661results about "Chemical structure search" patented technology

The invention relates to an inverse synthesis route planning method and system based on a multi-modal large model, and belongs to the technical field of chemical synthesis and artificial intelligence crossing. The method comprises the following specific steps: firstly, extracting three-dimensional molecular structure characteristics of a target product through a molecular encoder, and aligning the three-dimensional molecular structure characteristics with text modals of molecules; secondly, reasoning a possible reaction mechanism and a reaction type, and reducing an inverse synthesis search space; secondly, splitting a target product into reasonable synthesis subunits through structured marking and vocabulary expansion, data-driven atomic assignment learning and reaction center recognition and activity prediction; finally, SMILES representations and reaction paths of the reactants are generated. According to the method, the accuracy and interpretability of inverse synthesis path planning can be improved, and a new technical means is provided for automation and intellectualization in the fields of new drug development, material design and the like.

The invention discloses a soil organic pollutant identification method and system based on combination of AI and high-throughput screening, and relates to the technical field of pollutant screening, and the method comprises the following steps: extracting a substance peak from high-resolution mass spectrum data of a soil sample; the method comprises the following steps: establishing an organic pollutant mass spectrum database, extracting spectrogram characteristics and structural characteristics of compounds in the organic pollutant mass spectrum database, establishing a molecular fingerprint prediction model, and establishing a mapping relation from a spectrogram to a structure; constructing a spectrogram vector prediction molecular fingerprint according to the extracted substance peaks, and retrieving by using an organic pollutant mass spectrum database to obtain candidate chemical structures; scoring the candidate chemical structures by predicting the molecular fingerprints, selecting the chemical structures meeting a preset standard, constructing a recognition basis according to the selected chemical structures, and obtaining an organic pollutant recognition result of the soil sample. According to the method, non-targeted intelligent analysis is carried out through cross fusion of artificial intelligence and mass spectrometry, and rapid and accurate identification of soil organic pollutants is realized.

The invention provides a metal matrix composite microstructure strengthening simulation method and system, and relates to the technical field of composite mechanical property simulation, and the method comprises the following steps: establishing a polycrystalline aluminum matrix atomic model and a spherical silicon nitride particle strengthening phase atomic model unit; embedding the spherical silicon nitride particles into the polycrystalline aluminum matrix according to the target embedding position coordinates, and integrating to generate an atomic scale structure model; according to the method, an embedded atom potential function is adopted to describe interaction among atoms in a polycrystalline aluminum matrix, a Tersoft potential function is adopted to describe covalent bond interaction among atoms in spherical silicon nitride particles, and a Lennard-Jones potential function is adopted to describe interaction among heteroatoms at a Si3N4 / Al interface; based on the interaction potential between atoms, molecular dynamics simulation is adopted for conducting uniaxial stretching simulation on the atomic scale structure model, mechanical property parameters of the particle reinforced metal matrix composite are obtained through calculation, and a visualization method is combined for analyzing the microcosmic strengthening mechanism of the particle reinforced metal matrix composite.

The present application relates to a construction method for a corrosion-resistance degree-mechanical property analysis model of a metal material, and the use thereof. The construction method for the analysis model comprises the following steps: using a metal material as a test object, and acquiring corrosion test experimental data sets at different equivalent corrosion time points under corrosion test conditions and mechanical property test experimental data sets at different corrosion degrees corresponding to the different equivalent corrosion time points; and respectively establishing an empirical relational expression of corrosion parameters changing with an equivalent corrosion time and an empirical relational expression of mechanical parameters changing with the equivalent corrosion time.

The invention relates to a construction method and application of a corrosion resistance degree-mechanical property analysis model of a metal material. The application relates to an electric device comprising a battery system. The construction method of the analysis model comprises the following steps: taking a metal material as a test object, and obtaining a corrosion performance test experiment data set at different equivalent corrosion time points under a corrosion test condition and a mechanical performance test experiment data set at different corrosion degrees corresponding to the different equivalent corrosion time points; based on the corrosion performance test experiment data set, a first group of empirical relational expressions of corrosion parameters changing along with the equivalent corrosion time are established, based on the mechanical performance test experiment data set, a second group of empirical relational expressions of mechanical parameters changing along with the equivalent corrosion time are also established, and the corrosion test conditions are used for simulating a target service environment. The analysis model can be effectively used for mechanical property prediction and service life evaluation of the metal material, and the metal material can comprise an aluminum alloy material and can further comprise a battery box body material.

