93 results about "Extraction drug" 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 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 discloses an adverse drug reaction event recognition method and system based on multivariate knowledge mixed retrieval enhancement, and the method comprises the steps: extracting drug entities from a to-be-recognized clinical disease course record, segmenting the disease course record, and obtaining a drug entity set and a sentence set; for each extracted drug entity, retrieving drug concept knowledge having a hyponymy relationship with the drug entity and drug adverse reaction knowledge having an adverse reaction relationship with the drug entity in a pre-constructed multivariate knowledge base; for each sentence obtained through segmentation, searching suspected adverse drug reaction events meeting a first similarity requirement and drug field text knowledge meeting a second similarity requirement in a pre-constructed multivariate knowledge base; and finally, calling a large language model, taking all the retrieved knowledge as reference knowledge, and identifying the adverse drug reaction event from the to-be-identified clinical disease course record. According to the invention, the accuracy and reliability of adverse drug reaction event identification can be improved.

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 molecular structure characterization-based drug-drug interaction prediction method, which comprises the following steps of: acquiring drug molecule data and drug-drug interaction data, performing standardized preprocessing on the data, and constructing a drug molecule map according to the drug molecule data; inputting the drug molecule map into a model, extracting drug molecule multi-scale features through a multi-scale map convolutional network, and fusing the drug molecule multi-scale features through a dynamic attention mechanism to obtain drug molecule features; based on the drug molecular characteristics, through a full-connection neural network, learning the relationship between the drug molecular characteristics and the drug-drug interaction, and obtaining the prediction probability of the drug-drug interaction; and training the model by adopting a joint loss function according to the drug-drug interaction data and the prediction probability, and applying the trained model to drug-drug interaction prediction. And through the multi-scale image convolutional network, dynamic attention fusion and joint optimization, the prediction accuracy is significantly improved.

The invention discloses an antibody-coupled drug combination prediction method based on a virtual graph and multi-scale characteristics, provides a method for constructing initial characteristics of a drug carrier, a linker, an antibody heavy chain and light chain and an antigen target protein, provides a method for extracting drug sequence characteristics by using 1DCNN, and designs a drug characteristic extraction method based on graph virtual nodes. Virtual nodes and virtual edges are introduced into a molecular structure diagram, a Graph Transform is used as a diagram feature extractor, virtual node features are used as drug representation, then protein and drug features are input into a feature fusion module, an attention mechanism and a gating jump connection mechanism are added in feature fusion, and the protein and drug features are fused. And the feature information of different hidden layers is fused while the potential interaction is captured, so that higher-precision affinity prediction is realized. The method can solve the technical problem that the structural features of the antibody-coupled drug are difficult to extract in the prior art and the technical problem that the features of different components are difficult to fuse.

The invention relates to a drug relocation method, in particular to a drug relocation method using neighborhood information and a weighted fusion network. The invention aims to solve the problems that the existing drug relocation method is a shallow prediction model, the characteristics of drugs and disease nodes cannot be deeply known, and the relocation result of the existing drug relocation method is biased to common infectious diseases. The method comprises the following steps: extracting a data sample of the action relationship of drugs on diseases; in the drug-to-disease effect relation graph, learning interaction characteristics between drugs and disease nearest neighbors by using a weighted graph convolutional network; aggregating the learned characteristics of the drugs and the diseases; hidden high-order nonlinear features are obtained through a multi-layer fusion network; inputting the action relationship of the drugs in the training set on the diseases into the model, training the model, and obtaining a final prediction model after multiple iterations; and inputting the drug pairs in the test set into the prediction model to obtain a prediction result. The invention belongs to the technical field of drug relocation.

The invention discloses an intelligent decision-making method for a cerebral ischemia drug administration time window, and relates to the technical field of intelligent decision-making. The method comprises the following steps: S1, acquiring parameters, and setting a cerebral ischemia drug configuration scheme; s2, extracting a medicine characteristic parameter set and a medicine taking influence parameter set; s3, calculating a drug combination risk parameter set; s4, performing hierarchical classification processing on the drug combination risk parameter set, and performing drug correlation analysis; s5, performing weighted fusion on a correlation analysis result in combination with a drug influence attenuation rule, generating a drug administration time window evaluation parameter, and adding a label; and S6, according to the parameters, the labels and the configuration scheme, generating a cerebral ischemia drug administration time window intelligent decision scheme, and performing storage and backup. According to the method, by combining directional extraction of the drug characteristic parameter set and the drug taking influence parameter set with Pearson correlation algorithm screening, decision errors caused by data deviation are avoided.

