25 results about "Molecular Databases" patented technology

The invention discloses a rapid detection method for traumatic brain injury by fusing genetic engineering and quantum dots, and relates to the technical field of traumatic brain injury detection. The method comprises the following steps: acquiring a suspected patient biological sample, extracting biomolecule components, and comparing the biomolecule components with a healthy population biomolecule database to obtain related differential expression molecules. A specifically recognizable recombinant antibody is constructed by genetic engineering, and is connected with a quantum dot through a coupling reaction to prepare a detection probe. The probe and a biological sample are mixed and reacted, and the intensity of a fluorescence signal generated by combination is collected by a fluorescence detection device. A standardized signal value is obtained through signal processing algorithm noise reduction and feature extraction, and a preliminary detection result is determined by combining a bioinformatics database evaluation sample. And finally, according to the differential expression molecule, the standardized signal value and the preliminary result, analyzing a data change trend in a preset time window by using a multivariable statistical model, and generating a diagnosis report. According to the method, various technologies are fused, and a new path is provided for traumatic brain injury detection.

The invention provides a phytophagous insect food web DNA molecular data automatic analysis method and device based on high-pass sequencing and a storage medium, and relates to the field of molecular biological information detection.The method comprises the steps that sequence splicing, screening and species identification are carried out on obtained double-end sequencing data and local and downloaded DNA databases through an automatic system, and a DNA molecular database is obtained; generating an Excel table containing species names and a DNA bar code sequence file; performing comparative analysis on the double-end sequencing data by adopting matching splicing, and generating a contiguous group sequence based on a local DNA database; if the matching splicing cannot generate the effective sequence, generating a new gene file by adopting non-parameter splicing, and performing gene annotation in combination with the downloaded DNA database; all analysis steps are connected in series through standardized parameter input, including gene screening through threshold values and generation of insect recipe identification results. According to the method, the sequencing data can be subjected to full-process automatic analysis through a one-key command, and the efficiency of food web authentication high-throughput sequencing data processing is greatly improved.

The invention relates to a multi-model-based petrochemical production stream molecular-level composition prediction method and system, and relates to the technical field of oil refining chemical molecule management, and the method comprises the steps: measuring the molecular composition of a raw material, splicing structural units based on a structural unit-bond electric matrix frame, and constructing a molecular digital structure topology; constructing a molecular composition model by combining a preset molecular database, a probability density function and a molecular property predictor; establishing a molecular-level reaction network based on a catalytic reforming reaction mechanism, and constructing a molecular-level reaction kinetic model in combination with a reaction rate expression; constructing a reactor model according to mass, energy and momentum transfer equations; inactivation and separation unit models are constructed, multiple models are coupled in sequence, a whole-process reaction-separation molecular level prediction model is generated, and any stream molecular level composition is predicted. The method solves the problem that traditional petrochemical production depends on measurement results to adjust process parameters and has operation lag, and can eliminate lag and improve production control precision by predicting stream molecular composition.

The application provides a drug virtual screening method and device based on deep learning, wherein the method comprises the following steps: inputting all candidate compound small molecules in a candidate molecule database into a pre-trained molecular encoder respectively to obtain molecular vectorization representation of the candidate compound small molecules; constructing an index structure corresponding to the candidate compound small molecules based on the molecular vectorization representation of the candidate compound small molecules; inputting a protein target to be matched into a pre-trained protein target encoder to obtain protein target vectorization representation corresponding to the protein target; and matching the protein target vectorization representation based on the index structure to obtain a target compound small molecule corresponding to the protein target. The method establishes a full-amount mapping function of the target and the molecule through a vector calculation mode, realizes high-throughput virtual screening in a second-level calculation time, realizes rapid virtual screening of a full-amount candidate molecule library, improves the precision of the virtual screening, and increases the possibility of drug discovery.

