126 results about "Quantitative structure" patented technology

An improvement in a method for quantitative modulated imaging to perform depth sectioned reflectance or transmission imaging in a turbid medium, such as human or animal tissue is directed to the steps of encoding periodic pattern of illumination preferably with a fluorescent excitation wavelength when exposing a turbid medium to the periodic pattern to provide depth-resolved discrimination of structures within the turbid medium; and reconstructing a non-contact three dimensional image of the structure within a turbid medium. As a result, wide field imaging, separation of the average background optical properties from the heterogeneity components from a single image, separation of superficial features from deep features based on selection of spatial frequency of illumination, or qualitative and quantitative structure, function and composition information is extracted from spatially encoded data.

The invention discloses a method for fast predicting organic pollutant n-caprylic alcohol / air distribution coefficient based on molecular structure, belonging to the technical field of quantifying structure / active relationship (QSAR) facing to the environmental risk evaluation. The method is characterized of comprising the steps of: adopting the molecular structure of atomic center fragment characterization compound; and screening the atomic center fragment combination by means of stepwise regression and partial least-squares regression, to build a group contribution model for predicting KOA.The internal authentication and the external authentication improves that the built KOA group contribution model has stability and predicting capability, and a range and distance method and a probability density method express the application domain of the group contribution model, thereby defining the application range of the model and guaranteeing the predict accuracy. The method has the effectsand benefits of being capable of fast predicting the KOA of the high flux compound, obtaining the KOA with low cost, being helpful for obtaining the high flux KOA data, and having a significant meaning for the environment supervision and the risk evaluation of chemicals.

The invention discloses a theoretical prediction method for organic chemical n-octyl alcohol / air distribution coefficient (KOA) and belongs to the field of ecological risk assessment testing strategy. The method comprises the following steps of: establishing a quantitative structure-activity relationship (QSAR) based on a molecular Dragon descriptor of a compound and calculating free melting energy based on a thermodynamic principle by adopting an open source solvent model, and transforming to obtain the KOA according to a thermodynamic principle formula of logKOA=-deltaGOA / 2.303RT. A general strategy of predicting the KOA of the compound is provided based on the method, namely whether the molecule is in the application range is judged according to the Dragon descriptor, if so, a QSAR model is preferentially adopted (QSAR-T is adopted at different temperatures), otherwise, the compound is predicted by adopting an SM8AD solvent model. The method and strategy are adopted and accorded, the KOA of different compounds at different temperatures can be rapidly and effectively predicted, lots of manpower, material resources and financial resources are saved, and important essential data is provided for large-scale ecological risk assessment and environment supervision of chemicals.

The invention discloses an optimal control system and method for three-dimensional bio-printing aquogel supports. The system comprises a quantitative visualization device for the three-dimensional bio-printing aquogel supports based on optical coherence tomography scanning and three-dimensional bio-printing equipment; the visualization device comprises a light source, a low-coherence interference module, a sample scanning module, an interference signal detecting module and a computer. According to the method, the quantitative structure characteristics of the three-dimensional bio-printing aquogel supports are obtained through the OCT technology, the designing and the printing of the supports are fed back on the basis of the results of the quantitative structure characteristics, and the stability and the controllability of three-dimensional manufacturing of the supports are improved by lowering the difference between the designing and the printing through iteration. According to the invention, the requirements of optimal control of three-dimensional bio-printing aquogel for high resolution and wide-range and three-dimensional fast scanning can be met, non-destructive and non-intrusive fast imaging of the whole supports can be realized through the OCT technology, and the information of the whole supports and local shape features of space is quantitatively analyzed based on the algorithm automatically selecting object regions.

