333 results about "Molecular graph" patented technology

The invention discloses a drug combination risk prediction method and device based on multi-source feature fusion and comparative learning, equipment and a medium, and relates to the technical field of drug combination prediction. The drug combination risk prediction method comprises the following steps: acquiring a drug data set; and obtaining a feature map according to the drug data set. And according to the feature map, carrying out adaptive feature extraction on the molecular map structure to obtain molecular map features of the medicine. And defining molecular map features as structural features. And inputting the molecular graph features into a multi-layer cascaded graph convolutional network, extracting high-order topological features in a drug network, and obtaining relation features. According to the drug data set, similarity features are calculated, and interactive embedding features are extracted; according to the structural features and the similarity features, potential correlation and differences between the features are mined through a comparative learning mechanism, and structural comparative learning features and similarity comparative learning features are obtained; and performing feature fusion and feature alignment on the features to obtain fused features. And predicting the drug combination risk according to the fusion features.

The invention discloses a bioactive peptide function prediction method based on multi-view multi-modal representation learning. The method comprises the following steps: extracting polypeptide amino acid sequence information by using a multi-scale expansion convolution CNN and a bidirectional LSTM; extracting structure and functional characteristics of polypeptide molecules by using an ESM-2 model; the molecular fingerprint information is processed through a convolutional CNN and a Mamba structure; topological information of the polypeptide molecular graph is extracted through a traditional convolutional CNN, and node features are processed through a graph convolutional neural network. All the multi-view features are finally fused into aggregated feature representation through feature fusion, and then multi-label classification is carried out through a full connection layer and by applying a sigmoid function. The extracted features are finally subjected to feature splicing and fusion to form a comprehensive polypeptide molecule feature representation so as to predict various biological activity features of the polypeptide. The multi-view and multi-modal feature integration method not only enhances the prediction ability of the model, but also improves the flexibility and accuracy of processing complex biological data.

The invention belongs to the field of bioinformatics, and relates to a molecular property prediction method based on multi-modal gating and comparative learning, which comprises the technologies of comparative learning, graph neural network, cross-modal alignment, gating attention and the like. Firstly, data standardization and graph construction are carried out, and molecular fingerprint embedding is extracted; secondly, a heterogeneous dual-channel graph coding architecture is adopted, one path captures atom short-range interaction through an attention mechanism, the other path integrates a molecular global structure and long-range dependence, and complementary molecular representation is generated; then, a cross-modal attention mechanism is introduced, bidirectional association of graph and fingerprint features is achieved, and modal weights are adaptively and dynamically distributed through a gating fusion module; and finally, a comparison pre-training strategy is adopted, a molecular graph and fingerprints are utilized to construct a sample pair, and discriminative molecular representation is learned on unlabeled data. According to the method, the accuracy of molecular property prediction is remarkably improved, and an efficient and reliable calculation tool is provided for virtual drug screening and lead compound optimization.

The invention relates to the technical field of computer-aided drug design, in particular to a drug molecule screening and optimizing method based on artificial intelligence prediction, which comprises the following steps: S1, obtaining a dynamic protein conformation set and molecular multi-dimensional characterization: obtaining a dynamic conformation set of a target protein and a physicochemical property spatial distribution diagram of a binding pocket of the dynamic conformation set, a two-dimensional molecular map topological structure and three-dimensional conformation coordinates of the drug molecules are obtained; s2, multi-modal fusion prediction is carried out; s3, generating interpretable optimization guidance; and S4, automatic iterative optimization: performing batch prediction and screening on the new candidate molecular structure, taking the screened optimal molecule as a new starting point, repeatedly executing the interpretability optimization guidance generation step and the step until an iteration termination condition is met, and outputting a final optimized molecule list. Through the multi-modal fusion deep learning model, the interaction strength of the drug molecules and the target protein can be quickly and accurately predicted, and the screening efficiency of the drug molecules is greatly improved.

The invention discloses a drug resistance prediction method and system based on comparative learning and multi-modal fusion, and the method comprises the steps: firstly generating a molecular map and a molecular fingerprint based on the SMILES of a target drug, and extracting the molecular features of the drug through a comparative learning model constructed through combining a map attention network and a map convolution network; and then, acquiring protein expression, gene expression and metabolic expression data from the target tissue cells, extracting modal features through a deep convolutional network, a Transform encoder and a multi-dimensional attention network, and realizing adaptive fusion of the multi-modal features through a heterogeneous interactive attention mechanism. And finally, jointly inputting the fused multi-modal features and drug molecular features into a multi-layer sensor to realize high-precision prediction of the drug resistance of cells to drugs. By introducing a contrast learning and multi-modal feature fusion mechanism, the characterization capability and prediction precision of the model are effectively improved, and efficient and reliable support can be provided for drug screening and clinical decision making.

