17 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 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. 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.

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.

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 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.

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 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 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.

The embodiment of the invention discloses a refrigerant processing method, device and equipment, and the method comprises the steps: obtaining a target refrigerant and similar homolog molecules, and determining the structural characteristics, refrigeration characteristics and environmental influence characteristics of the target refrigerant and the homolog molecules; based on the refrigeration characteristic, the environmental influence characteristic, the weight corresponding to the refrigeration characteristic and the weight corresponding to the environmental influence characteristic, determining the comprehensive effect characterization of the target refrigerant and the homolog molecule, and based on the structural characteristics of the target refrigerant and the homolog molecule, determining the comprehensive effect characterization of the target refrigerant and the homolog molecule through a Spearman correlation coefficient algorithm. Determining molecular characterization of the target refrigerant and homolog molecules; and based on molecular characterization and comprehensive effect characterization, importance analysis is performed on characterization parameters of the target refrigerant and homolog molecules through a KNN multi-classification machine learning model, and sensitivity analysis is performed on influence factors of the comprehensive effect characterization through a quantitative structure-activity relationship model, so that a transformation strategy of the homolog molecules is determined.

The application relates to the technical field of computational materials science, and discloses a method for rapidly screening a diatomic doped BCN adsorbed H structure by using a machine learning potential, which comprises the following steps: generating three-dimensional coordinate data of a candidate structure by high-throughput automatic modeling; obtaining a relaxed structure by iterative geometric optimization by using a machine learning potential model; solving a final real displacement module length value of an atom by performing coordinate conversion and period winding correction; constructing a local neighbor atom set, extracting a displacement module length, and generating three quantitative structure judgment indexes; comparing the indexes with a judgment threshold value, executing a one-vote veto mechanism, and outputting a structure abnormal state marker; and classifying and archiving the structure according to the marker and outputting a statistical report. The application replaces traditional first-principle calculation with a machine learning potential, combines a quantitative displacement index, realizes automatic pre-screening of the stability of a large-scale candidate structure, and improves material calculation efficiency.