36 results about "Molecular graph" patented technology

The invention discloses a molecular graph generation method based on a variational auto-encoder and a message passing neural network. The molecular graph generation method is used for molecular generation and molecular target characteristic optimization. According to the method, the message passing neural network is constructed into the encoder and the decoder of the variational auto-encoder, so that the running time and the occupied memory of the training process are further reduced; in addition, by constructing a potential space of the variational auto-encoder, molecular properties are allowed to be optimized; in a molecule generation experiment on a QM9 chemical database, the model can generate 100% effective compounds, and the novelty rate and the uniqueness rate are also very high; in a target optimization experiment on a QM9 chemical database, target characteristics can be further optimized.

The invention discloses a method and an apparatus for molecular toxicity prediction based on a multi-task graph neural network. The method comprises the following steps: S1, preparing a toxicity data set, and obtaining toxicity data represented by a chemical molecule standard expression; S2, generating an atomic node eigenvector by using the toxicity data which is obtained in the step S1 and represented by the chemical molecule standard expression; S3, generating a side information eigenvector by using the toxicity data which is obtained in the step S1 and represented by the chemical molecule standard expression; S4, on the basis of the atomic node eigenvector obtained in the step S2 and the side information eigenvector obtained in the step S3, constructing a molecular toxicity prediction model based on a multi-task graph neural network; S5, verifying performance of the model. According to the multi-task graph neural network designed for molecular toxicity datasets, an automatic learning molecular graph structure information model is constructed, and the performance of a toxicity prediction task can be improved by using a multi-task learning method in combination with the relevance between molecular toxicity tasks.

A computing method for normalizing molecule graph data for hierarchical molecular generation can include: providing molecule graph data of a molecule having a node; recursively splitting the node into two nodes; iteratively recursively spilling other nodes into two nodes; generating generated molecular graph data of a generated molecule from node splitting; and providing a report with the generated molecular graph. A computing method can include: providing molecule graph data into a latent code generator having multiple levels with a forward and inverse; and generating latent codes by processing molecule graph data through multiple levels of operations, wherein each level of operations has a sequence of sublevels of operations in forward path and inverse path, wherein the sublevels of operations include node merging operation and node splitting operation; generating at least one molecular structure from latent codes; and outputting generate molecule graph data having the at least one molecular structure.

The invention discloses a graph attention mechanism transfer learning-based small molecule drug hERG toxicity prediction method and device; the method comprises the following steps: S1, data set preprocessing: enabling a to-be-detected drug-like compound to generate a fingerprint sequence through molecular fingerprint generation software; s2, acquiring atom and chemical bond characteristics through the fingerprint sequence generated in the step S1, and constructing a molecular graph and graph characteristics through the atom and chemical bond characteristics; s3, processing the molecular map obtained in the step S2 through a map attention mechanism, and generating a feature vector of each atom in the molecule; s4, generating a molecular feature vector through a graph attention mechanism and the feature of each atom. According to the method, a molecular graph structure is processed based on a graph attention mechanism, a substructure which contributes to a predicted attribute value is effectively obtained, and a source domain data set and a target domain data set are processed based on transfer learning; and thereby, the problem that the sample size is insufficient is effectively solved.

The invention is suitable for the technical field of computer aided article design, and provides an article molecule generation method, device and equipment and a storage medium. The method comprisesthe following steps: inputting a first molecular diagram structure and a first connection tree structure of a source molecule into a molecule generation model, and encoding the first molecular diagramstructure and the first connection tree structure through the molecule generation model to obtain embedded representation of the source molecule; decoding based on the structure information of each substructure in the label set and the embedded representation to generate a second connection tree structure of the target molecule; and decoding the second connection tree structure to obtain a secondmolecular diagram structure of the target molecule. The structure information of each substructure in the tag set and the embedded representation of the source molecule are combined for decoding, sothat the structure information of each substructure in the tag set can be well utilized to predict the connection tree structure of the target molecule, the reasonability of the prediction result is improved, and the influence of tag imbalance is relieved.

The invention discloses a molecular graph comparison learning method based on a chemical element knowledge graph. The method comprises the following steps of: constructing a chemical element knowledge graph according to all chemical attributes of each chemical element in a periodic table of chemical elements; performing graph enhancement on the molecular graph by utilizing the chemical element knowledge graph to obtain a molecular enhancement graph; obtaining graph representations of the molecular graph and the molecular enhancement graph by using a pluggable representation model; adopting a hard negative sample mining technology to select other molecular graphs similar to the molecular graph in the molecular fingerprint space as negative samples; mapping the graph representation of positive sample pairs and the graph representation of negative sample pairs to the same space, constructing a contrast loss function by maximizing the consistency between the positive sample pairs and minimizing the consistency between the negative sample pairs, and performing optimization learning by using the contrast loss function; and forming a prediction model by using the parameter-determined pluggable representation model and a nonlinear classifier, and predicting the molecular properties of the molecular graph by using a parameter-finely-adjusted prediction model so as to improve the prediction accuracy of molecular properties.