The invention discloses a method for predicting fish bio-concentration factors of organic chemicals by the quantitative structure-activity relationship, and belongs to the field of ecological risk assessment and test strategies. According to the method, bio-concentration factor data of 780 types of organic compounds are collected from public databases or published papers; molecular structures of the organic compounds are optimized according to the density functional theory, and 4885 types of molecule descriptors of the organic compounds are preliminarily screened on the basis of the optimized molecular structures to acquire 3480 molecule descriptors; the organic compounds are divided into a training set and a verification set according to a ratio of 4:1, the training set is used for creating a predication model, and the verification set is used for external verification after model creation. The method has the advantages that the model is clear in application field and covers new pollutants, has good imitative effect, robustness and predication capability, and can effectively predict bio-concentration factors of different types of organic compounds; predication results of the method can provide important data support for risk assessment and management of the organic chemicals and are of great significance in ecological risk assessment.

The invention discloses a variable selection method for modeling an organic pollutant quantitative structure and activity relationship. The method comprises the following steps of: calculating linear models combined with all single variables and different bivariables, and retaining a certain number of optimal models for the single variables and the bivariables; then sequentially taking out a model from the retained bivariable linear models, and combining two of the variables and each of the rest variables to form a tri-variable model until all the retained bivariable models are processed; comparing the quality of the tri-variable models, and retaining a certain number of optimal tri-variable models; and repeating, and stopping calculation until the number of variables forming the models meets the requirement, wherein the quality of the models is based on an end standard represented by q2 or a root-mean-square deviation (RMSEV) which is calculated by leave-one-out cross validation (LOOCV) or leave-multiple-out cross validation (LMOCV). The theory is simple and can be understood easily and programmed easily; and the method is quick and effective, so that the rationality of variable selection and the stability of the forecast capacity of the models are guaranteed.

The invention discloses a method for adopting a quantitative structure-activity relationship model to predicting soil or sediment adsorption coefficients of an organic compound. On the basis that a molecular structure of the organic compound is known, the soil or sediment adsorption coefficients of the organic compound can be quickly and efficiently predicted only by calculating a molecular descriptor with the molecular structure and applying a built QSAR (quantitative structure-activity relationship) model. The method is simple, quick, low in cost and capable of saving manpower, material resources and financial resources needed for experiment testing. Modeling is performed according to guidelines on building and using the QSAR model of the Organization for Economic Cooperation and Development, and a simple and transparent multiple linear regression analysis method is applied, so that easiness in understanding and applying is realized; the method has clear application domain, good fitting capacity, robustness and predicting capability; by the method, the soil or sediment adsorption coefficients of the organic compound in the application domain can be effectively predicted, and necessary basic data are provided for ecological risk evaluation and management of the compound; the method has important significance.

The invention is applicable for the field of the signal processing technology and provides a sigma-delta modulator. The sigma-delta modulator comprises a first-class modulation unit, a second-class modulation unit and a noise shaping unit, wherein the first-class modulation unit is used for transforming input analog signals into first-class digital signals; the first-class digital signals comprise quantization noise and multi-bit DAC (Digital-to-Analog Converter) nonlinear noise; the second-class modulation unit is connected with the first-class modulation unit so as to transform the quantization noise and the multi-bit DAC nonlinear noise in the first-class digital signals into second-class digital signals, and feedback the second-class digital signals to the first-class modulation unit; the noise shaping unit is connected with the first-class modulation unit and the second-class modulation unit so as to cancel the quantization noise and the multi-bit DAC nonlinear noise in the second-class digital signals and the first-class digital signals, obtain digital signals without noises and output the digital signals. The sigma-delta modulator counteracts the last-class multi-bit DAC nonlinear errors and upper-class quantization noises in a cascade structure through combining inter-stage feedback with a double quantitative structure.