The invention relates to the technical field of metal plastic forming, and discloses a titanium alloy radial forging reduction rate collaborative optimization method based on material testing, which comprises the following steps of: acquiring dynamic characteristic data in a forging process in real time by acquiring multi-dimensional material performance parameters, and constructing a multi-target collaborative optimization model; and intelligent collaborative configuration of the reduction rate parameters is realized by adopting a self-adaptive weight distribution strategy. And a thermal coupling factor normalization equation and a phase field free energy functional normalization model are established, and the technical problems that in a traditional forging process, due to empirical configuration of reduction rate parameters, the structure is uneven, and residual stress exceeds the standard are solved. A lightweight model is deployed through edge computing nodes, millisecond-level dynamic correction of an optimization scheme is realized in combination with a 5G industrial private network, and the grain size qualification rate and the size precision of the titanium alloy forgings are improved. The method improves the yield of the grain size of the forge piece, ensures that the residual stress of the forge piece is reduced, stably controls the size precision, and improves the batch stability of high-end titanium alloy parts.

The present application relates to the technical field of metal plastic forming, and discloses a titanium alloy radial forging reduction rate collaborative optimization method based on material testing, which acquires dynamic characteristic data of a forging process in real time through multi-dimensional material performance parameter collection, constructs a multi-objective collaborative optimization model, and realizes intelligent collaborative configuration of the reduction rate parameter by using an adaptive weight distribution strategy. A thermal coupling factor normalization equation and a phase field free energy functional normalization model are established to solve the technical problems of uneven structure and excessive residual stress caused by the empirical configuration of the reduction rate parameter in the traditional forging process. A lightweight model is deployed through an edge computing node, and millisecond-level dynamic correction of the optimization scheme is realized in combination with a 5G industrial private network, thereby improving the grain size qualification rate and size accuracy of titanium alloy forgings. The present application improves the grain size qualification rate of forgings, ensures stable control of the size accuracy while reducing the residual stress of forgings, and improves the batch stability of high-end titanium alloy components.

The invention discloses a multi-component catalyst active site prediction system and method fused with quantum embedding, and relates to the technical field of catalysis and material informatics, and the system comprises a structure and site enumeration module which generates candidate sites; the adaptive quantum embedding calculation module obtains key reaction microcosmic parameters; the unified site fingerprint and feature engineering module constructs and fuses standard site fingerprints; the physical consistency machine learning module predicts adsorption energy and other parameters and uncertainty thereof; the active learning and sample selection module selects a high-value sample optimization model; the microdynamics evaluation module calculates index values such as activity; and the multi-objective optimization and sorting module generates an optimization sorting list. According to the method, the unification of calculation precision and efficiency is realized, the problem of non-unification of locus characterization is solved, the model interpretability and extrapolation reliability are improved, and the comprehensive evaluation and optimization sorting of multi-target performance are completed.

The present disclosure relates to systems and methods for chemical toxicity prediction. The methods of the present disclosure comprise: receiving test data from an analysis instrument; selecting a candidate chemical according to the test data; determining a hazard translated level and a hazard evaluation level of the candidate chemical according to a molecular fingerprint of the candidate chemical; and predicting the toxicity of the candidate chemical by using a quantitative structure-activity relationship (QSAR) model based on the hazard translated level and the hazard evaluation level.

The invention discloses a construction method and system for volatile organic compound release behavior characterization, and belongs to the technical field of air analysis and risk assessment. According to the method, feature analysis is carried out based on multi-source information such as a molecular structure, a release carrier and environmental conditions, modeling is carried out on a release process by utilizing a mapping operator, and a three-dimensional representation vector V = [I, S, E] reflecting release intensity, environmental sensitivity and time evolution is constructed. The representation is output in a behavior vector or digital label mode, and unified quantitative benchmarking of different substances in a cross-scene mode is achieved. According to the method, depth characteristics such as molecular polarity and desorption activation energy are introduced, so that the dependence on long-period environmental chamber experiments is reduced, the generalization prediction capability on unknown pollutants is improved, the logic connection problem between a bottom-layer physical mechanism and an upper-layer intelligent management and control system is solved, and the reliability of the system is improved. And standardized and explainable behavior input is provided for prospective risk research and judgment.

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.

