34 results about "Quantitative structure" patented technology

The invention discloses a bidirectional reversible conversion method and system between a peptide molecule SMILES and a sequence expression. The core innovation lies in that a new sequence description syntax is defined to retain information of a polypeptide special bond and specific modification of amino acid; a main chain atom index and adjacency traversal topology identification algorithm is adopted, and end group and topology integrated detection and coding are carried out; a residue recognition algorithm for main chain cutting and template library matching is compatible with any standard or non-standard amino acid residues, an extensible end group library / monomer template library and an automatic increment mechanism, and automatic recognition and sequence annotation of S-S disulfide bonds; the invention relates to a high-fidelity assembly algorithm of HELM anchor points and topology aware cyclic peptide processing. The method solves the problems of incapability of supporting a complex polypeptide topological structure, poor reversibility, insufficient expansibility of a monomer library and the like in the prior art, can be widely applied to scenes of quantitative structure-activity relationship model construction, large-scale polypeptide data cleaning and the like, and has remarkable practicability and innovativeness.

The invention belongs to the technical field of biological information, and relates to an acetylcholin esterase inhibitor prediction method based on Stacking ensemble learning and molecular feature fusion, which comprises the steps of data collection and preparation, data annotation and optimization, feature extraction and analysis, construction of a Stacking model, result verification and feedback and construction of a prediction platform. The molecular fingerprints and the property descriptors are used as features, and an acetylcholin esterase inhibitor classifier is successfully constructed by adopting a Stacking algorithm. According to the method, the problems that the efficiency of finding the acetylcholin esterase inhibitor by a traditional experimental method is low, and a common quantitative structure-function relationship method is high in complexity and poor in generalization ability can be solved, the new drug finding speed is increased, experimental candidates are accurately positioned, and resource waste is reduced.

The invention provides a method for predicting the flammability upper limit volume percentage of a pure compound. The method can be used for predicting a mathematical model of the flammability upper limit volume percentage of the pure compound which is composed of 12 or less elements of hydrogen, carbon, nitrogen, oxygen, sulfur, fluorine, chlorine, bromine, iodine, silicon, phosphorus, arsenic and the like and has the atom number of 25 or less (excluding hydrogen) with high accuracy. Wherein the model is obtained as a universal quantitative structure-property relationship model by determining an optimal model from a plurality of multiple linear regression models by means of a step-by-step selection method, and the model takes some of the various molecular descriptors as independent variables, takes flammability upper limit volume percent as a dependent variable, and takes the flammability upper limit volume percent as a dependent variable. The value of the molecular descriptor included in the model can be received and input in a short time, and the flammability upper limit volume percentage can be output, so that only the specific value of the molecular descriptor included in the model is known. And the flammability upper limit volume percentage of the compound purely formed by the molecule can be predicted for any molecule meeting the requirements of the invention.

Provided is a mathematical model that can predict, with high accuracy, the saturated liquid density of a pure compound at 298.15 K, said pure compound comprising 12 or less elements such as hydrogen, carbon, nitrogen, oxygen, sulfur, fluorine, chlorine, bromine, iodine, silicon, phosphorus, and arsenic and having 25 or less atoms (excluding hydrogen). The model is a quantitative structure-property relation model and is obtained by solving an optimal model from a plurality of multiple linear regression models by a step-by-step selection method, and the model takes some molecule descriptors in various molecule descriptors as independent variables, takes saturated liquid density under 298.15 K as a dependent variable, and takes the saturated liquid density under 298.15 K as a dependent variable. The value of the molecule descriptor included in the input model can be received in a short time, and the saturated liquid density at 298.15 K can be output, so that the saturated liquid density at 298.15 K of a compound formed by the molecule singly can be predicted for any molecule meeting the requirements of the invention as long as the specific value of the molecule descriptor included in the model is known.

The invention provides an ecological risk evaluation method and system based on new pollutants, and the method comprises the steps: obtaining mass spectrum data of sample data, determining a normal mass spectrum fingerprint corresponding to a geographic position and time, marking an ion peak as an unknown peak when the ion peak in the mass spectrum data deviates from a preset range of the normal mass spectrum fingerprint, and determining that the ion peak is an unknown peak; the method comprises the following steps: acquiring a fragmented spectrum of an unknown peak based on secondary mass spectrometry, inferring a possible molecular structure of the unknown peak through a graph neural network model in combination with a preset chemical database, and constructing a quantitative structure-activity relationship of possible molecular structure analogues of the unknown peak based on a preset structural similarity network. The method comprises the following steps: acquiring predicted toxicity and signal channels influenced by the predicted toxicity, performing ecological risk assessment in a preset range of a petrochemical plant based on the predicted toxicity and the signal channels influenced by the predicted toxicity, and acquiring potential ecological risks of new pollutants. And a basic basis is provided for environment management and decision making.

