105 results about "Molecular graph" patented technology

The invention relates to the technical field of artificial intelligence application, and discloses an enzyme EC number prediction method, and the method comprises the steps: obtaining the sample sequence characteristics of a to-be-predicted sample containing a substrate SMILES sequence and a product SMILES sequence through a target BERT model; constructing a molecular object and feature coding based on atom mapping, atom truncation and sequence analysis, constructing a reaction graph of a to-be-predicted sample, inputting the reaction graph into a target graph isomorphic neural network, and constructing molecular graph features of the to-be-predicted sample based on a recursive neighborhood aggregation mechanism; and fusing the sample sequence features of the to-be-predicted sample with the molecular map features by using a bidirectional cross attention mechanism to obtain multi-modal features, inputting the multi-modal features into the multi-layer perceptron, and obtaining the prediction probability of the enzyme EC number of the to-be-predicted sample. According to the method, efficient and accurate end-to-end prediction of enzyme EC numbering is realized through the multi-dimensional chemical spatial characteristics of the collaborative modeling reaction.

The invention discloses a contrastive learning molecular property prediction method based on an anti-fact and a large language model, and belongs to the technical field of molecular representation learning combining a large language model and a graph neural network, and the method comprises the following steps: generating an original molecular graph through a molecular SMILES character string; generating a skeleton disturbance diagram and a functional group disturbance diagram; ensuring that the generated perturbation diagram is an anti-fact hard negative sample; generating a molecular natural language description through a large language model, and converting the molecular natural language description into a molecular semantic embedding vector by using a small language model; performing comparative learning among the original molecular graph, the positive sample and the anti-fact hard negative sample; original molecular graph embedding and molecular semantic embedding obtained after comparative learning are fused, and downstream molecular property prediction is carried out. According to the method, hard negative samples can be generated through an anti-fact mechanism, the diversity of molecular characterization modes can be ensured through a large language model, and the accuracy of comparative learning during molecular property prediction is remarkably improved.

The invention relates to an enzyme turnover number prediction method and system fused with multi-modal characteristics of an enzymatic reaction, and belongs to the technical field of bioinformatics. Comprising the following steps: respectively constructing an optimized protein pre-training model and an optimized chemical reaction pre-training model; extracting sequence features of a to-be-detected amino acid sequence by utilizing the optimized protein pre-training model to obtain an enzyme sequence feature matrix; extracting sequence features of an enzymatic reaction SMILES sequence to be detected by utilizing the optimized chemical reaction pre-training model to obtain an enzymatic reaction SMILES feature matrix; using molecular fingerprints to represent substrate and product molecular sets, and extracting a set feature matrix of the two molecular sets through a graph attention network; the method comprises the following steps: representing substrate and product molecule sets by using a molecular graph, and extracting internal feature matrixes of molecules in the two molecule sets through a graph isomorphic network; and carrying out feature enhancement on the obtained feature matrix so as to obtain an enzyme turnover number prediction value. The method improves the stability and precision of the prediction result, and has the advantages of low cost and short period.

The invention discloses a drug-target interaction prediction method and system, and belongs to the technical field of biological information. The method comprises the following steps: firstly, acquiring molecular structure data of a drug and sequence and structure data of a target spot; then, respectively extracting molecular map structure characteristics and SMILES sequence characteristics of the medicine, and amino acid sequence characteristics and three-dimensional space structure characteristics of a target spot; further, taking drugs and targets as nodes, taking the fused multi-modal features as node features, and combining known interaction and similarity information to construct an initial heterogeneous graph; inputting the heterogeneous graph into a dynamic graph neural network, dynamically learning an inter-node connection weight by using a graph attention mechanism, and iteratively updating node representation through multi-layer message transmission to obtain depth feature representation of drugs and targets; and finally, splicing the depth features, inputting the depth features into a multi-layer perceptron classifier, and predicting the drug-target interaction probability. According to the method, through multi-modal feature fusion and dynamic graph structure learning, the prediction accuracy and robustness are remarkably improved.

