1143 results about "Drug molecule" patented technology

The present invention provides a method and intelligent agent for data collection and drug safety signal mining for PMS, which relates to the field of drug monitoring technology, including standardizing PMS data using a medical text pre-training model, and integrating drug molecular characteristics and clinical medication information to construct a multi-layer drug knowledge graph. A heterogeneous graph neural network with a time series feature extraction function is used to represent the graph, and the drug action mechanism rules are integrated into the training process to obtain a node vector representation of multi-dimensional information fusion. Based on the node vector representation, a multi-task learning framework is constructed to simultaneously complete the drug-adverse reaction association probability prediction and risk level prediction, and combine time series analysis to detect dynamic change characteristics to generate candidate signals. Finally, a hierarchical evaluation system is constructed to verify the candidate signal, and its reliability is confirmed in combination with clinical data, the target signal is output, the warning level is determined, and differentiated monitoring cycles and trigger thresholds are set to perform real-time risk warnings.

The invention relates to the field of drug-target affinity prediction, in particular to a drug-target affinity prediction method based on a multi-scale mixed attention network. According to the method, global residue characteristics can be learned by protein pocket residues by utilizing a self-attention mechanism, so that the global characteristics of the protein are learned. In addition, a cross-modal feature fusion mechanism is utilized, protein and drug structural features are enhanced in sequence features, cross-modal feature fusion in entities is enhanced, meanwhile, a cross-entity interaction module is utilized to recognize key atoms of drug molecules or key residues of protein, and therefore the overall performance of the model is improved. Finally, multi-level protein feature extraction is further introduced to further extract protein features.

The invention discloses a drug target affinity prediction method and system based on a multi-scale protein attention mechanism, and belongs to the crossing field of bioinformatics and artificial intelligence. The method comprises the following steps: firstly, extracting protein sequence features through an ESM2 pre-training model, predicting that a three-dimensional structure is converted into a two-dimensional contact graph, and extracting spatial topological information in combination with a graph convolutional network; a two-dimensional attention mechanism is innovatively designed, structural features are taken as query vectors, sequence features are taken as key value pairs, and cross-modal feature fusion is realized by dynamically associating sequence semantics and spatial proximity relationships through multiple attention. Drug molecules are characterized by adopting MACCS fingerprints, are spliced with protein multi-modal features and then are optimized through a deep network, and finally an affinity value is output through a regression prediction module. According to the technology, the problem of protein heterogeneous data fusion is effectively solved, the generalization ability to unknown targets is remarkably improved, an efficient calculation tool is provided for new drug research and development and drug relocation, and the drug research and development cost can be reduced.

The invention discloses a drug and target interaction prediction method based on multi-scale convolution feature fusion, which comprises the following steps: acquiring drug molecule data, target protein data and drug and target interaction data, constructing a drug molecule map according to the drug molecule data, and coding a target protein sequence according to the target protein data; inputting the drug molecular map into a model, and obtaining drug features through a multi-scale map convolutional network and a dynamic gating attention mechanism; inputting a target protein sequence into the model, and obtaining target features through hierarchical cavity convolution and a bidirectional gating cycle unit; through multi-head cross attention, the drug features are aligned with the target features, local and global cross-modal fusion is carried out, and drug target fusion features are obtained; based on the drug target fusion features, outputting a drug and target interaction prediction probability; and training the model according to the drug and target interaction data and the prediction probability, and applying the trained model to drug and target interaction prediction.

The invention discloses a drug target activation and inhibition relation prediction method based on a depth map neural network, and aims to improve the modeling precision and prediction performance of an activation or inhibition action mechanism between a drug and a target. According to the method, on the basis of a fine-grained graph interaction modeling mechanism, multi-scale structural characteristics of drug molecules and three-dimensional space structural information of protein residue levels are fused, and a heterogeneous interaction graph between drugs and proteins is constructed. The method comprises the following steps: firstly, acquiring a drug-target sample with an activation / inhibition tag through a public database, predicting a protein structure by utilizing AlphaFold2, and constructing a protein residue map and a drug molecular map; multi-scale structure semantic representation is obtained through sub-graph decomposition, atomic-scale feature extraction and graph neural network coding of drug graph features; protein graph node features are combined with context embedding generated by a pre-training language model, DSSP coding, secondary structure spectrum and atomic structure features are constructed, and edge features are designed based on the geometrical relationship between residues. Then, based on constraints such as spatial distance and biochemical similarity, a fine-grained mapping relation between drug atoms and protein residues is established, an interaction graph is constructed, and coding is carried out through a GraphSAGE network; and finally, fusing the interacted multi-source embedding, and completing the prediction of the activation / suppression relationship through a multi-layer perceptron. A cross entropy loss function, an Adam optimizer and hyper-parameter grid search are adopted in model training; in the evaluation stage, five-fold cross validation and an independent test set are adopted, and indexes such as the accuracy rate, the recall rate, the F1 score, the specificity and the Morse correlation coefficient are used for comprehensively evaluating the performance of the model. Experimental results show that compared with an existing method, the method has the advantages that the prediction accuracy and mechanism interpretability are remarkably improved, and the method has good generalization ability and application prospects and is suitable for multiple fields of drug action mechanism research, new drug discovery and the like.