Systems, methods, and / or apparatuses may be operative to perform a dialysis process that includes a displacer infusion process. In one embodiment, a method for determining a displacer compound may include constructing a plurality of target protein quantitative structure-activity relationship (QSAR) models, one for each of the plurality of binding sites, analyzing a set of candidate compounds using the plurality of QSAR models to determine a set of at least one potential compound with an affinity for binding to each of the plurality of binding sites, and selecting at least one displacer compound from the set of at least one potential compound. Other embodiments are described.

1. The name of the design product: quantitative structure-activity relationship prediction graphical user interface of electronic equipment. 2. The use of the design product: an electronic device. 3. The design points of the design product: the interface content of the graphical user interface. 4. The picture or photo that best indicates the design points: front view. 5. The use of the graphical user interface: to show the details of the prediction task of the correlation between the quantitative structure-activity relationship chemical structure and biological activity, the front view is the user task management interface, clicking the icon button under the prompt of Actions in the prediction task row, entering interface change state figure 1, clicking the View model parameters button on the interface, entering interface change state figure 2.

1. Name of this design product: Quantitative structure-activity analysis prediction graphical user interface for electronic devices. 2. Purpose of this design product: an electronic device. 3. The key design point of this design product lies in the interface content of the graphical user interface. 4. The picture or photo that best illustrates the key points of the design: main view. 5. For conventional design, the rear view, left view, right view, top view and bottom view are omitted. 6. Purpose of the Graphical User Interface: Used to display the details of the prediction task for the correlation between chemical structure and biological activity in quantitative structure-activity analysis. The main view is the user task management interface. Click the icon button with the prompt "Details" under the Actions column of the prediction task row to enter the interface change state diagram 1, which displays the prediction details. Scroll to the bottom of the interface to enter the interface change state diagram 2, which further displays the prediction details.

The invention provides a method for predicting a flash point of a pure compound by using a multiple linear regression model. According to the method, a mathematical model for predicting the flash point of the pure compound with high accuracy is established to predict a flash point value. Specifically, the model is used as a quantitative structure-property relationship model, after the optimal model is obtained from a plurality of multiple linear regression models by using a stepwise selection method for most compounds with known flash point experimental values, the molecular descriptors included in the model are received in a short time, and the flash point values are output. As long as the specific values of the molecule descriptors included in the model are known, the flash point of the compound composed of the molecule is predicted for any molecule. As described above, the present invention provides a method and model capable of predicting a reliable flash point value even for many compounds having unknown experimental values, thereby saving the cost and time for physical performance measurement experiments or chemical structure prediction, and enabling research and development activities of related industries to become easier.

1. The name of the design product: quantitative structure-activity relationship prediction graphical user interface of electronic equipment. 2. The use of the design product: an electronic device. 3. The design points of the design product: the interface content of the graphical user interface. 4. The picture or photo that best indicates the design points: front view. 5. The use of the graphical user interface: to show the details of the prediction task of the correlation between the quantitative structure-activity relationship chemical structure and biological activity, the front view is the user task management interface, clicking the icon button under the prompt of Details in the Actions column of the prediction task row enters the interface change state figure 1, and in the interface, clicking the picture in the SMILES card enters the interface change state figure 2.

The invention belongs to the technical field of measurement, and relates to a qualitative and quantitative analysis method for drinking water disinfection by-products based on effect orientation. According to the method, a drinking water sample is separated in a grading manner, high-toxicity components are screened out in combination with cytotoxicity evaluation, disinfection by-products in the high-toxicity components are identified in a non-targeted manner by utilizing a high-resolution liquid chromatography-mass spectrometry technology, and identified high-toxicity substances are determined by virtue of a quantitative structure-activity relation model; and finally, constructing a pretreatment and instrument detection method so as to realize accurate quantitative analysis of the key toxic substances. According to the method, the recognition efficiency of high-toxicity new disinfection byproducts in drinking water is remarkably improved, a toxicity evaluation system is perfected, the quantitative detection process is optimized, rapid batch analysis is achieved, efficient technical support is provided for drinking water safety guarantee, and the requirements of modern drinking water supervision for wide coverage and high-frequency monitoring are met.