A computing system is provided, which receives a molecular graph at a message passing graph neural network (MPGNN), and produces scalar embeddings representing features of nodes and edges of the graph and vector embeddings representing geometric relationships of the graph. The system processes the scalar embeddings via a vector scalar interactive message passing mechanism of a message passing sub-block of theMPGNN to generate and pass scalar information from the scalar embeddings to an embedding space containing the vector embeddings. The system updates the vector embeddings based on the embedding space containing the scalar information and the vector embeddings. The system updates the scalar embeddings based on run-time geometry calculations of the geometric relationships encoded in the vector embeddings. The system computes an updated molecular graph based on the updated scalar and vector embeddings and outputs a target molecular property value based on the updated molecular graph.

The invention discloses a gas molecule smell prediction method and system based on an isotropic graph neural network, and relates to the technical field of tobacco production equipment.The method comprises the steps that an SMILES expression of gas molecules is converted into a molecular graph structure containing atomic features and edge features and three-dimensional space coordinates through DGL-LifeSci and RDKit toolkits; then inputting the data into an EGNN isotropic graph neural network, aggregating neighborhood information through a message passing mechanism, and synchronously updating node features and coordinate features; a plurality of EGNN layers are stacked for deep feature extraction, and high-dimensional node representation and final coordinate values are obtained; performing global pooling through an MLPReadout module to obtain a global molecular representation vector of a fixed dimension; and finally, mapping the vector to a 138-dimensional space through a full connection layer, processing through a Sigmoid function to obtain an odor probability vector, and finally judging and outputting a specific odor category through a threshold value.

The application provides a molecular graph output method and device. The method comprises the following steps: acquiring a chemical molecule SMILES; splitting the chemical molecule SMILES into a molecular substructure; inputting the chemical molecule SMILES and the molecular substructure into a pre-constructed molecular property prediction model; calling the molecular property prediction model to process the chemical molecule SMILES, obtaining a non-masked prediction value of the chemical molecule SMILES, and sequentially performing a masking process on the molecular substructure to obtain a masked prediction value of different molecular substructures; and outputting a molecular graph corresponding to the contribution of different substructures of the chemical molecule SMILES based on the non-masked prediction value and the masked prediction value. The application can calculate the contribution value of each substructure to the property and provide interpretability.

A foundation model for performing molecular-level tasks by learning multimodal data in the form of one-dimensional text and two-dimensional graphs, and a system therefor, according to an embodiment of the present invention, enable various molecular-unit tasks such as chemical reaction prediction, molecular attribute prediction, and natural language description generation to be effectively processed through a single foundation model. In addition, it is possible to increase prediction accuracy of the model by maximizing utilization of two-dimensional molecular graph information, and automatically generate, on the basis of statistical sparsity, high-quality descriptive text that emphasizes core and distinctive features of each molecule.

A formulation graph convolution network (F-GCN) with multiple GCNs assembled in parallel and connected to filters and an external learning architecture is able to predict the effectiveness of a formulation. Input into the multiple GCNs are molecular structures of formulants, which are processed as molecular graphs and output as molecular descriptors. The molecular descriptors are filtered by normalized ratios or fractions of the ingredient molecules in a formulation, such as a battery electrolyte or solvent. A formulation descriptor combines the filtered molecular descriptors to arrive at a predicted performance for the formulation, such as the battery capacity for an electrolyte formulation, by an external learning architecture. F-GCN may use a pre-trained GCN with physico-chemical properties of known molecular structures.

This invention discloses a multimodal umami peptide identification method and system based on molecular graphs and sequence features, belonging to the interdisciplinary field of food bioinformatics and artificial intelligence. This invention extracts amino acid sequence features of peptides through a protein pre-trained language model, and simultaneously constructs a two-dimensional molecular graph based on the peptide's SMILES characterization to extract atomic-level structural features. Standard one-dimensional convolutional layers and dilated convolutional layers in a sequence encoder are used to capture local features and long-range dependency features, respectively, and atomic local structural features are extracted through a two-dimensional convolutional layer in a structural encoder. Subsequently, the extracted three types of features are pooled and concatenated, and an enhanced residual module is introduced to discriminate the peptide identification results. This invention overcomes the limitations of traditional methods that rely on single sequence features, and significantly improves the accuracy and interpretability of umami peptide identification by deeply fusing sequence semantics and molecular structural information.