In one aspect, a method for identifying residues in a particular conformation of a protein molecule capable of forming a stable binding between a compound and the protein molecule is provided. The method can include identifying, for inclusion in a perturbation set, one or more residues from the conformation of the protein molecule that exhibit a protection factor satisfying one or more thresholds. One or more residues may be excluded from the perturbation set based at least on a spatial arrangement and / or an orientation of the one or more residues. One or more rounds of molecular dynamics simulations whose result is indicative of an interaction between the compound and the residues in the perturbation set may be performed. The residues in the perturbation set may be identified as forming a stable binding between the compound and the protein molecule based at least on the result of the molecular dynamics simulation

The invention belongs to the technical field of traditional Chinese medicine information processing, and provides a traditional Chinese medicine disease prescription data base construction method and system.The method comprises the steps that five types of data including ancient literatures, modern medical records, prescription data, traditional Chinese medicine resources and medical record manuscript images are accessed through an extensible adapter frame; a traditional Chinese medicine term service platform is constructed by using multi-source term resources, and standardized data is generated from the analyzed data; the method comprises the following steps: extracting diseases, syndromes, prescriptions and traditional Chinese medicine entities by adopting a rule engine and a domain self-adaptive entity recognition model, on the basis of a public traditional Chinese medicine standard knowledge graph, aggregating neighbor node information through GAT, and introducing a contrast learning optimization entity relationship classifier to obtain an enhanced entity vector and classify a semantic relationship; constructing a knowledge graph based on the extracted semantic relationship; and carrying out data quality and algorithm performance monitoring and tracing on data access analysis, standardization processing, knowledge extraction and graph construction. Uniform access and standardized integration of multi-source heterogeneous traditional Chinese medicine data are realized.

The invention relates to the field of in-situ pH value prediction, and discloses an in-situ pH value prediction model construction method and device in an oil and gas field pipeline, equipment and a medium, which can obtain the value ranges of a plurality of in-situ pH influence parameters in the oil and gas field pipeline, and generate a plurality of environmental condition data according to the value ranges of the plurality of in-situ pH influence parameters. And performing in-situ pH value simulation on each piece of environmental condition data to obtain a corresponding in-situ pH simulation value, taking each piece of environmental condition data and the corresponding in-situ pH simulation value as training data, and performing symbolic regression analysis based on all the training data to generate an in-situ pH value prediction model of the oil and gas field pipeline. The in-situ pH value prediction model can be used for predicting the in-situ pH value of the oil and gas field pipeline under different working conditions, the model construction cost is low, the reliability is high, the prediction condition is simple and feasible, the prediction process is efficient and accurate, and the in-situ pH value prediction model can be widely applied to the in-situ pH value prediction of the oil and gas field pipeline under different environment conditions.

The invention provides a method and a device for determining hyper-parameters of a potential energy model based on a graph neural network. The method comprises the following steps: acquiring a key sample from a plurality of samples of a data set for molecular dynamics simulation; determining a truncation radius configuration of the potential energy model based on the key sample, the truncation radius configuration including a truncation radius of each element combination; training of the potential energy model is performed based on the key sample and the truncation radius configuration, and the truncation radius configuration is adjusted based on an inference result of the trained potential energy model, where an input of the potential energy model is an atomic graph determined based on the key sample and the truncation radius configuration, and an output of the potential energy model is an atomic graph determined based on the key sample and the truncation radius configuration. The output of the potential energy model comprises energy parameters and force parameters of atoms.

The present disclosure provides a method, device, electronic device and storage medium for molecular screening, and relates to the fields of deep learning technology, graph theory technology and biological and information technology in the field of artificial intelligence technology. The method is as follows: obtain a first label map of the molecule to be screened and a second label map of the reference molecule, each molecule to be screened and the reference molecule form a molecule pair, for each molecule pair, obtain the mapping between the vertices of the first label map and the second label map and the conflict information between the mappings to generate a mapping map of the molecule pair, sample the mapping map, obtain the maximum weight fully connected subgraph of the molecule pair, and according to the maximum weight fully connected subgraph of each molecule pair, screen out the molecule to be screened with the greatest similarity to the reference molecule as the target molecule. A framework for molecular screening based on Gaussian boson sampling is proposed, so that Gaussian boson sampling can be used to efficiently implement the problem of ligand-based drug virtual screening and improve the efficiency of molecular screening.

The invention discloses a method for preparing a high-strength heat-resistant die-casting Mg-Gd-Zr alloy by regulating physical parameters, and relates to the technical field of alloy die-casting processing. According to the method for researching and developing the die-casting high-strength heat-resistant Mg-Gd-Zr alloy by adjusting the Gd / Y ratio, the Zn / RE ratio and the Al content in the Mg-Gd-Zr alloy, the feasibility of the scheme is preliminarily verified through low-cost gravity casting starting from alloy component screening, a large number of elements beneficial for improving the fluidity of the alloy are screened in combination with thermodynamic calculation and dynamic calculation, and the high-strength heat-resistant Mg-Gd-Zr alloy is obtained. On the basis of keeping high strength, the die casting defects are reduced, the elongation of the alloy is improved, finally, the high-strength heat-resistant Mg-Gd-Zr alloy subjected to high-pressure die casting is successfully prepared, and the technology starts from the alloy, improves the alloy flowability, reduces the defects brought in the die casting process and improves the alloy performance.

