most valuable model relative to the toxicity and the most important molecular characteristic thereof. The novel nonlinear high-efficient model building method and the application of the phenols for the toxicity prediction and evaluation of the photogenic bacteria provide effective theoretical references for the design of phenol analogues for strengthening or weakening the toxicity of the photogenic bacteria.

The invention discloses a method for predicting organic chemical biodegradability according to a logistic regression algorithm. According to the method for predicting organic chemical biodegradability, on the basis that the molecular structure of a compound is obtained, a person just needs to calculate descriptors of representational structure characteristics and use a built quantitative structure-activity relationship (QSAR) model, and accordingly the biodegradability of the organic compound can be fast and efficiently predicted. The method for predicting organic chemical biodegradability is low in cost, and easy and convenient and fast to adopt, and saves large required labor sources, cost and time. According to the method for predicting organic chemical biodegradability, modeling completely accords with the QSAR model building and guidelines for use of the Organization for Economic Co-operation and Development (OECD), only 14 molecular structure descriptors are adopted, the logistic regression method which is clear and transparent in algorithm is applied, and therefore the method for predicting organic chemical biodegradability is easy to understand and apply. Model application fields are explicit, and 1629 kinds of compounds are covered. The method for predicting organic chemical biodegradability according to the logistic regression method has good fitting effect, robustness and prediction ability, can effectively predict biodegradability of a plurality of organic compounds and provide important data support to organic chemical risk assessment and management, and has important significance in ecological risk assessment.