The invention relates to a quantitative feeding device. The quantitative feeding device is arranged at the bottom part of a feed barrel; the lower end of a discharge port at the bottom part of the feed barrel is provided with a guiding barrel and a quantitative tray; the upper end and the lower end of the quantitative tray are provided with an opening, the upper end is provided with a positioning plate, and the quantitative tray is communicated with the guiding barrel through the positioning plate; the lower end of the quantitative barrel is provided with a material cover plate corresponding to the position of the guiding barrel, a central shaft of the quantitative tray is provided with a central supporting block, and the quantitative tray together with the material cover plate and the central supporting block forms a material receiving space; and the upper end of the central supporting block is provided with a driving device for driving the quantitative tray to rotate. The quantitative feeding device has a simple structure and is labor-saving and time-saving; and compared with the spiral conveying structure or other quantitative structures, the quantitative feeding device is accurate in quantifying, has less or no feed waste, and is suitable for automatic quantitative feeding in large-sized farms or feeding ponds; the quantitative feeding device quantitatively feeds through the rotary sprinkling feeding of the positioning tray, and is suitable for synchronous growth of aquatic products and livestock and poultry; the positioning plate and the material cover plate are easy to dismount and convenient to clear and replace; and the use cost and the wastage cost are low.

The invention discloses a method for adopting a quantitative structure-activity relation model to predict a reaction rate constant of chlorine free radical of organic chemicals. According to the method, a quantum chemistry descriptor with structural characters is figured out only by means of basic molecular structure information of the organic chemicals, and by adopting a constructed QSAR prediction model, a kCl value of the organic chemicals can be predicted quickly and efficiently. The method follows a guide rule issued for the construction and verification of a QSAR model by the Organization For Economic Co-Operation And Development (OECD). According to the method, by adopting a genetic algorithm-multiple linear stepwise regression analysis combination use method (GA-MLR) and a supportvector machine-multiple linear stepwise regression analysis combination use method (SVM-MLR), the transparency degree is high and more convenience is provided for application; since quantum chemistrydescriptors are adopted in all GA-MLR models, the physical meaning of the descriptors is clear; the method has a specific application domain, and is applicable to abundant organic varieties, excellentin goodness of fit, robustness and prediction capability, easy to program and capable of supplying important data support to environmental risk evaluation and management of the organic chemicals.

The invention relates to a fault diagnosis system sensor optimal configuration method based on a quantitative structure model. The method comprises the following steps: 1) establishing a structure model of a fault diagnosis system through a structure analysis method, wherein the structure model is represented by two matrixes, that is an analytical redundant relation matrix and a fault feature matrix, and the structure model can qualitatively describe the fault diagnosis system; 2) detecting nondeterminacy with the structure model of the fault diagnosis system and sensors being combined to obtain quantitative description of the fault diagnosis system and realize the purpose of quantitatively analyzing the fault diagnosis system, and establishing a calculation formula for fault diagnosis system key performance indicators with the detection of nondeterminacy being taken into consideration; 3) establishing a sensor optimization model of the fault diagnosis system; and 4) solving the optimization model through a genetic algorithm. The sensor optimal configuration method for the fault diagnosis system can effectively save the cost of the sensors of the fault diagnosis system, and the configuration result accords with engineering practice situations better.

The invention relates to a pesticide sprayer with good mixing effect and quantitative spraying function, which comprises a casing, a fixed block and four wheels. The casing is provided with a feed tank, a water tank, a water pump, a mixing mechanism and a quantitative mechanism. The fixed block There is a rotating mechanism on it. The mixing mechanism includes a stirring unit and at least two feeding units. The quantitative mechanism includes a conveying unit, an extrusion unit and a recovery unit. The pesticide sprayer with good mixing effect and quantitative spraying function, through the mixing mechanism, Not only can a single pesticide be sprayed, but also a variety of pesticides can be uniformly mixed and sprayed, which enhances the control effect on crop diseases. Not only that, through the quantitative structure, the pesticide sprayer can accurately control the spraying of the pesticide solution per unit area Quantitative spraying of pesticides can save the amount of pesticides and protect the environment on the premise of ensuring the control effect of crop diseases.