This invention relates to a method for constructing a machine learning potential energy model based on hierarchical active learning, comprising the following steps: constructing a database of background solvent molecules and lithium salt molecules; initializing a machine learning potential energy model committee; outer active learning automatically exploring the chemical species composition space of the electrolyte, generating electrolyte formulations and initial structures; determining whether a structure needs to be included in the labeling range based on uncertainty calculations; automatically constructing a liquid phase environment based on classical force field simulation methods; performing high-precision simulations based on first-principles calculations and labeling the energy and force values ​​of the structures; inner active learning driving the machine learning potential energy model to explore the configuration space and label the energy and force values ​​of structures with high uncertainty; obtaining a first-principles structure database of the real liquid phase environment through several iterations; and training to obtain the final high-performance machine learning potential energy model. This invention systematically and comprehensively samples the chemical species composition space of the electrolyte and the molecular simulation configuration space based on hierarchical active learning, thereby ensuring the predictive performance and simulation accuracy of the machine learning potential model on unknown and complex electrolyte systems.

The invention relates to the field of computational chemistry, in particular to universality optimization in a transition metal chromophore computational design process. According to the technical scheme, the high-universality design method of the transition metal chromophore comprises the following steps: establishing a molecular database; a calculation model is adopted to scan and calculate molecules in the molecular database, molecules with calculation results meeting screening requirements are regarded as to-be-selected molecules, and DFA is adopted as the calculation model; and the calculation model adopts a plurality of DFAs for calculation. The invention aims to provide a high-universality design method and equipment for transition metal chromophores and a storage medium, and functional molecules of metal chromophores can be designed for different design systems. According to the method, the reliability of the output result of the VHTS in the design process of different metal chromophores is greatly improved, better training data is provided for subsequent AI model training, and therefore the accuracy of the design result output by the AI model is improved.

The invention discloses an oil refining model generation method and a visual display method and device. The method comprises the following steps: creating a single device model for each device based on a molecular database and a chemical reaction rule base in the oil refining field; according to the cutting condition output by the single device model, constructing a logistics connection model with a downstream single device model; according to the whole-process technological processes of the historical production schemes in different time periods, the logistics connection model is connected with a downstream single device model, and a whole-process model of each historical production scheme is constructed; and according to historical raw material information, performing simulation calculation on the whole-process model of each historical production scheme to obtain and store a result log, and based on this, providing a whole-process display function of the historical production scheme for a user through a visual UI, so that the user can visually understand the process of different production schemes in different time periods of refinery equipment, and the user experience is improved. Historical process data can be conveniently traced, and production schemes and process changes can be tracked.

The invention discloses a crude oil data standardization processing method, system and device and a storage medium, and the method comprises the following steps: comparing a data format of to-be-input crude oil data with a preset data format in a crude oil molecular database, and judging whether the data formats are the same or not; when the comparison result is different, performing standardization processing on the to-be-input crude oil data according to a preset data format in the crude oil molecular data; and inputting the standardized to-be-input crude oil data into a crude oil molecular database. Crude oil data in different data formats are subjected to unified standardization processing by adopting a crude oil molecular database unified management method, so that workers can conveniently manage or query the crude oil data, and the working efficiency is improved.

A generic cancer prognosis evaluation system based on LAMA5 expression provided by the invention relates to the technical field of generic cancer prognosis evaluation, and comprises the following steps: acquiring multi-source molecular data, establishing a standardized data input path to obtain LAMA5 gene molecular information, and completing sample identification and field standardization by calculating mutation frequency and gene copy number variation of the LAMA5 gene molecular information to obtain a generic cancer prognosis evaluation result. And an LAMA5 generic cancer molecular database is formed. A standardized data input path and a unified field format are formed by establishing an LAMA5 generic cancer molecular database and integrating TCGA, GTEx, a human protein map and other multi-source molecular data, the database achieves generic cancer prognosis evaluation system calculation of LAMA5 mutation frequency and gene copy number variation, the data consistency and statistical reliability of multiple cancer species are improved, and the method is suitable for large-scale popularization and application. And a data basis is provided for pan-cancer level expression and variation analysis.

A molecular vocabulary can be added to a tokenizer of a language model. The molecular vocabulary can include character strings as words representing atomic environments, explicit atomic positions, and forward-connecting positions based on a molecular database that includes data describing molecules and their properties. A molecular syntax can be defined for the tokenizer that represents each of the molecules as a respective molecular sentence made up of tuples corresponding to a quantity of atoms in a respective molecule and including words from the molecular vocabulary. The molecules can be tokenized according to the molecular vocabulary and the molecular syntax. The language model can be trained to perform property prediction for molecules or to identify molecules having targeted properties using the tokenized molecules and their properties. The trained language model can perform property prediction for a molecule not in the molecular database or identify molecules having the targeted properties.