The invention provides a drug target binding affinity prediction method based on multi-modal data fusion. The method comprises the following steps: firstly, extracting sequence feature information of drug SMILES and target FASTA, then constructing an affinity graph, modeling drug molecules and target protein molecules into an undirected graph, and extracting molecular-level features of atoms, bonds, residues and contact. And fusing the hierarchical graph structure information of the affinity graph and the molecular graph to obtain the graph structure feature representation of the drug-target spot. The sequence feature information and the graph structure feature representation are further fused by using intramolecular and intermolecular attention fusion mechanisms. And finally, performing affinity prediction by using the fused features, and outputting a drug-target binding affinity score. According to the method, sequence and structural information are effectively fused, the accuracy of drug target affinity prediction is improved, and the problems of insufficient information fusion and insufficient structural information utilization in an existing method are solved.

The invention belongs to the field of bioinformatics, and relates to a molecular property prediction method based on local graph features and global relationships, which comprises the technologies of deep learning, comparative learning, graph neural network and the like. Firstly, data processing is carried out, input data is prepared, and initial representation of a molecular map is constructed. Secondly, extracting local features of the molecular map through a local topological structure feature network, and focusing on local information of atoms in molecules and adjacency relations of the atoms; secondly, performing global feature extraction on the molecular graph by utilizing a global attention network, and capturing a global dependency relationship and long-range interaction among molecules; and finally, combining the local features and the global features through feature fusion, and performing final prediction. According to the method, through a double-branch network architecture, details of local structures in molecules can be concerned, and global interrelationships among the molecules can be captured, so that more comprehensive and accurate feature representation is provided for prediction of molecular attributes.

The invention provides a molecular structure and physical information fusion-based compound property prediction method, a molecular structure and physical information fusion-based compound property prediction system and a computer readable storage medium. The molecular structure and physical information fusion-based compound property prediction method comprises the following steps: acquiring a data set containing molecular SMILES descriptors; converting the data set into a molecular graph embedding vector and a materialization information descriptor, wherein the molecular graph embedding vector is determined by an unsupervised learning graph convolutional neural network model; fusing the molecular map embedded vector and the materialized information descriptor to determine a molecular mixed descriptor; and inputting the molecular mixed descriptors into a molecular property prediction model to predict compound properties, wherein the molecular property prediction model is constructed based on a multi-layer perceptron.

According to the method for predicting the affinity of the drug and the target based on graph neural network topology enhancement, the physicochemical properties of protein, especially the structure and the dynamics of a pocket region, can be better described, and key modes and characteristics in DTA prediction can be better captured, so that the prediction accuracy is improved. Comprising the following steps: (1) expressing a drug and a target as a structure of a graph, constructing a protein graph by using an amino acid sequence of protein and a contact graph, and linearly inputting a standard character string into a molecular graph of the drug based on simplified molecules; (2) extracting representations of drugs and targets, integrating protein pocket information into a topological structure, creating virtual nodes, incorporating the virtual nodes into a residue graph, and extracting representations by using different graph neural network modules after obtaining an enhanced residue graph and a molecular graph; and (3) connecting the representations and inputting the representations into a multi-layer perceptron to predict affinity.

The invention provides a drug response prediction method based on a gene relation network and a drug substructure. The drug response prediction method comprises the following steps: performing feature extraction on three multi-modal features of gene expression, copy number variation and mutation of a cell line by adopting an enhanced graph attention network; acquiring a substructure of the medicine by using a gating message passing neural network, and extracting features by enhancing a graph attention network; constructing two complementary graph structures, namely an atom-bond graph and a bond-angle graph, and performing feature extraction and message passing on the two graphs by using a graph convolutional neural network so as to extract 3D features of the medicine; the characteristics of the drug and the cell line are integrated through concat operation, and drug response prediction is carried out through mlp. According to the drug response prediction method provided by the invention, cell line features are extracted by establishing and using three kinds of omics data with different emphasis, and drug feature extraction is performed by using a drug molecular map, a drug 3D structure, a drug molecular fingerprint and a drug substructure relationship.