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 belongs to the technical field of deep learning, and discloses a drug and protein interaction prediction model construction method based on multi-modal fusion. Due to the lack of protein three-dimensional data, the three-dimensional data of the protein is subjected to high-accuracy complementation by using a prediction model AlphaFold 2, and for the modal fusion problem, the drug molecules are fused at the atomic level with finer granularity, so that the multi-modal fusion effect is further enhanced, and the multi-modal fusion effect is improved. Protein 1D data and protein 3D data are processed by using Cross-Attention, the processed protein 1D data and protein 3D data are subsequently fused by using Cross-Attention, multi-modal fusion is performed by using Cross-Attention, so that the interaction relationship between modals can be carefully captured, and in a word, the DTI task effect is improved by designing a multi-modal fusion mode.

The invention relates to the technical field of pharmaceutical knowledge maps, in particular to a pharmaceutical knowledge map construction method and system based on artificial intelligence, and the method comprises the following steps: querying and collecting a molecular structure of a drug and a corresponding target protein sequence through a database, and carrying out the numerical coding of the molecular structure data of the drug, molecular fingerprints and protein structural domain features are extracted, and a drug and protein feature set is formed by combining drug chemical attributes and protein sequence features. According to the invention, through accurate analysis of the molecular structure of the drug and the target protein sequence thereof, the innovative scheme significantly enhances the understanding of the interaction of the drug and the protein, so that researchers can directly extract key features from data and monitor the dynamic change of the drug effect, thereby not only accelerating the development process of the drug, but also improving the development efficiency of the drug. By dynamically tracking the interaction between the side effect of the medicine and the pathological characteristics, the scheme provides powerful data support for personalized medical treatment.

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 a five-membered sulfur-containing heterocyclic nucleoside derivative and application thereof in an anti-human immunodeficiency virus (HIV) active medicine, and belongs to the technical field of medicinal chemistry. A series of five-membered sulfur-containing heterocyclic ring nucleoside compounds are synthesized through a Sulfa-Michael / Aldol cascade reaction. The result shows that in an anti-HIV virus test, the five-membered sulfur-containing heterocyclic nucleoside lead compound 42a has the optimal anti-HIV-1 virus activity, and a new structural unit is provided for antiviral candidate drug molecules.

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.

According to the pre-training language model-based drug-target interaction prediction method designed by the invention, natural language processing and graph neural network technologies are fused, context semantic features are automatically extracted from drug molecule SMILES character strings and protein sequences, and by constructing a graph structure taking drug-target pairs as nodes, the drug-target interaction is predicted. The weight of an edge is defined according to the similarity between embedded vectors, and a simplified graph convolutional network is adopted to carry out graph structure modeling to realize complex relation learning, so that the accuracy, generalization and interpretability of prediction are improved, the limitation of a traditional method on the problems of sparse feature expression, mutual information loss and'words outside a vocabulary 'is overcome, and the prediction accuracy, generalization and interpretability are improved. And finally, the accuracy of predicting the drug-target interaction relationship is improved.

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 provides an asymmetric drug interaction prediction method based on a diffusion diagram attention network, and relates to the technical field of drug interaction prediction.The method comprises the steps that a directed graph network is constructed by using chemical structure features of drug molecules, and features are extracted from drug applying and receiving perspectives through a two-way diagram attention network; therefore, the asymmetry between the drugs is effectively represented. Furthermore, a diffusion model is introduced to carry out noise injection and de-noising processing on a graph structure, so that the adaptability of the model to sparse data and the robustness of feature extraction are remarkably enhanced. Finally, multi-modal features are fused through a deep neural network, and high-precision prediction of drug interaction is realized. Compared with the prior art, the method has the advantages that the prediction accuracy exceeds that of the prior art, the dependence on labeled data is greatly reduced, the feasibility and generalization ability of the model in practical application are greatly improved, and a new technical path is provided for safety evaluation and precise medical research of drug combination therapy.