The invention provides a software defect discovery method based on regional molecular graph mining, and relates to the technical field of software engineering. The method comprises the following steps: firstly, extracting software packages of a new version and an old version from a software project, performing same data preprocessing on the software packages of the new version and the old version,constructing a control flow graph of a program, and storing the control flow graph into a text file to obtain a positive graph data set and a negative graph data set; performing hash conversion on the program statements stored in the control flow diagram of the text file, so that the control flow diagram is represented by numerical values obtained after Hash conversion of the program statements;carrying out overlay mining on the obtained hash-converted positive and negative graph data sets to obtain an overlay graph set; performing data vectorization on the control flow graphs in the positive and negative graph data sets according to the coverage graph set; and training an extreme learning machine by taking the control flow diagram subjected to data vectorization as feature training data, obtaining a training model by adopting a voting mechanism, and testing a to-be-tested program file through the tested training model to discover software defects.

The invention provides a drug connection graph score prediction method and device based on a double-graph convolution fusion model. The method includes: after a drug association graph is trained by using a symbol graph convolutional network layer, fusing node features containing global information into a drug molecular graph of each drug through transformation of a full connection layer, then training the drug molecular graph fused with the global features by using a graph attention network layer, carrying out pooling operation, acquiring fusion features of a drug, realizing communication fusion of global information and local information, decoding the fusion features by calculating cosine similarity, comparing a predicted value with a true value, calculating an error, and performing back propagation and continuous iteration to obtain a drug connection diagram score prediction network for predicting drug connection diagram scores of the drug pairs. According to the method and device, the connection diagram score of the drug pair can be predicted quickly and accurately, candidate drugs can be screened, and time and capital costs can be reduced.

The invention provides a molecular graph reconstruction model training method and device and electronic equipment, the method is suitable for the artificial intelligence medicine field, and the molecular graph reconstruction model comprises an encoder, a decoder and a graph matching module; in the training method provided by the invention, on one hand, a graph matching module is designed as a pre-training module, and a relation matrix output by the graph matching module is designed to be used for calculating the reconstruction loss between a sample molecular graph and a reconstruction molecular graph, on the other hand, a representation vector of the sample molecular graph output by an encoder is designed as an input vector of a decoder, and the reconstruction loss between the sample molecular graph and the reconstruction molecular graph is calculated. According to the molecular graph reconstruction model training method provided by the invention, the molecular graph reconstruction model can be suitable for a molecular discovery process, the matching complexity and resource consumption can be reduced, the matching accuracy is improved, and the guiding effect of a graph matching module on the molecular graph reconstruction model is further improved. In addition, the method provided by the invention can also improve the practicability and reconstruction effect of the molecular graph reconstruction model.

The invention relates to a method for retrieving molecular structure diagrams based on evolutionary calculation and multi-view fusion. Including: step 1, use operations such as rotation, translation, and scaling to enhance data; step 2, use the enhanced data to train multiple deep models as multi-view feature extractors; step 3, use the trained multi-view extractor to extract enhanced data The multi-view feature; step 4, search for a multi-view fusion model with excellent performance through the evolutionary algorithm to obtain the fusion feature of the classification model of the molecular structure diagram and the data set; step 5, directly use the classification model or use the fusion feature to calculate The ranking value of the similarity between the search graph and the molecular graph in the search database completes the search for the molecular structure graph to be retrieved. The invention is used to solve the problem of image-based chemical molecular structural formula retrieval without relying on molecular structural formula coding characters in the field of chemical informatics.

The invention belongs to the technical field of machine learning, and in particular relates to a molecular structure prediction method based on a graph convolutional network. The present invention constructs a molecular graph and a molecular complete graph according to the SMILES of the input molecule, and correspondingly constructs a network model with two branches, one branch uses MLP for edge prediction, and the other branch includes graph convolutional network and MLP for The overall structural features of the branches are extracted. The invention uses graph convolution to extract molecular structure features, which can well extract the overall structural features of molecules, so as to better predict the structure; the double-branch model design is used to solve the problem that the complete graph destroys molecular structure information .

The invention discloses a drug relationship extraction method based on deep learning. The invention uses the RDKit tool to convert the drug molecular formula into a molecular graph structure, and then expresses the characteristics of the drug molecule, and at the same time extracts the text features of the sample, and extracts the drug molecule After the feature and the text feature of the sample are combined, the fully connected layer softmax is used to classify the drug relationship, and the physical and chemical properties of the drug in the sentence are used, which can improve the extraction accuracy and solve the problem that the existing methods are difficult to cover all text scenes and rely too much on Problems with external natural language processing tools.