In one aspect, a method is provided for identifying a residue in a particular conformation of a protein molecule that is capable of forming a stable bond between a compound and the protein molecule. The method can include identifying one or more residues from the conformation of the protein molecule to include in a perturbation set, the one or more residues exhibiting a protection factor that satisfies one or more thresholds. One or more residues may be excluded from the perturbation set based at least on the spatial arrangement and / or orientation of the one or more residues. One or more rounds of molecular dynamics simulations may be performed, the results of which are indicative of interactions between the compound and the residues in the perturbation set. The residues in the perturbation set may be identified as forming a stable binding between the compound and the protein molecule based at least on the results of the molecular dynamics simulation.

The invention discloses an additive molecule screening design method for optimizing the performance of ester-based insulating oil, and relates to the technical field of electrical insulating materials. The invention provides an additive molecule design method combined with a deep molecule generation model, aiming at solving the problems that the breakdown voltage performance is insufficient due to the fact that ester-based insulating oil is easy to generate rapid streamer under high-strength lightning impulse, and time and labor are consumed when an appropriate additive is found through an experiment. The method comprises the following steps: acquiring molecular data with target electronic attributes; taking the fragment of the molecular structure as the input of the model, and designing a new additive molecular structure by utilizing the molecular generation model; performing molecular screening according to target characteristics; and the performance of the screened molecules is verified through streamer discharge simulation. According to the method, generative design of ester-based insulating oil additive molecules is realized by using the deep autoregressive neural network, the molecular screening efficiency is remarkably improved, and a new technical route is provided for improving the breakdown voltage performance of the ester-based insulating oil.

This disclosure describes a machine learning system that includes a contrastive learning based two-tower model for retrieval of relevant chemical reaction procedures given a query chemical reaction. The two-tower model uses attention-based transformers and neural networks to convert tokenized representations of chemical reactions and chemical reaction procedures to embeddings in a shared embedding space. Each tower can include a transformer network, a pooling layer, a normalization layer, and a neural network. The model is trained with labeled data pairs that include a chemical reaction and the text of a chemical reaction procedure for that chemical reaction. New queries can locate chemical reaction procedures for performing a given chemical reaction as well as procedures for similar chemical reactions. The architecture and training of the model make it possible to perform semantic matching based on chemical structures. The model is highly accurate providing an average recall at K=5 of 95.9%.

Disclosed herein are methods and systems that acquire mass spectrum peak data for fragment ions from a mass spectrum of fragment ions generated in an LC-MS / MS measurement instrument, acquire molecular structure and fragment ion map data for fragment ions by using the acquired data, and determine fragment ion map data for fragment ions. And a candidate molecular structure of the target substance is obtained by assembling the fragment ion diagram.

The invention discloses a compound smell prediction method based on an isotropic graph neural network. The method comprises the following steps: (1) constructing molecular graph representation; (2) constructing an isovariant graph neural network model; (3) preparing training data; (4) training the model; (5) evaluating the model; and (6) predicting the odor of the compound. The invention further discloses a system of the compound smell prediction method based on the isotropic graph neural network. The method provided by the invention has relatively high prediction accuracy and relatively strong generalization ability.

The invention discloses a lattice distortion and entropy value-based high-entropy alloy screening method, which comprises the following steps of: firstly, simultaneously calculating the lattice distortion degree and entropy value of a high-entropy alloy, and then determining the components of the high-entropy alloy meeting the requirements according to the screening requirements; according to the Hume-Rother alloy solid solution theory, the lattice distortion degree is calculated; the lattice distortion degree must be greater than or equal to 8.7%, and the upper and lower limit difference must be greater than or equal to 2.0%; entropy calculation is carried out based on the statistical definition of the concept of mixed entropy in statistical mechanics; the entropy value must be greater than or equal to 1.5 R. The obtained TiFeMnCoVZr high-entropy alloy only contains a Laves phase, and when the TiFeMnCoVZr high-entropy alloy is used as a magnesium hydride hydrogen storage material, the maximum hydrogen absorption amount can reach 4.7 wt.%, the maximum hydrogen desorption amount is 4.6 wt.%, and the hydrogen desorption activation energy is 68.9 kJ / mol.