A real-time drug interaction alert system using AI-powered analytics, consisting of: a wearable or implantable biosensor device configured to continuously measure physiological parameters of a subject, including at least heart rate, blood pressure, blood oxygen saturation, skin temperature, and biomarkers related to liver and kidney function; a communication module integrated into the device that can securely transmit physiological data to a cloud-based analysis engine via wireless protocols such as Bluetooth Low Energy (BLE), Wi-Fi, or LTE-M; a cloud-hosted artificial intelligence (AI) inference engine configured to perform multimodal drug-drug interaction risk analysis, the engine comprising at least a convolutional neural network (CNN) for time-series analysis of biosignal patterns, a natural language processing (NLP) module for extracting drug information from structured and unstructured medical sources, and a Bayesian reasoning module for modeling probabilistic risk interactions between drugs and patient-specific variables; a pharmaceutical input interface for capturing medication data by at least one of the following methods: manual entry, optical character recognition (OCR) of the packaging, or near field communication (NFC) scanning of embedded tags; an alert dissemination module configured to trigger multi-channel alerts, including haptic signals via the wearable device, audiovisual notifications via a paired smart device, and the secure transmission of interaction reports to designated healthcare professionals based on severity thresholds.

The invention discloses a drug target binding affinity prediction method based on collaborative attention, and belongs to the technical field of natural language processing, and the method comprises the steps: building a CLAT-DTA prediction model comprising an input data representation module, a feature extraction module, an information fusion module and a prediction module; converting drug molecules into fingerprint representation, and pre-training protein sequence data by using ESM; extracting drug data by using Encoder, and extracting protein data by using Bi-LSTM (Bidirectional Long Short-Term Memory); fusing the drug target data using a collaborative attention mechanism; three-layer full ligation is used to predict drug target binding affinity. According to the method, important information can be better polymerized, and the binding affinity between the drug and the protein can be predicted.

The invention provides a collaborative drug prediction method and system based on a heterogeneous hypergraph, and relates to the field of collaborative drug prediction.The collaborative drug prediction method comprises the steps that information of various cells and structures of various drugs are obtained, and corresponding cell fusion features and drug structure features are obtained; constructing a heterogeneous hypergraph based on the drug structure features and the cell fusion features, performing convolution processing to obtain drug node features, cell node features and hyperedge features, and training a neural network by using the extracted features to obtain a prediction model; and obtaining a to-be-detected drug, and inputting the to-be-detected drug into the prediction model to obtain a prediction result. According to the method, the heterogeneous hypergraph is constructed based on the collaborative information of the known drug combination in the cells, and the feature information of the drug nodes, the cell nodes and the hyperedges is extracted by adopting the difference of the multilayer hypergraph convolutional layers, so that the defects of excessive smoothness and redundancy of the information during feature extraction of the heterogeneous network by a common graph neural network are overcome; and a complex relation mode is difficult to capture.

The invention relates to the technical field of drugs, in particular to a drug relocation method for integrating multi-source biological data based on a meta-path, which comprises the following steps: acquiring an original data set; constructing a fusion heterogeneous network, and fusing the incidence matrixes of every two combinations of drugs, diseases and proteins and the respective similarity matrixes to obtain a heterogeneous graph; designing a multi-level association path based on meta-path and similarity negative sampling, fusing similarity information of same-level networks in the heterogeneous network based on a similarity principle, and selecting a negative link sample; extracting drug characteristics; constructing an SMS-MPF module, carrying out migration on the meta-paths through a heterogeneous network, extracting and fusing features among different meta-paths, and obtaining associated features of drugs and diseases; and inputting the disease phenotype similarity matrix, the drug characteristics and the associated characteristics of the drugs and the diseases into an associated prediction module to obtain final node representation. The method solves the problem that an existing method has significant defects in selection of positive and negative samples.

The invention relates to the technical field of bioinformatics and artificial intelligence, and provides a drug-target interaction prediction method and system based on multi-modal feature fusion, and the method comprises the steps: carrying out the word segmentation coding of an obtained to-be-recognized drug sequence and a target sequence, and respectively extracting the subsequence features of a drug and a target; constructing a two-dimensional molecular diagram of the drug and a three-dimensional structure diagram of the target, and respectively extracting diagram structure characteristics of the drug and the target; fusing the subsequence features and graph structure features of the drug and the target through a cross attention mechanism; and carrying out interactive fusion by adopting a bidirectional collaborative attention mechanism to obtain a prediction result of the binding affinity of the drug and the target. According to the invention, by combining the multi-modal complementary information of the drug and the target, deep interaction between different modal features is deeply mined; meanwhile, a two-way collaborative attention mechanism is introduced into interaction modeling of the drug and the target, and the accuracy of drug-target binding affinity prediction is effectively improved.