The invention discloses a reaction site prediction method and device based on chemical and physical prior driving, and the method comprises the steps: extracting set features through the multi-modal input of a fusion molecular map, an SMILES sequence and a three-dimensional conformation; generating atomic embedding by using a message passing neural network, and calculating a mixed feature fusing a topological path and a three-dimensional distance; combining the key type weight to construct a graph position code of chemical environment correction; injecting the mixed distance and the charge difference into a Transform attention mechanism, and explicitly modeling an inter-atomic long-range electron effect; a model is jointly trained through double tasks of comparative learning and mask prediction, the comparative learning adopts a directional negative sample to enhance generalization, and mask prediction synchronously recovers an atom type and a charge transfer matrix; and finally, injecting quantum chemistry priori constraint attention weights such as a Fuzzy well function, outputting an atomic-scale reaction activity probability, generating a thermodynamic diagram, and realizing high-precision and interpretable active site labeling. According to the method, the drug design and reaction mechanism analysis efficiency can be remarkably improved.

The invention provides a molecular attribute prediction method introducing domain knowledge, which belongs to the technical field of meta-learning, integrates molecular graph information and molecular chemical information through a two-channel feature coding architecture, fuses small sample learning of molecular graph structure and chemical domain knowledge, and optimizes the molecular attribute through dynamic feature fusion and task perception optimization mechanism. The problem of insufficient model generalization ability caused by data scarcity in molecular attribute prediction is solved, so that multi-modal molecular attribute prediction is realized, and the prediction precision and interpretability are improved.

The invention relates to the field of computer-aided drug design (CADD) and molecular informatics, in particular to a multi-modal representation learning method based on cyclopeptide molecules, which is used for predicting cell membrane permeability of cyclopeptide. The method mainly comprises the following steps: (1) data collection: integrating cyclic peptide permeability data from a ChEMBL database, a CycPeptMPDB database and a CyclicPepeda database and patent literatures; (2) multi-modal learning: for different modal data, a deep learning model is adopted to extract feature representations of the data; the method comprises the following steps of: encoding an SMILES sequence by using ChemBERTa (ChemBERTa); using Vision Transform to extract molecular image features, and learning a molecular image structure and 3D coordinate information based on GNN; (3) multi-modal feature fusion: adopting a self-adaptive extensible fusion mechanism, integrating SMILES feature information into image, graph and 3D coordinate features through a cross-modal feature fusion mechanism, and splicing all modal features to obtain multi-modal molecular representation; and (4) permeability prediction: sending the multi-modal molecular representation into a full connection layer for regression prediction so as to evaluate the permeability of the cyclopeptide.

The invention discloses a multi-expert molecular attribute prediction method based on substructure information bottleneck, and aims to solve the problems that an existing method cannot fully excavate molecular substructure information and lacks an effective feature filtering mechanism. Substructure representation is coded through a graph neural network; constructing a substructure information bottleneck module, and filtering redundant features by taking maximization of mutual information of a substructure and a target attribute and minimization of mutual information of the substructure and a complete molecular graph as an optimization target; dynamically distributing the substructures to an expert model based on a skeleton and a functional group in combination with a gating mechanism, and calculating a distribution probability by utilizing a trainable clustering center; updating the model by adopting a double-layer strategy of optimizing discriminator parameters through internal circulation and optimizing main model parameters through external circulation; and finally, weighting and fusing expert output to realize attribute prediction. The method significantly improves the accuracy and robustness of molecular attribute prediction, and is suitable for the fields of drug research and development and material design.

The invention discloses a drug target affinity prediction system based on cross-modal feature fusion, and relates to the technical field of biological information. The invention aims to solve the problem of low prediction precision of the existing DTA prediction method. The method comprises the following steps: preprocessing a drug SMILES character string and a protein amino acid sequence to obtain a drug molecular map, a drug SMILES sequence embedding characteristic, a protein contact map and a protein amino acid sequence embedding characteristic; according to the drug molecular diagram and the protein contact diagram, drug diagram modal characteristics and protein diagram modal characteristics are obtained; obtaining drug sequence modal characteristics and protein sequence modal characteristics by using drug SMILES sequence embedding characteristics and protein amino acid sequence embedding characteristics; fusing the drug pattern modal features and the drug sequence modal features to obtain drug fusion features, and fusing the protein pattern modal features and the protein sequence modal features to obtain protein fusion features; and acquiring the drug target affinity by using the drug fusion feature and the protein fusion feature. The method is used for predicting the affinity of the drug target.

One embodiment of the invention provides a computer-implemented method for training an autoencoder to learn one or more chemical properties. The method comprises providing, as input, to an encoder of the autoencoder, a molecular graph representing a molecular structure. The method further comprises receiving, as output, from a decoder of the autoencoder, a production rule sequence for producing a molecule description of the molecular structure. The method further comprises optimizing the autoencoder using a loss function and the production rule sequence.