The invention discloses a leave-multiple-out cross validation (LMOCV) method of a quantitative structure and activity relationship (QSAR) model of an organic pollutant. In the method, a uniform design method is integrated with LMOCV; related coefficients of uniform design optimized LMOCV (UDOLMOCV) serve as variable screening ending standards; meanwhile, the model is subjected to sample internal cross validation by using the UDOLMOCV during validation in the model; higher sample volatility is provided by using predictive capacity judgment indexes; validated samples are uniformly distributed in sample spaces by infrequent sampling validation of the sample; and the sample which is selected every time has quite high representativeness. The validated sample which is obtained by the method has quite high representativeness of sample distribution, so the defect of singleness of sample selection of a Monte Carlo method is overcome. The invention can be used for validating the stability and predicative capacity of the QSAR model and can be used for discovering and determining instability factors in the model.

The invention discloses a method for representing activity relationships of antibacterial proteins or polypeptides. The method is characterized in that starting from 85 structural parameters and topological indexes of representation molecules, a characteristic descriptor comprising 166 amino acid systems is obtained through principle component analysis (PCA), namely a principle component score vector of structural and topological variable (VSTPV).. The VSTPV is applied to study of the quantitative structure-activity relationships (QSAR) of 101 antibacterial cationic peptides and 28 hydrolyzed bovine lactoferricins (BPPs), the models have better fitting and forecasting capabilities, and the cross validation related coefficients and related coefficients are above 0.70. The study shows that the VSTPV can well represent coded and uncoded amino acids and establish polypeptides with stronger forecasting capabilities and derivative QSAR models thereof.

This invention provides a method for predicting pharmacokinetic properties of molecules comprising the steps of: (a) preparing 2D-structures of molecules used as a training set; (b) constructing a 2D-fingerprint by counting the number of structural descriptors that potentially relate to a pharmacokinetic property, either manually or automatically using internally developed macro; wherein said structural descriptors consist of predefined 20 to 80 atoms / fragments or substructures; (c) analyzing the obtained 2D-fingerprint by a statistical analysis method to correlate with the pharmacokinetic property of the molecule to yield a quantitative structure-property relationship (QSPR) model; and (d) calculating the pharmacokinetic property of a trial molecule using the above obtained QSPR model. A system for this invention is also provided. According to this method and system, it is possible to predict pharmacokinetic properties of molecules prior to synthesis, without labor-intensive and time-consuming experimentation.

The invention discloses a centrifugal double-channel trace liquid quantitative sampling device and a manufacturing method thereof, and relates to a sampling structure for centrifugal double-channel trace liquid quantitative sampling. The device solves the problems that the trace liquid quantitative sampling structure in the conventional centrifugal micro-fluidic chip is a single liquid quantitative structure, has complex manufacturing process and low integration precision, needs secondary centrifugation during use and has fussy operation processes. The device consists of a PDMS substrate and a glass sheet, wherein the PDMS substrate is stuck on the surface of the glass sheet; the surface of the PDMS substrate is provided with two trace liquid quantitative sampling units; each liquid quantitative sampling unit comprises an inverse V-shaped liquid quantitative cavity, a liquid breaking air inlet channel, a waste liquid cavity, a liquid outlet channel and the like; one end of each inverse V-shaped liquid quantitative cavity is communicated with the waste liquid cavity; and the other end of the inverse V-shaped liquid quantitative cavity is communicated with the liquid outlet channel. The device is applicable in the quantitative sampling field for bio-chemically detecting trace liquid in the centrifugal micro-fluidic chip.

The invention discloses an oligonucleotide retention time prediction method which comprises the following steps that: (a) based on a main component analysis method, a DNA sequence structure characterization method i.e. the score of alkali-based three-dimensional nature is established; (b) the score of the alkali-based three-dimensional nature characterizes the structures of oligonucleotides; (c) the characterization variables of each oligonucleotide are normalized through a self-crossing covariance method; (d) nature parameters which are closely related to the chromatogram retention index of the oligonucleotide are picked through genetic algorithm; and (e) RBF-SVR is applied to build an oligonucleotide retention time prediction model so as to validate the prediction capability of the model respectively through self-replacement test, LOO interactive test and external test. The method can be used for predicting the oligonucleotide retention time and oligonucleotide quantitative structure retention relationship analysis, and optimize and select appropriate chromatographic conditions and parameters.