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.

The invention discloses a drug incompatibility detection method based on fusion of a map and a large language model. Comprising the following steps: standardizing drug names in a prescription, mapping the drug names into uniform national standard codes, and generating a drug pair set; and extracting a drug interaction triple in combination with a large language model in the medical field, and constructing a heterogeneous drug knowledge graph. Based on a graph neural network and a comparative learning mechanism, embedded representation learning is performed on the knowledge graph, and the discrimination capability of drug node features is improved. Path retrieval and risk scoring are carried out on the drug pairs with the explicit interaction paths; and for a drug pair without an explicit path, screening alternative drugs by using chemical component similarity, inputting a pre-trained large language model through structured prompt, predicting a potential interaction relationship and credibility, and supplementing implicit risk information. According to the method, the problems of insufficient coverage and limited reasoning ability of the traditional knowledge graph are effectively solved, and the comprehensiveness and reliability of detection are improved.

The invention provides a drug target prediction method based on cross-modal attention and uncertainty evaluation, and belongs to the technical field of drug target prediction. In order to solve the technical problems that the existing drug target prediction lacks quantitative evaluation on the reliability of a prediction result and a nonlinear interaction relationship between a drug and a target is difficult to establish, the method comprises the following steps: collecting data, and fusing graph structure features and sequence features of extracted drug molecules to obtain a final code of the drug molecules; extracting amino acid sequence characteristics of the target protein, and constructing protein sequence characteristic expression; inputting the drug molecular features and the protein sequence features into a cross-modal attention module, and aligning and fusing the drug features and the protein features by using a bidirectional cross attention mechanism to obtain drug-target combined feature representation; introducing an uncertainty quantification mechanism, and outputting uncertainty estimation of a drug-target interaction prediction label and a prediction result; the method is used for predicting drug target interaction.

The invention discloses a cross-modal interactive fusion structure diagram feature extraction method, and the method employs a multilayer diagram isomorphic network to extract local neighborhood features and global topological features of drug molecules, thereby improving the capability of a model for capturing the change of a fine structure. A convolutional neural network and a multi-head convolutional self-attention mechanism are integrated to comprehensively capture local features and long-range dependency relationships in a protein sequence; and interaction information between a drug and a target is fully modeled through a cross-modal interaction fusion module based on a gating mechanism, so that more accurate DTI prediction is realized.

The application relates to a drug-lncRNA relationship prediction method and system based on embedding constraints, and the method comprises the following steps: collecting lncRNA-drug association data and preprocessing, and constructing a data set; based on the data set, an lncRNA-drug association matrix is constructed, and an lncRNA-drug bipartite graph is extracted; based on the lncRNA-drug association matrix, an lncRNA similarity matrix and a drug similarity matrix are obtained, and dimension reduction processing is performed; the lncRNA-drug bipartite graph and the dimension reduction processed lncRNA similarity matrix and drug similarity matrix are input into an LDA-GNN model and an LDA-DHG model with an embedding constraint strategy, and the probability score of lncRNA and drug association is obtained; and based on the probability score, the relationship prediction of the drug-lncRNA is realized. Through the embedding constraint strategy, the prediction performance and the distinguishing ability of the model are significantly improved.

The present application belongs to the technical field of biological information, and particularly relates to a drug-target interaction prediction method based on multi-granularity representation; the method comprises the following steps: extracting an enhanced molecular representation of a drug molecule by using a hierarchical network; modeling first-order information and second-order information in an amino acid sequence respectively based on adjacent residues in the amino acid sequence, so as to extract multi-order sequence features; processing multiple sequence information by using a Pconsc4 tool to obtain spatial structure information representation of a protein; splicing the enhanced molecular representation, the multi-order sequence information and the spatial structure information representation of the protein to obtain fusion features; inputting the fusion information into an interaction prediction network to obtain a drug-target interaction prediction result; the present application can not only solve the weakness of existing methods that only focus on single-granularity information, improve prediction accuracy, but also show interpretability.