The application discloses a compound water solubility prediction method based on enhanced molecular graph representation learning, and aims to improve the prediction accuracy and robustness. The technical scheme is: a compound water solubility prediction system based on enhanced molecular graph representation and double-branch graph neural network fusion is constructed, which is composed of a data preprocessing and enhanced molecular graph construction module and a water solubility prediction module (containing a double-branch fusion graph neural network model). The data set required for constructing the training prediction system is constructed. The double-branch fusion graph neural network model contains two branches of GraphSAGE and GIN, and the model is trained by using the AdamW optimization method combined with the learning rate scheduling strategy, gradient clipping and early stopping mechanism, and the trained prediction system is obtained by loading the model parameters with the best performance on the validation set. The trained prediction system is used to predict the compounds to be predicted, and the water solubility prediction value of the compound is obtained. The prediction accuracy of the application is significantly improved compared with the traditional method.

This application provides a method, system, and storage medium for predicting the expression efficiency of mRNA cap structures. The prediction method includes obtaining the SMILES sequence of the molecule related to the mRNA cap structure to be predicted; generating a DGL molecular graph based on the SMILES sequence; performing a first encoding on the DGL molecular graph to obtain a graph vector; performing a second encoding on the SMILES sequence to obtain a text context vector; using the text context vector as a query to obtain text-guided graph attention features; using the expanded graph vector as a query to obtain graph-guided text attention features; fusing the text-guided graph attention features and the graph-guided text attention features to obtain a fused feature vector; and performing prediction based on the fused feature vector to obtain the predicted expression efficiency value of the molecule related to the mRNA cap structure. This improves the accuracy of predicting the relationship between mRNA cap structure and expression efficiency.

The invention relates to the technical field of biological information, in particular to a method for establishing a visual model of degradation paths and products of carbohydrates, which comprises the following steps: acquiring chemical structure data of target carbohydrates, standardizing the chemical structure data, and outputting a standardized molecular map; recursively splitting the standardized molecular map, and outputting a candidate intermediate set and a reaction sequence; according to the reaction sequence of the reaction sequence, assembling the candidate intermediate set, and outputting a path diagram; distributing candidate enzymes and species for each step of the path diagram, and outputting a step-enzyme-substance triple; step rating and polymerization splitting are carried out on the step-enzyme-substance triple, and a confidence interval is output; and outputting the degradation path of the target saccharide and a visual model of the product according to the confidence interval. The problems that in the prior art, the product false positive is high, the path cannot be explained, and the consistency with literature / experiment is insufficient are solved.

The application relates to the technical field of energetic material design and performance prediction, and relates to an energetic material multitask property prediction method. Standardized data sets are constructed by collecting energetic material sample data and performing molecular graph data enhancement processing on a training subset; molecular SMILES strings are respectively parsed into molecular topological graphs and word sequences, three-dimensional graph neural network is used to encode atomic space position information to extract molecular local topological features, a Transformer encoder is used to extract molecular global long-range correlation features, and a dynamic weighting mechanism is used to realize adaptive fusion of the two types of features, so that the depth and comprehensiveness of feature mining are improved; a multitask prediction model with a shared encoding layer, multiple independent decoding layers and a task decoupling constraint mechanism is built, an end-to-end training is completed by using a multitask loss function, the problems that existing multitask methods are difficult to balance the internal conflicts between energy performance and safety performance and are difficult to simultaneously predict multiple key indicators are solved, and the simultaneous prediction of multiple performance indicators is realized.

The application discloses a traditional Chinese medicine small molecule Gibbs free energy prediction method and system based on deep learning. The application receives a traditional Chinese medicine small molecule in the form of a SMILES string or a molecular structure file, and after structure verification and standardization processing, the traditional Chinese medicine small molecule is converted into a molecular graph representation. The molecular graph is input into a deep learning model. The model captures the molecular structure characteristics and interaction relationship through a graph representation learning mechanism, refines the representation through multi-layer updating, and finally outputs the predicted Gibbs free energy value of the traditional Chinese medicine small molecule through a pooling operation. The test set determination coefficient R2 is greater than or equal to 0.99. The application improves the calculation efficiency and prediction accuracy while maintaining accuracy. In addition, the model of the application supports general equipment deployment, and has strong deployment flexibility.