The invention discloses an A beta aggregation inhibitor. The aggregation inhibitor is characterized in that: the amino acid sequence of the aggregation inhibitor is amino terminal-IYVLIY-carboxyl terminal. The peptide sequence is designed based on a quantitative structure function relational model of peptide and is obtained through technical detection of microscope scan. The aggregation inhibitor has a good effect of inhibiting the aggregation of the toxic body, namely A beta capable of causing Alzheimer's disease and can be further developed into a medicament for treating the Alzheimer's disease.

The invention discloses a method for predicting acute toxicity of an organophosphorous flame retardant on a zebra fish based on QSAR / QEcoSAR method combination and belongs to the technical field of quantitative structure-activity relationship (QSAR) for chemical environment supervision. The method comprises the steps that based on the chemical reaction principle and the minimum energy principle, QSAR and QEcoSAR are combined, and a multivariate linear regression method is adopted to construct a QSAR model of LC50 and training set compound molecule descriptors (electronegativity, chemical hardness, chemical force, eletrophilicity and other characteristics). The method is low in cost and convenient and fast to use, and a large amount of manpower, cost and time needed in experimental testingcan be saved; and by use of the prediction result, important data support can be provided for ecological risk evaluation and management of organophosphorous chemicals, and therefore the method has important realistic significance and theoretical value.

The invention discloses a QSAR (Quantitative Structure-Activity Relationships) model constructed based on comprehensive toxicity action mode classification for predicting acute toxicity of an organic compound to a daphnia magna. At first, a comprehensive toxicity action mode classification method is developed, and the method is adopted to classify the compound based on structure characteristics. After the compound is classified according to the method, an EC50 model is constructed on the basis of a molecular structure descriptor of the compound through a multiple linear regression method, so that an EC50 value of acute effective toxicity of the organic compound to the daphnia magna can be quickly and effectively predicted; and the goodness of fit, the robustness and the predictive ability of the model are represented according to construction and verification guidelines of an organization for economic cooperation and development about the QSAR model; the model application domain is clear; the prediction model is simple and easy to be programmed, and an important data support is provided for acute toxicity assessment of organic chemicals.

The invention discloses a method for optimizing reliability of structural noise based on a dimension-by-dimension analysis strategy. The method comprises following steps: firstly, establishing a structural noise optimizing model based on an interval reliability analysis model by using a quantitative structure for a model of interval value and environment and other uncertainties, determining orders and gauss integral points based on least-squares approximation responding to interval parameters by utilizing structural acoustics to response to nonlinearity degree related to interval parameters; secondly, utilizing gauss integral points and a quantitative model for interval numbers to sample parameter vectors of intervals, calculating response vector value at interval parameter sample points, establishing the best square approximation in order to determine the max-minimum point matrix such that response interval vectors can be calculated; and finally, utilizing response interval vectors and safety requirement to calculate reliability in intervals and finishing structural optimization under drive of the optimizing algorithm. The method for optimizing reliability of structural noise based on the dimension-by-dimension analysis strategy has following beneficial effects: by replacing classic safety factor with interval reliability, the method echoes the fine development trend of optimizing structural noise; conservativeness of a conventional optimizing method is effectively avoided; and the method has a broad application prospect.