The present invention provides a method for predicting drug-target interactions by integrating multi-layer drug structure information. First, the drug and target information in the pharmacomics database is preprocessed to extract the drug and target information with interactions; secondly, the drug SMILES molecular fingerprint is represented as a molecular graph structure, and the molecular completion graph convolutional neural network and the Transformer network are used to extract drug feature information; then, the target sequence information is processed using a convolutional neural network, and the target feature information is extracted; finally, the extracted drug feature information and target feature information are sent to a classification model for training, the model is saved, and the relationship between the drug and the target is predicted. The present invention effectively extracts the feature information in the drug molecular structure, has a higher accuracy rate when predicting the drug-target relationship, improves the efficiency and accuracy of drug-target relationship verification, effectively shortens the drug research and development cycle, and greatly reduces the cost of new drug research and development.

The application discloses a DDI prediction method based on a Siamese structure and graph contrast learning, comprising the following steps: collecting drug-drug interaction text data and physical and chemical property data files and targeting relationship data files of drugs; extracting the physical and chemical property characteristics and the targeting relationship characteristics of the drugs, fusing the characteristics to obtain initial characteristics based on the physical and chemical properties and the targeting relationship; calculating a drug-drug interaction adjacency matrix, combining the initial characteristics to construct a drug-drug interaction heterogeneous graph; inputting the heterogeneous graph into a Siamese structure-based graph contrast learning model to learn and obtain embedding characteristics of drug nodes; and using a link prediction method to calculate the score of edges between any two drug nodes. The application can alleviate the problem that the drug targeting relationship characteristics and the drug-drug interaction text data are difficult to be used alone when the data are sparse and have an impact on the performance of the model, improves the accuracy of drug-drug interaction prediction, and can be applied to identifying potential interactions between drugs.

The invention relates to a drug-lncRNA relationship prediction method and system based on embedding constraints, and the method comprises the steps: collecting and preprocessing lncRNA-drug associated data, and constructing a data set; based on the data set, constructing an lncRNA-drug association matrix, and extracting an lncRNA-drug bipartite graph; based on the lncRNA-drug association matrix, obtaining an lncRNA similarity matrix and a drug similarity matrix, and carrying out dimension reduction processing; inputting the lncRNA-drug bipartite graph and the lncRNA similarity matrix and the drug similarity matrix which are subjected to dimension reduction processing into an LDA-GNN model and an LDA-DHG model which are provided with an embedded constraint strategy, and obtaining a probability score associated with the lncRNA and the drug; and based on the probability score, realizing drug-lncRNA relationship prediction. According to the method, through an embedded constraint strategy, the prediction performance and the distinguishing capability of the model are remarkably improved.

The application discloses a drug synergistic effect prediction method based on a biomedical knowledge graph and a Transformer, and relates to a drug synergistic effect prediction method. In order to solve the problem that a traditional drug combination discovery process mainly depends on clinical trials, which is not only time-consuming and laborious, but also high in cost and risky to patients, the application comprises the following steps: extracting drug data samples, generating a data set, dividing training and test sets, performing network training and testing; constructing a biomedical knowledge graph; converting a sequence list of a drug structure into a graph by using Rdkit; mining a subgraph of the knowledge graph by using a multi-hop subgraph mining network; learning feature representations of the knowledge network and the drug molecular graph by using a relation-aware Transformer, and performing fusion; performing synergistic effect prediction between drug pairs by using a multilayer perception machine; inputting drug pairs in a training set into the above model; inputting drug pairs in a test set into the prediction model to obtain a prediction result. The application belongs to the technical field of drug synergistic effect prediction.

The application discloses a drug interaction prediction method based on DS evidence theory. First, multi-modal data of drugs are acquired to construct a drug interaction dataset; then, multi-modal features of the drugs are extracted as inputs of a deep neural network, a multi-modal drug interaction prediction model is constructed, and the prediction model is trained; then, the trained prediction model is used to predict drug interactions. The prediction model uses Dirichlet distribution to calculate classification probability and uncertainty value, and obtains multi-modal prediction results and overall uncertainty value through Dempster-Shafer evidence theory fusion. Compared with existing prediction methods, the application uses multi-modal drug data, has a lower prediction error, can provide meaningful uncertainty information for guiding multi-modal prediction fusion and expressing prediction confidence, and has higher reliability and robustness.

The invention belongs to the technical field of intelligent medical treatment, and discloses a drug relocation method and system based on implicit interactive adjacency matrix remodeling, and the method comprises the following steps: S1, constructing a fusion drug-disease incidence matrix; s2, extracting drug domain features and disease domain features by adopting a parameter sharing double-path self-attention mechanism; s3, mapping the drug / disease features to the dimension space of the opposite side through the trainable projection matrix, and generating an implicit interaction prediction matrix; and S4, dynamically adjusting the contribution degree of the positive and negative samples through the weight lambda to solve the problem of data sparsity. According to the method, drug domain and disease domain feature decoupling learning is realized through a dynamic adjacency matrix by adopting a double-path self-attention mechanism, cross-dimensional implicit interaction prediction is completed by utilizing a trainable projection matrix, feature representation is iteratively optimized in combination with a recurrent neural network, the noise influence of artificial similarity data can be effectively reduced, and the accuracy of the method is improved. And the method has remarkable advantages in actual scenes such as Parkinson's disease drug relocation and the like.