The embodiment of the invention relates to a processing method and device for generating an MR-TADF molecular library. The method comprises the following steps: taking a preset benzene ring structure as an initial molecular skeleton; performing molecular skeleton deduction based on the initial molecular skeleton to obtain a corresponding molecular skeleton library; carrying out heteroatom substitution and hydrogen atom addition treatment on molecular skeletons in the molecular skeleton library to obtain a corresponding preselected molecular library; and performing MR-TADF molecular screening on the molecular structure of the preselected molecular library to obtain a corresponding MR-TADF molecular library. According to the method, a molecular database with massive MR-TADF molecular structures can be constructed, and the purpose of effectively increasing MR-TADF molecular types and the number of molecular structures is achieved.

Embodiments of the invention provide a data screening method, a computing device, a storage medium and a program product. The data screening method comprises the steps of obtaining at least one item molecule attribute information and a verified molecule set corresponding to a target item; determining a molecule set according to the item molecule attributes and the verified molecule set; then acquiring a set access frequency and a molecular set score corresponding to each molecular set, and acquiring a molecular set weight corresponding to each molecular set according to the set access frequency and the molecular set score; determining a target sample molecule set according to the molecule set weight of each molecule set, and generating a project scoring model for the target project based on the target sample molecule set; and determining a target molecule in a molecule database according to the project scoring model. The molecules for training the scoring model are determined by obtaining the weight corresponding to each molecule set, so that the problem of poor scoring model effect caused by excessive invalid molecules when the scores for training the molecules are obtained can be avoided.

The application relates to a method and system for predicting the molecular level composition of a petrochemical production stream based on multiple models, and relates to the technical field of oil refining and chemical molecular management, and comprises the following steps: determining the molecular composition of raw materials, splicing structural units based on a structural unit-bonding moment matrix framework, and constructing a molecular digital structure topology; combining a preset molecular database, a probability density function and a molecular property predictor to construct a molecular composition model; establishing a molecular level reaction network based on a catalytic reforming reaction mechanism, combining a reaction rate expression to construct a molecular level reaction kinetics model; constructing a reactor model according to mass, energy and momentum transfer equations; constructing inactivation and separation unit models, coupling multiple models in sequence, generating a full-process reaction-separation molecular level prediction model, and predicting the molecular level composition of any stream. The application solves the problems that traditional petrochemical production relies on measured results to adjust process parameters and has operation lag, and through prediction of the molecular composition of a stream, the lag can be eliminated, and the production control precision is improved.

The present invention relates to a virtual screening method for LCK inhibitors based on deep learning, comprising: obtaining molecular data of the LCK target, cleaning the data, and obtaining a dedicated evaluation dataset of the LCK target; calculating the Morgan fingerprints of each molecule in the dedicated dataset and establishing a similarity matrix, and clustering each molecule to form multiple clusters; obtaining an active molecule set in each of the clusters, and generating a decoy molecule set according to an active molecule set; respectively obtaining ligand small molecules and receptor proteins, and evaluating and determining a deep learning algorithm and a molecular docking program according to the active molecule set and the decoy molecule set; respectively obtaining a molecular database and a protein, and performing virtual screening according to the determined deep learning algorithm and molecular docking program to obtain quasi-target molecules; detecting the in vitro inhibitory activity of the quasi-target molecules against LCK to obtain final target molecules; and evaluating the biological effects of the final target molecules. The present invention also relates to a device, equipment, and a storage medium.

The application relates to an interstellar molecule database construction method, an interstellar molecule database query method, an interstellar molecule database construction device and a computer device. The interstellar molecule database construction method comprises the following steps: generating the number of an interstellar molecule according to the basic information of each interstellar molecule in an interstellar molecule data set; determining an interstellar molecule corresponding to each number as a target interstellar molecule; performing standardization processing on the molecular structure information in the basic information of each target interstellar molecule to obtain the molecular structure code of the target interstellar molecule; and storing the basic information of the target interstellar molecule based on the number and the corresponding molecular structure code of each target interstellar molecule, so as to construct the interstellar molecule database of each target interstellar molecule. Through the application, the problem that a standardization system of interstellar molecule data is not established, data heterogeneity and redundant storage exist in the database, and a high-quality database cannot be provided is solved, and a high-quality and standardized database is constructed.