The present invention is related to a quantitative structure-based affinity scoring method for peptide / protein complexes. More specifically, the present invention comprises a method that operates on the basis of a highly specific force field function (e.g. CHARMM) that is applied to all-atom structural representations of peptide / receptor complexes. Peptide side-chain contributions to total affinity are scored after detailed rotameric sampling followed by controlled energy refinement. The method of the invention further comprises a de novo approach to estimate dehydration energies from the simulation of individual amino acids in a solvent box filled with explicit water molecules and applying the same force field function as used to evaluate peptide / receptor complex interactions.

The invention provides a method for predicting the wear resistance of lubricating base oil according to chemical structures. The method includes steps of 1), generating three-dimensional chemical structures of molecules of the lubricating base oil; 2), minimizing energy of the three-dimensional chemical structures; 3), computing an EVA (evaluation of an infrared vibration-based descriptor) parameter of each three-dimensional chemical structure; 4), preprocessing wear surface data of a friction pair sample after the lubricating base oil is applied to the friction pair sample; 5), performing regression on the EVA parameters and the wear surface data by means of partial least squares, and establishing a relationship between the preprocessed wear surface data and the EVA parameters so as to create a quantitative prediction model; 6), performing cross validation on the prediction model; 7), predicting the wear area of the friction pair sample according to the created prediction model when the lubricating base oil is applied to the friction pair sample in an experimental state. The method has the advantages that a computer-aided design process is introduced into the field of lubricating oil design for the first time on the basis of the quantitative structure-tribo-ability relationship, the method is beneficial to reducing lubricating oil design risks and research cost, and the lubricating oil development efficiency can be greatly improved.

The invention relates to a method for constructing a three-dimensional quantitative structure activity relationship model of a B-cell lymphoma-2 (Bcl-2) protein inhibitor and application of the method and belongs to the technical field of biological information. The method comprises the following steps of: according to the known small molecule inhibitor, establishing the three-dimensional quantitative structure activity relationship model of the Bcl-2 protein inhibitor by using a three-dimensional quantitative structure activity relationship technology; and further improving the accuracy of the model by using technologies for analyzing molecular similarity, optimizing molecular conformation, optimizing parameters and the like. By the invention, the Bcl-2 protein binding constants of an active unknown compound can be quickly predicted, and clues of the active compound are acquired within short time. Compared with the conventional high-throughput screening technology, the invention has the advantages of greatly improving screening efficiency and reducing cost.

The invention belongs to the technical field of compound performance evaluation, and discloses a machine learning estimation method for sensitivity and mechanical properties of energetic substances and a relationship between the sensitivity and the mechanical properties of the energetic substances. The estimation method comprises the following steps: constructing a quantitative structure-activityrelationship model of impact sensitivity and bulk modulus of seven nitro energetic substances based on an artificial neural network and a method for determining independent screening and sparse operators by taking a molecular descriptor and molecular structure information calculated by E-Dragon as characteristics; and determining the relationship between the impact sensitivity and the mechanical property of the nitro energetic substance and the quantitative relationship between the impact sensitivity and the mechanical property of the nitro energetic substances respectively with the molecularstructure by utilizing the constructed quantitative structure-activity relationship model of the impact sensitivity and the bulk modulus of the nitro energetic substances. According to the method, seven QSPR models of the impact sensitivity and the bulk modulus of the nitro nitro energetic substances are established on the basis of molecular descriptors calculated by EDragon and several common molecular structure information, so that the process of experimental research on energetic materials is shortened, and design and comprehensive evaluation of novel energetic compounds are facilitated.

The invention relates to a construction method and application for a food-borne polypeptide antioxidant activity three-dimensional quantitative structure-activity relationship model. The method comprises the steps of using a food-borne polypeptide with known activity as a research object, utilizing the three-dimensional quantitative structure-activity relationship technology for establishing the food-borne polypeptide antioxidant activity three-dimensional quantitative structure-activity relationship model, and adopting the MM+ method the semi-empirical quantum chemistry method AM1, the DFT method and other quantum chemistry technologies to obtain quantum chemistry parameters. According to the construction method, analysis and statistics are carried out on the food-borne polypeptide quantum chemistry parameters, the antioxidant activity sites of the food-borne polypeptide are determined, and quantitative structure-activity model is constructed by utilizing the bond length, charge distribution, frontier molecular orbit energy parameters of the activity sites and food-borne polypeptide antioxidant activity values. The constructed model can rapidly and accurately predict the activity value of the food-borne polypeptide with the unknown activity, and the relationship between the antioxidant activity and structural features can be reasonably explained; compared with a traditional high throughput screening technology, the screening efficiency is greatly improved, and the cost is reduced.