The invention discloses a drug response prediction method based on a graph neural network and a convolutional neural network, and belongs to the technical field of drug response prediction, and the method comprises the following steps: S1, data preprocessing; s2, constructing a network; s3, performing network training; and S4, drug reaction prediction. According to the method, the convolutional neural network and the graph neural network are combined, the drug molecule graph data are added, and the structural features of drug molecules are introduced, so that the final regression prediction is more accurate; feature extraction is carried out on drug molecule fingerprint global features derived from the sequence by using a stacked one-dimensional convolution module, and neighborhood information is enhanced; through a designed information interaction fusion branch, information interaction between drug characteristics and cell line characteristics is realized, interaction between drug reaction information and drug smiles sequence information is also realized, and the prediction performance of the model is improved through an obtained new characteristic vector.

The invention discloses a deep learning method and device for evaluating the carcinogenic risk of a compound based on multi-modal data fusion and a storage medium, and belongs to the technical field of chemical health risk evaluation, and the method comprises the steps: (1) building a carcinogenicity prediction data set; (2) constructing a cross-modal knowledge graph, and learning the cross-modal knowledge graph by using a convolutional neural network to obtain cross-modal feature representation; (3) extracting a molecular fingerprint feature representation and a molecular map feature representation, and fusing the molecular fingerprint feature representation and the cross-modal feature representation to obtain a fused feature representation; (4) a fusion model is constructed, a first deep neural network and a second deep neural network in the fusion model are used for processing different feature representations, and a classifier is used for prediction; (5) carrying out supervised training on the fusion model to optimize parameters of the fusion model; and (6) performing carcinogenic risk assessment on a to-be-detected compound by using the optimized fusion model. The method can comprehensively and accurately evaluate the carcinogenic risk of the compound and explain the carcinogenic mechanism.

The invention belongs to the field of bioinformatics, and particularly relates to a drug and target effect prediction method based on a Floyd algorithm network, which integrates technical means such as a Floyd algorithm, deep learning, a Kolmogorov-Arnod network, an attention mechanism and the like. The method comprises the following steps: firstly, preprocessing a drug molecular map and a protein map, extracting molecular structure features by using a Morgan fingerprint algorithm, and introducing a second-generation protein sequence pre-training model of an evolution scale modeling framework to extract protein semantic features; and then, respectively inputting the embedded features of the drug and the protein into a parallel Kolmogorov-Arnod network constructed by a Floyd algorithm to carry out feature mapping, and introducing a joint attention mechanism to model a high-dimensional interaction relationship between drug atoms and protein residues. Experimental results show that the method disclosed by the invention has relatively high accuracy and generalization ability in a drug-target affinity prediction task.

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 relates to the technical field of molecular property prediction, in particular to a molecular property prediction method based on double-graph collaborative representation and cross-view feature fusion, which comprises the following steps: automatically identifying key functional groups in a molecular graph through a graph attention mechanism (GAT), and constructing a Motif graph to represent a local chemical environment of the molecular graph; the Motif graph can effectively capture local structure characteristics in molecules. The Motif graph and the molecular graph are respectively processed through GAT, information of global features and local features is respectively extracted, and the two features are fused through cross attention to obtain fused molecular graph features, so that more comprehensive molecular feature representation is obtained, and the comprehensiveness of molecular representation and the performance of the model are improved. Molecular fingerprint information is jointly processed by using a bidirectional gating cycle unit (Bi-GRU) and a multi-head attention network, the expression ability of the model to complex molecular fingerprint data is enhanced, and the accuracy of molecular property prediction and the generalization ability of the model are improved.

The invention relates to the technical field of computational biology, and discloses a high-precision drug screening method and system based on hierarchical multi-modal representation learning, and the method comprises the steps: carrying out the feature extraction of an amino acid sequence of a target protein through a plurality of pre-trained protein language models; based on the three-dimensional structure of the target protein, multi-scale graph representation and ligand molecular graph representation of the protein are constructed; performing multi-modal feature learning on the multi-scale graph representation and the ligand molecular graph representation of the constructed protein by using a neural network based on message passing; and based on the multi-modal features, constructing a binding affinity prediction model for drug screening, and completing a drug screening task. A complex interaction relationship in a protein and ligand graph structure is effectively captured by using a multilayer graph neural network, and efficient coding of protein-ligand pair features is realized. The surface, the structure and the ligand embedding vector of the protein can be extracted, and the accuracy and the efficiency of protein-ligand interaction prediction are improved.