The present invention discloses a method and apparatus for predicting molecular drug properties. The method includes: obtaining training molecules and basic information of the training molecules from a molecular database, and using the basic information of the training molecules to obtain the high-dimensional feature distance D of the training molecules; after respectively performing dimensionality reduction and network processing on the high-dimensional feature distance D of the training molecules, obtaining a two-dimensional feature pixel matrix GMap of the training molecules; obtaining a feature calculation value vector m of a prediction molecule, and assigning the feature calculation value vector m to the two-dimensional feature pixel matrix GMap to obtain a representation two-dimensional matrix MolMap of the prediction molecule; using the representation two-dimensional matrix MolMap of the prediction molecule as the molecular representation of the prediction molecule, and using the molecular representation of the prediction molecule to predict the drug properties of the prediction molecule.

The invention discloses an intelligent question-answering system and method for molecular oil refining, and relates to the technical field of oil refining chemical engineering and computer software, and the system comprises a molecular database which is used for storing various types of crude oil data vectors; the intention recognition module is used for extracting intentions in user query based on a pre-trained intention recognition model; the vector data processing module is used for retrieving a plurality of crude oil data vectors from the molecular database according to an accurate matching retrieval mode and / or a similarity retrieval mode, wherein the matching value of the crude oil data vectors and the user intention exceeds a preset value; and the response generation module is used for generating an expression form matched with the intention based on the plurality of crude oil data vectors with the matching values exceeding the preset value, and pushing the expression form to the user. Through natural language processing and machine learning technologies, user demands are responded and understood in real time, and automatic analysis and processing of crude oil data are realized.

The application discloses a rapid detection method for traumatic brain injury by combining gene engineering and quantum dots, and relates to the technical field of traumatic brain injury detection. The method obtains a biological sample of a suspected patient, extracts biomolecular components, compares the biomolecular components with a biomolecular database of healthy people, and obtains relevant differentially expressed molecules. A recombinant antibody capable of specific recognition is constructed by gene engineering, and is connected with quantum dots through a coupling reaction to form a detection probe. The probe is mixed with the biological sample for a reaction, and a fluorescence signal intensity generated by combination is collected by a fluorescence detection device. After noise reduction and feature extraction by a signal processing algorithm, a standardized signal value is obtained, a sample is evaluated in combination with a bioinformatics database, and a preliminary detection result is determined. Finally, according to the differentially expressed molecules, the standardized signal value and the preliminary result, a multivariate statistical model is used to analyze a data change trend in a preset time window, and a diagnosis report is generated. The method combines multiple technologies and provides a new path for traumatic brain injury detection.

The present invention belongs to the field of medicine, and specifically relates to a method for building a library through intelligent combination, and using this library to discover small molecules with anti-cancer activity. The present invention establishes a virtual database of 12,904 molecules. The virtual molecules in this database are assembled using Python from existing and commercially available molecular building blocks. The conditions for intelligent combination are mature and simple reaction conditions. Therefore, the small molecules in this database are easy to synthesize and have greater practical significance. Using this database for virtual screening and synthesizing the screened molecules can obtain small molecules with high anti-cancer activity. These molecules are essentially non-toxic to normal gastric mucosal cells and have significant therapeutic effects on gastric cancer, and their therapeutic effects are better than cisplatin, thus having good application prospects.

The invention relates to a molecular property prediction method based on topological perception generative molecular characterization, which comprises the following steps of: sampling molecular data from a public unlabeled molecular database, analyzing the molecular data into a molecular graph, and extracting a topological unit set; encoding the molecular graph by using a graph convolutional neural network and a cross-scale attention module, and obtaining a latent variable for subsequent generation; through a dynamic generation mechanism, atoms and chemical bonds are reconstructed in combination with Laplacian position codes, a molecular fingerprint prediction task is introduced, and a loss function is jointly constructed to pre-train a model; and carrying out fine tuning on the pre-trained model on a downstream reference data set MoleculeNet for specific molecular property prediction. By adopting the method disclosed by the invention, the problems of'feature inhibition ', 'shortcut learning' and the like of the existing self-supervised learning method can be solved, and the high calculation overhead of the traditional generative method is avoided, so that the molecular representation with strong discrimination is learned, and the accuracy, robustness and efficiency of molecular property prediction are improved.