Disclosed are systems and methods for identifying a target molecule by receiving a molecule dataset from a library of molecules comprising chemical-structural representations of candidate molecules for a material application; determining one or more target molecular property values derived from a set of predictive molecular descriptor values determined from a candidate molecule of the molecular dataset applied to a trained machine learning model, wherein the trained ML model is configured to output the set of predictive molecular descriptor values for a given molecule data input, and wherein the trained ML model was trained for a set of training data and the set of molecular descriptors generated from a molecular descriptor modeling application; and outputting, via a report or to a data store, the one or more target molecular property values for each of the molecular dataset.

The invention discloses a molecule design method and system based on multi-parameter optimization, and the method comprises the steps: constructing a molecule database which comprises a plurality of candidate molecules; selecting a plurality of scoring functions, wherein the scoring functions comprise a drug similarity scoring function, a molecular weight scoring function, a hydrogen bond donor quantity scoring function and a hydrogen bond receptor quantity scoring function; for each candidate molecule, respectively calculating a score value of the candidate molecule under each scoring function; according to a preset weight coefficient, performing weighted summation on the score values of the scoring functions to obtain a comprehensive score of each candidate molecule; and sorting the candidate molecules according to the comprehensive scores, and selecting the candidate molecule with the highest comprehensive score as an optimized molecule. According to the method, the drug properties of molecules are optimized by combining multiple scoring functions; custom combination of various scoring functions is supported, an optimization strategy can be flexibly adjusted according to different drug design targets, and the comprehensiveness and flexibility of molecular design are remarkably improved.

The invention belongs to the technical field of high voltage and insulation, and relates to an XLPE breakdown field strength evaluation method and system based on gel content and preparation process, and the method comprises the following steps: S1, sampling, weighing, shearing and bagging; s2, extracting, weighing and calculating the gel content; s3, constructing an evaluation model; s4, predicting the breakdown field intensity; according to the technical scheme, the physical model fusing the cross-linking enhancement effect and the thermal damage effect is constructed, the breakdown field strength is predicted based on the physical model, accurate and rapid evaluation of the XLPE insulation performance under different process conditions can be achieved only through a small amount of breakdown test data calibration model, dependence on a traditional destructive electrical test is remarkably reduced, and the reliability of the XLPE insulation performance is improved. The test cost is greatly reduced, the research and development period is greatly shortened, and an effective tool is provided for cable insulation material performance analysis and production process optimization.

A method for operating a protein structure-based candidate discovery system may include: displaying a first screen for receiving input of a plurality of ligands to be docked to a target protein; calculating predicted binding energy by sequentially performing molecular docking on the plurality of ligands based on settings input by a user via the first screen; displaying a first list on a second screen, the first list containing rows including a user interface control for user selection, names of the plurality of ligands, and the calculated binding energy values; predicting binding affinity for the rows selected by the user via the user interface control from the first list displayed on the second screen; and displaying a second list on a third screen, the second list containing rows including the names of the plurality of ligands, values of the binding energy, and values of the predicted binding affinity.

Computer-implemented methods may include identifying a polymer for decomposition. The method may further include accessing, for an ionic liquid, one or more properties corresponding to the polymer. One or more properties may characterize a reaction between the polymer and the ionic liquid. The method may also include accessing a value of the property using a quantum-mechanical or thermodynamical method. The method may include determining a bond string and position (BSP) representation of a molecule of the ionic liquid. The method may further include determining an embedded representation of the ionic liquid based on the BSP representation. In addition, the method may include generating a relationship between BSP representations of molecules and the one or more properties. The method may also include identifying an ionic liquid as a prospect for depolymerizing the specific polymer based on the relationship. The method may include outputting an identification of the ionic liquid.

One embodiment provides a multi-scale simulation-based functionally graded material design method for a high temperature environment. According to one embodiment, since the characteristics of a functionally graded material (FGM) may be predicted through multi-scale simulation in advance and optimal conditions may be derived, it is possible to reduce the costs required for experiment and manufacture and shorten the manufacturing time.

To provide a creation method of initial experimental data and an experiment planning method for creating proper initial experimental data, while reducing calculation costs.SOLUTION: A creation method of initial experimental data includes an initial experimental data creation step for creating initial experimental data 34 by extracting data with which combinations have high adaptability from experiment candidate data 32. In the initial experimental data creation step, the initial experimental data 34 is created by repeating: a selection step in which N pieces of data are extracted from data 33 in process, after the experiment candidate data 32 has become the data 33 in process, and M pieces of data (where M<N) with which combinations have high adaptability are selected from the extracted N pieces of data, by using a genetic algorithm; and a data update step for updating the data 33 in process by combining the data selected in the selection step, and data remaining of the data 33 in process that was not extracted in the selection step.SELECTED DRAWING: Figure 1