Technologies for quantitative structure-activity relationship (QSAR) modeling for lipid nanoparticle (LNP) biotherapeutics include a computing device that receives a training data set including LNP test results, which each include an LNP chemical formulation and a corresponding result value of an LNP target variable, such as activity or cytotoxicity. The computing device extracts multiple input features for each LNP chemical formulation, where each of the input features is indicative of an attribute of a component or a composition of the LNP chemical formulation. The computing device trains a machine learning model to predict the LNP target variable with the input features and the result values of the training data set. A computing device may predict the LNP target variable result by extracting input features from a supplied LNP chemical formulation and supplying the input features to the trained machine learning model. Other embodiments are described and claimed.

This invention belongs to the field of detection technology and relates to a highly sensitive method for quantitative analysis and structural identification of cobalamin, as well as its application. Using three common natural cobalamins with different structures found in the environment as standards, a full-scan, automatically triggered secondary mass spectrometry acquisition method is employed to obtain the cobalamin precursor ion and a series of high-abundance characteristic ion fragments. Based on the principle that fragments of the same cobalamin have the same fragment ion m / z and the fragment ion search function of the software, the structure of unknown cobalamin is identified. Then, a parallel reaction monitoring mass spectrometry quantitative method is used. A large number of interfering ions are filtered out by a quadrupole, and the cobalamin low-position ligand fragment ions with the highest abundance and high identifiability are used as quantitative ions to analyze the type and content of cobalamin in the sample. This invention, by combining two data acquisition methods of high-resolution mass spectrometry, can effectively eliminate interference from the endogenous matrix, improve the sensitivity of sample analysis, and enhance the specificity of quantification, thereby achieving highly sensitive detection of different types of cobalamin.

The application discloses a kind of temperature-resistant water-soluble monomer design methods based on generative artificial intelligence.The method comprises the following steps: constructing polymer thermal performance dataset, training quantitative structure-property relationship prediction model;Adopt generative artificial intelligence strategy, by the structure deconstruction and recombination of existing temperature-resistant monomer and water-soluble monomer, generate massive virtual monomer library;Build water-soluble evaluation index considering solubility parameter, oil-water partition coefficient and hydrogen bond number;Combined with synthesis accessibility score, multi-objective high-throughput screening is carried out on virtual monomer library.The application realizes the synergistic optimization and high-throughput screening of the temperature resistance and water solubility of candidate monomers, solves the problems of limited types of existing temperature-resistant water-soluble monomers, mainly relying on experience trial and error in research and development, and low screening efficiency, especially the lack of unified and calculable evaluation index for water solubility, which makes it difficult to optimize and screen the temperature resistance and water solubility as constraints in the process of artificial intelligence aided design.

The invention discloses a method and a mathematical model for high-precision prediction of solubility parameters of a pure compound which is composed of 12 or less elements of hydrogen, carbon, nitrogen, oxygen, sulfur, fluorine, chlorine, bromine, iodine, silicon, phosphorus, arsenic and the like and has the atom number of 25 or less (excluding hydrogen). Wherein the model, as a universal quantitative structure-property relationship model, is obtained by determining an optimal model from a plurality of multiple linear regression models by means of a stepwise selection method, and the model takes some descriptors in various molecular descriptors as independent variables, takes solubility parameters as dependent variables, and takes the dissolvability parameters as dependent variables. The value of the molecule descriptor included in the model can be input in a short time and the solubility parameter can be output, so that the solubility parameter of a compound purely composed of the molecule can be predicted for any molecule as long as the specific value of the molecule descriptor included in the model is known.

The application relates to a method for constructing a quantitative structure-activity relationship of a dendrimer molecular structure and salt tolerance and application. According to the quantitative structure-activity relationship, the salt tolerance of an oil field chemical polymer can be obtained according to the molecular structure of the dendrimer, without needing to pass through traditional experiments or complicated molecular simulation dynamics calculation. Meanwhile, the quantitative structure-activity relationship can be used to quickly screen the dendrimer with good salt tolerance, and has a good reference significance for the rapid design and research and development of the salt-resistant polymer for drilling fluid and even oil field chemicals.