The application discloses a cross-modal molecular information retrieval method, system, device and medium, wherein the application adopts a multi-modal fusion method, calculates a contrast loss through a modal extraction encoder to obtain single-modal representations of graph structure, image and text data of molecular information, so that the graph structure information and the image information are aligned to supplement and enrich the text information, and calculates a fusion loss through a modal fusion encoder to obtain multi-modal representations of the molecular information, so that the fusion of different modal data such as the graph structure, the image and the text is realized, and then in the application process, cross-modal retrieval of molecules can be performed based on the multi-modal representations of the molecular information in a molecular database according to target modal data and a target query condition of target molecular information, the demand query of multi-modal information processing is met, and therefore the accuracy and efficiency of molecular information retrieval are improved.

The invention discloses a molecule generation method and device based on a generative adversarial network, equipment, a medium and a product, and relates to the technical field of computational chemistry and batteries. The method comprises the following steps: constructing a molecule database by adopting a matrix engineering method according to atom types and quantity required by target molecules; converting a molecular structure corresponding to the candidate molecular set in the molecular database into a graph structure to obtain a molecular graph database; inputting the molecular graph database into the trained generative adversarial network, and performing molecule generation and decoding to obtain newly generated molecules; based on a preset screening rule, performing molecular screening on the newly generated molecules to obtain screened qualified molecules; converting the screened qualified molecules into three-dimensional structures to obtain three-dimensional molecules; the three-dimensional molecules are used for computational simulation and experimental verification to provide a reference basis for electrolyte design and optimization. According to the invention, molecule generation and screening can be efficiently realized.

The present application relates to the field of computational chemistry, in particular to the universality optimization in the process of computational design of transition metal chromophores. The present application is realized by the technical scheme that a high-universality design method of transition metal chromophores comprises the following steps: establishing a molecular database; performing scanning calculation on the molecules in the molecular database by using a calculation model, and regarding the molecules with calculation results meeting the screening requirements as candidate molecules, wherein the calculation model adopts DFA; and the calculation model adopts multiple DFAs for calculation. The purpose of the present application is to provide a high-universality design method of transition metal chromophores, equipment and storage medium, which can design functional molecules of metal chromophores in the face of different design systems; greatly improve the reliability of the output results of VHTS in the design process of different metal chromophores; provide better training data for subsequent AI model training, thereby improving the accuracy of the design results output by the AI model.

The application discloses a drug virtual screening method based on hash learning, which comprises the following steps: firstly, obtaining a protein-molecule complex dataset; defining protein and molecule encoders respectively; defining a contrast learning objective function to learn the similarity information of the protein and the molecule; defining a multi-modal hash objective function to learn the binary vector of the protein and the molecule; constructing a final loss function by combining the contrast learning and the multi-modal hash objective function, and training a model; and representing molecules in a molecule database as binary vectors. When performing drug virtual screening, a protein target is represented as a binary vector. The Hamming distance between the binary vector of the protein target and the binary vectors in the molecule database is calculated, or an inverted index is constructed from the binary vector database to retrieve the most likely binding molecules. According to actual requirements, a certain proportion of molecules are selected as candidate drug molecules. The application improves the precision of drug virtual screening, reduces storage overhead, and improves retrieval speed.

1. Name of the Design Product: Electronic Device with a Graphical User Interface for AI-Combined Drug-Assisted R & D 2. Use of the Design Product: This platform integrates several protein, drug, and small molecule databases, platforms, and toolkits, aiming to build a one-stop AI + drug-assisted R & D platform. 3. Design Key Points of the Design Product: Lies in the front view. 4. Picture or Photograph Most Indicating the Design Key Points: Front view. 5. Human-Computer Interaction Method of the Graphical User Interface: Clicking on the PDB preview on the left side of the front view can enter the Variant State Diagram 1. Clicking on the compound search on the left side of Variant State Diagram 1 can enter the Variant State Diagram 2. Entering the compound name in Variant State Diagram 2 can enter the Variant State Diagram 3. Clicking on the molecular preview on the left side of Variant State Diagram 3 can enter the Variant State Diagram 4. Clicking on the input sequence above and entering text in Variant State Diagram 4 can enter the Variant State Diagram 5.