The invention provides a molecular structure determination method and device, electronic equipment and a storage medium, and belongs to the technical field of molecular optimization. The electronic device can input a two-dimensional molecular graph and a three-dimensional molecular graph of an initial molecule into a graph encoder to obtain molecular structure characteristics of the initial molecule, input a target molecule text into a text encoder to obtain text characteristics, and input the molecular structure characteristics and the text characteristics into a diffusion model. And adjusting the molecular structure features based on the text features through a diffusion model to obtain the molecular structure of the target molecule. According to the method, the two-dimensional molecular diagram and the three-dimensional molecular diagram can be processed, when the molecular structure is optimized, the two-dimensional structure of the molecule is considered, the three-dimensional structure of the molecule is combined, the molecule is optimized based on the two-dimensional structure and the three-dimensional structure of the molecule, the utilized molecular structure information is more complete, and the molecular structure optimization efficiency is improved. Therefore, the accuracy of the predicted molecular structure of the target molecule can be improved.

The application provides a kind of molecular graph-oriented element level representation learning method and system, based on graph diffusion model, original molecular graph is added with reconstruction, edge reconstruction error is calculated, and original molecular graph is divided into multiple element subgraphs according to edge reconstruction error;Atom view and element view are constructed, and graph neural network encoder is used to encode atom view and element view respectively, to obtain atom embedding matrix and element embedding matrix;Based on atom view and element view, positive and negative sample pairs are constructed, and barlow twin double view contrast loss function is used to optimize atom embedding matrix and element embedding matrix, to realize cross-granularity representation alignment.The application extracts molecular element through diffusion model self-supervision, and solves the systematic defects of traditional method in cross-granularity structure modeling, semantic guided contrast learning and multi-level representation fusion through atom-element double view alignment, significantly improves the semantic completeness and task generalization ability of molecular representation.

The application discloses a molecule property prediction method based on a chemical element knowledge graph and a functional group prompt, and comprises the following steps: collecting chemical knowledge including chemical elements and their chemical attributes, functional groups and their chemical attributes, and constructing a chemical element knowledge graph according to the chemical knowledge; enhancing an original molecule graph according to the chemical element knowledge in the chemical element knowledge graph to obtain a molecule enhanced graph; pre-training a graph encoder in a comparative learning mode according to the original molecule graph and the molecule enhanced graph; constructing a functional group prompt according to the functional group knowledge in the chemical element knowledge graph, adding the functional group prompt to an input molecule graph, and fine-tuning the pre-trained graph encoder and a nonlinear predictor by using the input molecule graph added with the functional group prompt; and the fine-tuned graph encoder and the nonlinear predictor constitute a molecule property prediction model; and the molecule property prediction model is used to predict the molecule property, so that the accuracy of the molecule property prediction is improved.

The present application relates to a kind of molecular structure perception chemical reaction kinetics equation symbol regression method, for solving the problem that existing symbol regression technique ignores species chemical information, causes poor modeling effectiveness.The method adopts encoder-decoder architecture, and the encoding part is provided with double branch: one branch is handled concentration time series by Transformer, another branch utilizes graph neural network to encode molecular graph, and structural characteristics are extracted.After bimodal feature fusion, it is directly returned to the kinetics equation of ordinary differential equation form by the Transformer decoder with cross attention mechanism.Training data is generated by synthesis: based on the data set of elementary reaction, SMILES is converted into molecular graph, and the kinetics equation is deduced according to mass action law, and then concentration trajectory is generated by random sampling and numerical integration, and training sample comprising molecular graph, trajectory and equation is formed.The present application significantly improves the accuracy, interpretability and data utilization efficiency of symbol regression in reaction kinetics modeling.