Provided is a mathematical model with which it is possible to accurately predict the standard boiling point of a pure compound having 25 or less atoms (excluding hydrogen), said pure compound comprising 12 or less elements such as hydrogen, carbon, nitrogen, oxygen, sulfur, fluorine, chlorine, bromine, iodine, silicon, phosphorus, and arsenic. Wherein the model, as a universal quantitative structure-property relationship model, is obtained by determining an optimal model from a plurality of multiple linear regression models by means of a stepwise selection method, and the model takes some of the various molecular descriptors as independent variables, takes a standard boiling point as a dependent variable, and takes the standard boiling point as a dependent variable; according to the present invention, the values of the molecule descriptors included in the model can be received and input in a short time and the standard boiling point can be output, so that the standard boiling point of the compound purely composed of the molecule can be predicted for any molecule satisfying the requirements of the present invention as long as the specific values of the molecule descriptors included in the model are known.

The present application relates to the field of environmental science and computational toxicology, and specifically provides a method for constructing species sensitivity distribution and performing soil ecological risk probability assessment based on multi-species quantitative structure-activity relationship model, which constructs QSAR model for each receptor and combines Monte Carlo simulation technology, models three key uncertainty sources of model application domain confidence, model reliability factor and ecological weight as Beta distribution, Logit- Normal distribution and truncated normal distribution respectively, randomly samples and calculates comprehensive weight in each iteration, constructs SSD curve based on weighted toxicity data, and then obtains probability distribution of hazard concentration, and finally, outputs risk probability distribution of pollutants according to comparison between pollutant concentration and HC5 distribution in the environment, which quantitatively the uncertainty of QSAR model and ecological parameters, realizes the transformation from point estimation to probability distribution, and provides a scientific basis with statistical significance for soil ecological risk classification management and remediation decision.

The invention discloses a mathematical model capable of predicting the flammability lower limit temperature of a pure compound which is composed of 12 or less elements such as hydrogen, carbon, nitrogen, oxygen, sulfur, fluorine, chlorine, bromine, iodine, silicon, phosphorus, arsenic and the like and has the atom number of 25 or less (excluding hydrogen) with high accuracy. Wherein the model is obtained as a universal quantitative structure-property relationship model by determining an optimal model from a plurality of multiple linear regression models by means of a stepwise selection method, and the model takes some descriptors in various molecular descriptors as independent variables, takes flammability lower limit temperature as a dependent variable, and takes the flammability lower limit temperature as a dependent variable. According to the present invention, the value of the molecule descriptor included in the input model can be received in a short time, and the flammability lower limit temperature can be output, such that the flammability lower limit temperature of the compound singly composed of the molecule can be predicted for any molecule satisfying the requirements of the present invention as long as the specific value of the molecule descriptor included in the model is known.

A quantitative structure-activity relationship model construction method based on model robustness relates to a quantitative structure-activity relationship model construction method, in particular to a quantitative structure-activity relationship local model construction method based on model robustness and a consistency model construction method of the quantitative structure-activity relationship local model, taking a compound liver metabolic clearance rate prediction model as an object, belonging to the field of chemical information science and bioinformatics. In view of the fact that the current global modeling method is difficult to cope with the complex modeling compounds of the liver metabolic clearance rate prediction model, the model robustness is poor, and the prediction performance is difficult to further improve, the QSAR local model based on the activity mechanism is difficult to be applied in practice, and the QSAR local model based on the chemical structure similarity lacks the technical means to guarantee the model robustness, the application provides a QSAR local model construction method based on model robustness, and a support vector machine regression technology is used to establish a QSAR local model.

Provided is a mathematical model with which it is possible to accurately predict the critical volume of a pure compound having 25 or less atoms (excluding hydrogen), said pure compound comprising 12 or less elements such as hydrogen, carbon, nitrogen, oxygen, sulfur, fluorine, chlorine, bromine, iodine, silicon, phosphorus, and arsenic. Wherein the model is obtained as a universal quantitative structure-property relationship model by determining an optimal model from a plurality of multiple linear regression models by means of a step-by-step selection method, and the model takes some of the various molecular descriptors as independent variables, takes critical volumes as dependent variables, and takes the critical volumes as dependent variables. According to the present invention, the value of the molecule descriptor included in the input model can be received in a short time and the critical volume can be output, such that the critical volume of the compound purely composed of the molecule can be predicted for any molecule satisfying the requirements of the present invention as long as the specific value of the molecule descriptor included in the model is known.