The invention provides a material structure quantitative analysis method based on a transmission electron microscope HAADF image. The method is characterized by obtaining the coordinate position of each atom lattice in a transmission electron microscope high-resolution image through the image processing technique to obtain integral intensity of each atom and neighbourhood normalized intensity; and by fully combining the relation between HAADF imaging principle and intensity information, obtaining some quantitative information on the basis of qualitative research of the HAADF image. Through the data of the atomic lattice coordinates and normalized intensity and the like, a plurality of quantitative structure information can be obtained through analysis. In the method, the determination of the atom lattice coordinates and integral intensity is scientific and reasonable; application of the neighbourhood normalized intensity avoids the influence due to the case that the material (especially the polycrystal nanometer material) has larger range of structure fluctuation and defects; and the structure information can be summarized and analyzed quickly, and enormous workloads and errors due to manual measurement are prevented. The method has the advantages of accurate result, wide application range and high operability and the like.

The invention relates to a method and a system for automatically constructing a QSPR model of a material. According to the method, a machine learning method is utilized; based on a data set formed bythe crystal structures of a target material system and target property data, a QSPR model of the material is trained in an automatic loop iteration manner; the target material system is subjected to crystal structure regulation and control, so structure sample space is enriched; crystal structure properties generated after regulation and control can be automatically calculated through first principles, and a training set is expanded; and the QSPR model is further trained and testing is further conducted until a test result of the QSPR model reaches preset precision or the crystal structure sample space is exhaustive. The method is characterized in that sample data is automatically generated, self-learning is carried out, and a structure-property prediction model meeting certain predictionprecision is automatically trained.

The invention discloses a method for predicting the elimination rate coefficient of a gas state sulfur compound on the low-temperature hydrolysis condition by building a Quantitative Structure-Activity Relationship (QSAR) model, and belongs to the technical field of quantitative structure-activity relationships of ecological risk assessment. The elimination rate coefficient of the gas state sulfur compound on the low-temperature hydrolysis condition can be rapidly and efficiently predicted only calculating a molecule descriptor with the structural feature and applying the built QSAR model on the basis of the known gas state sulfur compound molecule structure. The method is simple, rapid and low in cost, and labor, material resources and financial resources needed by experimental tests can be saved. The simple and transparent multiple linear regression method is applied and is easy to understand and apply. The method has the specific application field, and good fitting ability, robustness and predictive ability, and can effectively predict the elimination rate coefficient of the gas state sulfur compound on the low-temperature hydrolysis condition in the application field.

The invention discloses a method for predicting organic compound and hydroxyl radical reaction rate constant in a water phase based on a quantitative structure-activity relationship. On the base of obtaining a compound molecular structure, a quantitative structure-activity relationship model is built by calculating a molecular structure descriptor through a multi-linear-regression method, and the water phase kOH value of organic compounds can be predicted quickly and efficiently. According to the method, modeling is conducted according to the building and using guide rules of a QSAR model of an economic cooperation and development organization, the model application field is clear, 526 kinds of different organic compounds are included, and particularly compounds containing most compounds of atoms such as nitrogen, phosphorus, and sulphur which are not contained in previous models are covered. The model has good fitting capacity, robustness and predicting capacity, the water phase kOH value of the organic compounds in an application field can be effectively predicted, and an important data support is provided for environmental durability evaluation and ecological risk assessment of organic chemical products.