This invention belongs to the field of bioinformatics and relates to a molecular property prediction method based on multimodal gating and contrastive learning, including techniques such as contrastive learning, graph neural networks, cross-modal alignment, and gated attention. First, data standardization and graph construction are performed, and molecular fingerprint embeddings are extracted. Second, a heterogeneous dual-channel graph encoding architecture is adopted, with one channel capturing short-range atomic interactions through an attention mechanism, and the other integrating the global molecular structure and long-range dependencies to generate complementary molecular representations. Subsequently, a cross-modal attention mechanism is introduced to achieve bidirectional association between graph and fingerprint features, and modal weights are adaptively and dynamically allocated via a gated fusion module. Finally, a contrastive pre-training strategy is employed to construct sample pairs using the molecular graph and fingerprint, learning discriminative molecular representations on unlabeled data. This method significantly improves the accuracy of molecular property prediction, providing an efficient and reliable computational tool for virtual drug screening and lead compound optimization.

The invention provides a multi-angle persistent chemical screening method based on a multi-task graph neural network, and belongs to the technical field of high-throughput screening for chemical risk management. The method aims at achieving synchronous, efficient and accurate screening of the related attributes of the durability of the chemicals. The method comprises the following steps: firstly, constructing a multi-angle chemical durability parameter data set; the method comprises the following steps: taking a molecular graph generated by a chemical structure as model input, constructing a model through a graph neural network, and finishing synchronous training by adopting a mean square error and a superposition loss function with positive sample weight cross entropy; evaluating the performance of the model through indexes such as a determination coefficient and an area under a subject working curve; and characterizing a model application domain by adopting structure-activity relationship morphology analysis. The model constructed by the research relates to more persistent parameters, is more comprehensive, larger in data volume and wider in coverage range, has good fitting ability, prediction ability, robustness and reliability, and can provide technical support for environmental risk assessment of persistent chemicals.

The application discloses a kind of metal organic compound energy prediction methods based on deep learning, belong to compound property prediction technical field.It includes the following steps: obtaining the molecular structure information of metal organic compound and calculating electronic structure characteristics, constructs molecular graph and encodes generation geometry feature vector;Global fusion features are output through multimodal feature fusion model;Total energy value and molecular structure are predicted through multi-task prediction network;The mean and variance of the total energy value are obtained by statistical prediction, and the energy prediction value and confidence interval are obtained.The application constructs molecular graph and extracts geometric features, and simultaneously utilizes cross attention and gate fusion network to realize the deep interactive fusion of geometric and electronic structure features, solving the problem of inaccurate feature representation of metal organic compounds and poor multimodal feature fusion effect of traditional methods.

The present application belongs to the technical field of chemical structure recognition, and particularly relates to a chemical structure recognition method and a recognition system. The method comprises the following steps: obtaining an original data set containing chemical structure images based on historical literature, and generating an image segmentation data set and an image recognition data set according to the original data set; converting PDF format literature into a plurality of to-be-recognized images for literature needing chemical structure recognition, recognizing chemical structures in the plurality of to-be-recognized images, and extracting chemical structure images; generating an image recognition learning data set and an image recognition test data set respectively according to the image recognition data set, recognizing chemical atoms in the chemical structure images through an image recognition model, and recognizing hyper texts; reasoning and constructing a chemical molecule graph based on the chemical atoms and the hyper texts, and analyzing and outputting chemical structure formulas conforming to SMILES or InChI specifications. The present application can extract machine-readable chemical structure formulas from PDF format documents.

The application discloses a molecular graph generation method and device, a storage medium and a program product, relates to the technical field of molecular graph processing, and comprises the following steps: predicting an input noise graph through a graph generation model to determine a probability adjacency matrix of the noise graph; performing power operation on the probability adjacency matrix and an original adjacency matrix of an original molecular graph multiple times to obtain a first high-order adjacency matrix of multiple noise graphs and a second high-order adjacency matrix of the original molecular graph; constructing a target loss function according to the first high-order adjacency matrices and the second high-order adjacency matrices; updating the graph generation model according to a back propagation algorithm and the target loss function, and performing the step of predicting the input noise graph through the graph generation model until the target loss function converges, so that a trained graph generation model is obtained. Through local-to-global structure consistency learning, the rationality of a molecular structure generated by the model is improved.