The invention belongs to the technical field of artificial intelligence, and provides a drug research and development architecture and method based on artificial intelligence, electronic equipment and a computer program.The drug research and development architecture constructs a multi-artificial-intelligence-model cluster interactive agent, and applies a large language model to deeply learn and integrate drug related data for new drug research and development. Fusing multi-dimensional information of the whole process of new drug research and development, performing effective analysis, and performing global reasoning to generate potential novel drug candidate molecules and biomolecular targets corresponding to the potential novel drug candidate molecules; and interaction, iteration and optimization are carried out through the thinking chain and various types of artificial intelligence models. The artificial intelligence models comprise a molecular docking model, a protein structure prediction model, a multi-omics conjoint analysis model, a pharmacokinetic model and a quantitative structure-activity model, so that the current situations of high failure rate and high risk of drug candidate molecules and targets in later chemical experiments, biological experiments and clinical experiments are effectively avoided; and the efficiency and success rate of drug research and development are greatly improved.

The application discloses a method for predicting compound mass spectrum ionization efficiency based on a COSMO-RS and ANN algorithm, and belongs to the technical field of predicting compound mass spectrum ionization efficiency. On the basis of knowing the structure of a compound and an elution solvent environment, optimal structural property descriptors are obtained by using a COSMO-RS program to perform thermodynamic simulation calculation, the determined relative mass spectrum ionization efficiency is combined, a nonlinear regression algorithm of an artificial neural network is written, and a quantitative relationship between various structural property descriptors and the mass spectrum ionization efficiency is established. Then, the mass spectrum ionization efficiency of an organic compound can be quickly and effectively predicted. In the data set collection, gradient elution is considered, so that the application range is expanded and the data set is expanded. In addition, in the descriptor calculation, factors such as the compound and the elution solvent are considered, so that the efficiency and accuracy of the prediction of the mass spectrum ionization efficiency are greatly improved.

The invention belongs to the technical field of environmental science and chemical engineering, and provides a QSAR (Quantitative Synthetic Aperture Radar) model for predicting adsorption of microplastics to organic pollutants, a construction method and application. A quantitative structure-activity relationship is constructed based on drug physicochemical properties and linear free energy parameters, the ability of polyethylene and polystyrene micro-plastics to adsorb organic pollutants, especially non-steroidal anti-inflammatory drugs, before and after aging is predicted, and the ability of the polyethylene and polystyrene micro-plastics to adsorb organic pollutants, especially non-steroidal anti-inflammatory drugs, is predicted by taking a distribution coefficient (Kd) of the organic pollutants in the micro-plastics as a dependent variable and taking the physicochemical property parameters of the organic pollutants as independent variables. A retreating stepwise regression method is adopted to screen variables and determine model parameters, on the premise of ensuring model interpretability, an optimal independent variable combination is screened out, the internal relation between the variables is accurately recognized, redundancy with high correlation is effectively eliminated, the collinearity degree between the variables is remarkably reduced, and the method is suitable for large-scale popularization and application. The obtained multiple QSAR model formulas are used for predicting organic pollutants adsorbed by micro-plastics, especially non-steroidal anti-inflammatory drugs, and the prediction accuracy and precision are improved.

Provided is a mathematical model with which it is possible to accurately predict the critical temperature of a pure compound having an atomic number of 25 or less (excluding hydrogen), said pure compound comprising 12 or less elements such as hydrogen, carbon, nitrogen, oxygen, sulfur, fluorine, chlorine, bromine, iodine, silicon, phosphorus, and arsenic. Wherein the model is obtained as a universal quantitative structure-property relationship model by determining an optimal model from a plurality of multiple linear regression models by means of a step-by-step selection method, and the model takes some molecular descriptors in various molecular descriptors as independent variables, takes critical temperature as a dependent variable, and takes the critical temperature as a dependent variable. According to the present invention, the value of the molecule descriptor included in the input model can be received in a short time, and the critical temperature can be output, such that the critical temperature of the compound singly composed of the molecule can be predicted for any molecule satisfying the requirements of the present invention as long as the specific value of the molecule descriptor included in the model is known.