The invention discloses an OLED material stability prediction method based on a heterogeneous graph neural network, and the method comprises the steps: constructing a molecular graph based on the molecular structure of an OLED material, carrying out the data enhancement of the molecular graph, fusing the features of the data-enhanced molecular graph with multi-modal data, and obtaining the feature data of the molecular structure of the OLED material; oLED material molecular structure feature data are transmitted to a heterogeneous graph neural network, deep feature information in a molecular graph is learned, and OLED material molecular structure fusion features are obtained; and constructing an OLED material thermal stability prediction model based on the depth operator network, and inputting the OLED material molecular structure feature data and the OLED material molecular structure fusion features into the OLED material thermal stability prediction model to obtain a prediction result of the OLED material stability. According to the method, the stability prediction performance of the OLED material can be remarkably improved, and a new path is provided for material design and optimization.

The embodiment of the invention discloses a training method, a prediction method, a device and equipment for a molecular attribute prediction model, and relates to the field of artificial intelligence assisted pharmacy. Comprising the steps that in a first training stage, in order to improve the interpretability of a sub-graph extraction model, the sub-graph extraction model and a sub-graph prediction model are trained based on a sample molecular graph, and the sub-graph extraction model obtained through training is used for extracting at least two interpretable key sub-graphs from the molecular graph; and in a second training stage, on the basis of the sub-graph extraction model obtained by training in the first training stage, training the sub-graph prediction model based on the sample sub-graph by taking improvement of the distribution external generalization ability of the sub-graph prediction model as a target, the sub-graph prediction model obtained through training is used for predicting molecular attributes of the molecular graph based on the at least two key sub-graphs of the molecular graph. By adopting the method provided by the invention, the interpretability of molecular attribute prediction and the generalization ability outside distribution can be improved.

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.

A molecular structure prediction method based on a multi-granularity graph neural network comprises the steps of data set preprocessing, multi-granularity level data feature construction, multi-granularity graph neural network construction, multi-granularity graph neural network training, multi-granularity graph neural network verification and multi-granularity graph neural network testing. The key connection relation between atoms, the incidence relation of substructure keys and graph-level global feature representation are determined in the molecular graph, and the problem of local information loss in graph representation learning is effectively relieved; a message passing mechanism of graph neural sub-networks with different granularities is improved, a multi-granularity molecular graph data structure is trained, unique information of each hierarchical molecular structure is fully utilized, and the modeling capability of a model for a complex chemical structure is enhanced. The method has the advantages of being high in prediction accuracy, reducing errors, relieving local information loss existing in the prediction method, being good in prediction interpretability and the like, and can be applied to the technical fields of drug discovery, molecular property prediction and the like.

The invention relates to a semi-template drug molecule inverse synthesis prediction method based on graph-sequence pre-training. The method comprises the following steps: selecting a public chemical molecule data set N; selecting a molecular sample from the N, and then extracting a molecular graph and a word segmentation sequence from the molecular sample; obtaining a sequence embedding matrix and a graph embedding matrix of the i by utilizing the word segmentation sequence of the i and the molecular graph; constructing an overall model M and a loss function Ltotal used for training the M; and training M in two stages by using AdamW to obtain a trained model M. By adopting the method disclosed by the invention, reactant atoms for generating a product can be accurately reduced, a starting material of the product is defined, and the research and development success rate of medicines or other products is improved.

The invention discloses a molecular performance prediction method and system based on layered characterization, and belongs to the field of molecular property prediction. The method comprises the following steps: constructing a multi-layer graph structure, including an atomic layer, a group layer and a molecular layer, based on a molecular graph structure and an SMILES sequence; designing a hierarchical information interaction mechanism combining intra-layer polymerization and inter-layer information transfer, and performing multilayer structure semantic fusion to obtain characterization of an atomic layer, a group layer and a molecular layer; a hierarchical graph representation fusion module is constructed based on an attention mechanism, differential fusion is carried out on representation of an atomic layer, a group layer and a molecular layer, expression of key structure features is automatically enhanced, and unified fusion molecular representation is constructed; according to the method, a prediction task-oriented loss function is constructed, fusion molecular representation is input into a full-connection neural network prediction model, end-to-end molecular performance prediction is realized by training and learning a mapping relation between the fusion molecular representation and molecular properties, and classification and regression performance in a molecular property prediction task is remarkably improved.

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 molecular map feature extraction method and device based on multi-scale information and a medium, and relates to the technical field of molecular activity prediction. The molecular map feature extraction method comprises the following steps: extracting atomic-scale features, bond-scale features and conformation features of target molecules; and fusing the atomic-level features, the bond-level features and the conformational features to obtain molecular map features of the target molecules. According to the method, the atomic-level features, the bond-level features and the conformation features of the molecules are extracted and fused to serve as input features of activity prediction, understanding of a model on the relationship between the atomic features, the bond features and the spatial features of the molecules and the activity is promoted, and therefore the molecular activity is predicted more comprehensively.