The application is based on a method for predicting anticancer drug sensitivity by deep factor decomposition machine, characterized in that it comprises the following steps: step 1, constructing a data set and preprocessing; step 2, constructing a deep factor decomposition machine DeepFM model: the main idea of the deep factor decomposition machine DeepFM model is to construct a factor decomposition machine FM model, construct a deep neural network model DNN, and fuse the factor decomposition machine FM and the deep neural network to construct a deep factor decomposition machine DeepFM model; step 3, training and evaluating the model results of each stage, and dividing the anticancer drug sensitivity data obtained in step 1 into 5 parts, performing 5-fold cross-validation, and training the model results of each stage in this stage. The application separately models the low-order feature combination of the drug, and fuses the high-order feature combination to construct a model that fuses the deep neural network model and the factor decomposition machine model, better extracts the drug feature structure, and makes the constructed model more accurate and higher in precision.

The invention provides a drug interaction relationship prediction method based on LLMs and ICL, belongs to the technical field of drug interaction relationship prediction, and solves the problems that a large language model is high in data set arrangement cost in the DDI-related characteristic prediction field, the generalization ability in a zero sample scene is limited, and the prediction efficiency is high. The method comprises the following steps: extracting a drug data sample, and making a data set; screening the drug data samples in the data set, and selecting the first K positive and negative sample drug pairs with the highest similarity; constructing a prompt of a drug relationship prediction model based on context learning and the selected positive and negative sample drug pairs; and mixing all the drug relationship prediction models by using MOE, evaluating scores of the drug relationship prediction models, and performing weighted fusion according to the scores to obtain a DDI final prediction result.

The invention provides an intelligent drug interaction prediction method and system based on a knowledge graph, and relates to the technical field of medical information, and the method comprises the steps: extracting drug molecular structure and metabolic data, calculating the conformation overlapping degree, determining a metabolic pathway overlapping site, calculating the competitive binding probability, analyzing the drug distribution state, and judging a metabolic cycle overlapping interval. Determining a concentration accumulation range, evaluating a metabolic inhibition level, generating early warning data and updating the knowledge graph. The drug interaction can be accurately predicted, the medication safety is improved, and the adverse reaction risk is reduced.

The invention relates to the technical field of medication data processing, in particular to a medication reminding system and method for the elderly, and the system comprises a medicine dependence analysis module which extracts medicine dependence information based on collected daily medication data of the elderly, builds a directed graph model of medicine taking, and carries out the medicine taking of the elderly in the directed graph model of medicine taking, the nodes represent medicines to be taken, and the edges represent dependency information among the medicines. According to the method, the time, the interval and the interaction information among the medicines in the medication data of the old people are extracted, and the relevance among the medicines is displayed in a quantitative mode. The reminding time is optimized according to the dependency relationship of the medicines, the coordination effect of the positive correlation medicines is enhanced, and the potential conflicts of the negative correlation medicines are reduced. Based on the reminding response data of the user, the reminding frequency and the distribution sequence are dynamically adjusted, interference of multiple times of abnormal reminding on the user is reduced, and the actual execution effect of system reminding is further improved.

The application discloses a brain hemorrhage perioperative medication scheme intelligent recommendation method and system, acquires physiological parameters and operation time sequence characteristics of a patient, constructs a patient risk image, carries out risk matching with a drug knowledge base to generate a candidate drug set, extracts drug attribute characteristics to determine a drug function grouping, carries out adaptability scoring based on the patient risk image to generate a grouping adaptability matrix, carries out drug interaction analysis to identify synergistic pairs and antagonistic conflict pairs, carries out combination optimization on the synergistic pairs to generate a synergistic scheme, carries out conflict exclusion screening on the antagonistic conflict pairs to generate safety constraints, constructs a medication scheme topology, carries out dose sensitivity anomaly detection and correction to generate a correction factor to generate a time-sharing drug administration scheme, carries out real-time monitoring to carry out perfusion evaluation to determine a safety level and trigger dose adjustment, forms an optimized medication scheme, and realizes intelligent recommendation of perioperative medication.