The application provides a natural ester molecular structure modification and performance improvement method of quantitative structure-property relationship, and the method comprises the following steps: (1) exploring the correlation mechanism between the molecular structure of a natural ester insulating liquid and electrical properties; (2) building a simulation platform for predicting the low-temperature kinematic viscosity and pour point of the natural ester insulating liquid based on molecular dynamics, and based on the simulation prediction results, a prediction model of the low-temperature kinematic viscosity and pour point of the natural ester insulating liquid is built, the quantitative fitting characteristic relationship of the insulating liquid is obtained, and thus the influence mechanism of the molecular configuration and components of the natural ester insulating liquid on the cold flow characteristics is studied; and (3) performing natural ester insulating liquid intrinsic molecular structure modification based on the quantitative structure-property relationship. The application can reveal the molecular relationship between the intrinsic molecular structure of the natural ester insulating liquid and the electrical and cold flow characteristics, and based on this, the structure of the natural ester molecule is modified and the performance is improved. This has important significance for promoting and popularizing the engineering application of the natural ester insulating liquid.

The invention discloses a performance evaluation method for repairing chromium-polluted soil by using a micro-nano iron-based reducing material. The method comprises the following steps: S1, obtaining performance parameters: obtaining material repairing activity parameters and repairing long-term performance parameters of the micro-nano iron-based reducing material through a characterization test means; chromium pollution degree parameters and soil property parameters of target soil are obtained through a soil detection means; s2, establishing a correlation model: constructing a quantitative structure-activity relationship model between the performance parameters and the soil chromium reduction efficiency based on a machine learning or Meta analysis method; s3, determining an evaluation benchmark: according to the quantitative structure-activity relationship model, determining the weight and the scoring benchmark of each index for evaluating the material performance; s4, comprehensive evaluation is carried out, wherein the performance parameters of the to-be-evaluated material and the target soil are input into the evaluation benchmark, and a comprehensive performance score is obtained through calculation.

The application discloses a bidirectional reversible conversion method and system between peptide molecules SMILES and sequence expressions, and the core innovation is that: a new sequence description grammar is defined to retain the information of special bonds of polypeptides and specific modifications of amino acids; a topological identification algorithm of main chain atom index and adjacency traversal, end group and topological integration detection and coding; a residue identification algorithm compatible with any standard or non-standard amino acid residue of main chain cutting and template library matching, an expandable end group library / monomer template library and an automatic increment mechanism, automatic identification and sequence annotation of S-S disulfide bond; a high-fidelity assembly algorithm of HELM anchor points and topologically-aware cyclic peptide processing. The application solves the problems that the prior art cannot support complex polypeptide topological structure, has poor reversibility, and has insufficient monomer library expansion, and can be widely applied to quantitative structure-activity relationship model construction, large-scale polypeptide data cleaning and other scenes, and has obvious practicability and innovation.

The invention discloses a polyamide acid imidization additive preparation method based on a descriptor calculation and generation model, which comprises the following steps: identifying SMILES of a structural additive in an obtained known polyamide acid imidization additive, and calculating a molecular descriptor of the structural additive; inputting the calculated molecular descriptor and known additive performance data into a machine learning prediction model for training, and establishing a quantitative structure-activity relationship between the molecular structure and the additive performance; based on the trained prediction model, generating a candidate molecule set in combination with a generative adversarial network; performing reaction path search and transition state calculation on the candidate molecule set, and evaluating the catalytic cyclization promotion capability and reaction selectivity of the candidate molecules; screening the candidate molecule set through a multi-stage screening mechanism, carrying out experimental preparation and performance characterization on optimized molecules, and feeding experimental data back to a machine learning model to realize closed-loop optimization. The additive research and development efficiency can be improved, the research and development period is shortened, and the research and development cost is reduced.

1. Name of the product in this design: Graphical User Interface for Quantitative Structure-Property Analysis and Prediction of Electronic Devices. 2. Purpose of this design: An electronic device. 3. The key design features of this product are its graphical user interface content. 4. The image or photograph that best illustrates the design's key features: the front view. 5. Purpose of the graphical user interface: It is used to display the details of the prediction task of the correlation between chemical structure and biological activity in quantitative structure-activity analysis. The main view is the user task management interface. Clicking the icon button with the prompt "Details" under the Actions column of the prediction task row will enter the interface change state diagram 1. Clicking the Table option in the button group will enter the interface change state diagram 2. Clicking the SMILES image in the table will enter the interface change state diagram 3.