The present disclosure provides a molecular characterization model training method and device and electronic equipment, relating to the technical field of computers, in particular to the technical field of deep learning. The specific implementation scheme is: for each sample molecule, according to the molecular formula of the sample molecule, generating a two-dimensional molecular graph and a three-dimensional molecular graph of the sample molecule, wherein the two-dimensional molecular graph is used to represent the chemical bonds between atoms in the sample molecule, and the three-dimensional molecular graph is used to represent the positional relationship between atoms in the sample molecule; for each sample molecule, inputting the two-dimensional molecular graph and the three-dimensional molecular graph of the sample molecule into an original model to obtain the two-dimensional features of the two-dimensional molecular graph and the three-dimensional features of the three-dimensional molecular graph output by the original model; according to the similarity between the molecular properties represented by the two-dimensional features and the three-dimensional features respectively, adjusting the model parameters of the original model to obtain a molecular characterization model. The accuracy of the trained molecular characterization model can be improved.

The application belongs to the field of bioinformatics and relates to a drug and target interaction prediction method based on joint network attention. First, multi-task self-supervised feature learning is performed on the drug molecular graph and protein sequence to extract the context information of drug substructures and protein residues, thereby obtaining high-precision representation features. Second, a machine learning model is combined to realize the interaction between drugs and targets. Finally, the trained model realizes the interaction prediction between drugs and targets. The application can maintain excellent prediction performance and strong generalization ability under limited labeled data conditions. Experimental results show that the method has significant advantages in drug discovery, target screening and candidate drug mechanism identification, and can provide efficient and reliable tool support for new drug research and development.

The invention provides a protein-ligand binding affinity prediction method based on a graph convolutional network. The method comprises the following steps: firstly, constructing a protein map based on a three-dimensional structure of a protein pocket, and carrying out feature coding on sequence residues by using ESM3; meanwhile, a molecular map containing geometric information is constructed for the ligand, and chemical characteristics are extracted in combination with molecular fingerprints. And then cross-graph interaction of the protein graph and the ligand graph is realized by adopting a multi-layer message passing graph neural network, and an attention mechanism is introduced to capture key interaction sites and spatial dependency relationships. And generating a unified representation vector of a compound level through pooling, and outputting binding affinity through a prediction network. According to the method, the multi-modal association of the structure and the sequence can be automatically learned, and the accuracy and efficiency of virtual screening and lead compound optimization are effectively improved.

The invention discloses a molecular smell prediction method based on diversity and a graph attention network, and the method comprises the steps: S1, taking a general text character string SMILES representing a molecular structure as input, and carrying out the preprocessing; s2, extracting atomic and chemical bond characteristics and Transform characterization of the pretreated molecules; s3, inputting the features in the S2 into a graph attention network model, and capturing a relationship between atoms and chemical bonds in the molecular graph; s4, after the image attention network, performing weighted convergence on the features, and performing feature integration through a multi-layer perceptron in combination with the Transform representation extracted in the S2 to form final molecular feature representation; and S5, calculating a probability value of each category according to the final molecular feature representation, forming an adaptive focus loss function with a real label, and by dynamically adjusting weight distribution of difficult samples and reducing loss contribution of easy-to-classify samples, enabling the model to pay more attention to rare label samples difficult to classify so as to constrain training of a molecular odor prediction model.

The invention relates to the technical field of environmental geochemistry, in particular to a degradation prediction method for soluble organic molecules based on a time sequence chart neural network. According to the degradation prediction method for the soluble organic molecules, a data set is formed based on existing water body degradation whole-process molecules, and data standardization processing is carried out according to a specified standard; converting the standard data matrix into a molecular graph network, inputting the molecular graph network into a time sequence graph neural network, and organizing the molecular graph network according to a time sequence to form a graph sequence; a dynamic graph neural network model is constructed through combination of graph network characteristics of a molecular structure and time sequence constraints of environmental conditions to realize high-precision prediction of degradation paths, rates and potential products of organic molecules in the environment under different conditions. According to the method, a rule rationality constraint and interpretability mechanism is introduced, the chemical effectiveness of a prediction result can be effectively guaranteed, an interpretability output result can be provided, and thus the reliability and interpretability of the model are further enhanced.