The invention provides a new drug molecule design method and device fusing convex optimization and evolutionary learning, and belongs to the field of biological information.The method comprises the steps that in a generative adversarial network, a generator is adopted to generate a small molecule sequence, and the generator is established by adding an attention mechanism model in a long-short-term memory network; evaluating the authenticity of the small molecule sequence by adopting a discriminator, wherein the discriminator is established by adopting a convolutional neural network improved based on convex optimization; updating the parameters of the generator by adopting a strategy gradient method, and updating the parameters of the discriminator by adopting a gradient descent method; and generating a final small molecule sequence by the generator corresponding to the final generator parameter. According to the method, the small molecules which are highly similar to original samples and have higher effectiveness and innovativeness can be generated, the diversity and quality of the generated molecules are improved, and particularly, the method has excellent performance in the aspect of generating molecules with specific functions.

The invention provides a liposome preparation and a preparation method thereof, the liposome preparation comprises an ion pairing agent, phospholipid or a derivative thereof, sterol, a lipid molecule derivative and a drug compound, the ion pairing agent comprises at least one of a compound (I) and a compound (II), and the compound (II) comprises at least one of a compound (III) and a compound (III). Structures in the compound (I) and the compound (II) contain a plurality of functional groups (such as amide, carbonyl, thioether and the like), so that abundant binding sites are provided for drug molecules, and the compound (I) and the compound (II) can interact with different types of drug compounds (such as fat-soluble and water-soluble drugs); the liposome preparation is allowed to be combined with various drug molecules through hydrogen bonds, hydrophobic interaction or electrostatic interaction, and the liposome preparation adapts to physicochemical properties of different drugs. The compound (I) and the compound (II) further comprise different hydrophobic groups (such as alkyl chains) and hydrophilic groups (such as sulfonic acid, carboxylic acid and the like), so that the liposome preparation has the amphiphilic characteristic, not only can encapsulate hydrophobic drugs, but also can encapsulate hydrophilic drugs, and the variety and range of encapsulated drugs are improved.

The invention discloses a drug molecule generation method capable of explaining a federal large model, and relates to the technical field of artificial intelligence, and the method comprises the steps: S1, through a data feature extraction and model initialization step, extracting multi-modal features from distributed data, constructing and initializing a drug molecule generation model, and obtaining a drug molecule generation model; s2, performing parameter optimization on a federal learning client through the large model, including local training fine tuning based on a distributed client, and integrating fine-tuned parameters into a global model of a server to improve model performance and data utilization efficiency, S3, based on the optimized model, generating drug molecular structure information including a molecular description sequence and a molecular graph; according to the drug molecule generation method capable of explaining the federal large model, on the basis of guaranteeing data privacy, the generation capacity and diversity of new molecules are remarkably improved, meanwhile, the transparency and credibility of the model are enhanced through causal analysis and an attention mechanism, the performance and robustness of the model are improved, and the method has wide application prospects.

The invention provides a drug-target correlation prediction method based on a hierarchical representation learning framework, and the method comprises the steps: screening high-information-density nodes based on a dynamic fluctuation threshold value, reducing low-noise nodes, and reconstructing topological connection according to a virtual edge weight formula; performing tensor splicing on the drug molecular features extracted by the dynamic neighborhood search framework and the protein semantic features generated by the denoising auto-encoder to form drug-protein pair joint feature representation; a multi-head attention mechanism is utilized to allocate dynamic weights for multi-view features based on drug-protein pairs, multi-view feature vectors are spliced, after key information is screened through the attention mechanism, the key information is input into a full-connection neural network, and a correlation prediction value is output based on the full-connection neural network. The association prediction method solves the problems that the prediction precision of the model on the complex biological interaction is poor, and the understanding ability of the model on the multilevel feature learning association in the complex biological network is seriously limited.

The invention belongs to the field of computational biology, and particularly relates to a drug target matching recommendation method based on a multi-view neural network, and the method comprises the steps: obtaining to-be-predicted drug target information, inputting the to-be-predicted drug target information into a trained drug target recommendation model, selecting a drug target whose prediction probability is greater than an interaction threshold, and recommending the drug target to a user; according to the method, the feature vectors of the drug molecules are extracted by using the graph neural network for carrying out message passing based on points and edges, modeling and feature extraction are carried out on the drug molecules from two angles of atoms and chemical bonds, and the globality of drug sequence information is considered at the same time; the atomic characteristics, chemical bond characteristics and sequence characteristics of the drug molecules are interacted by using a cross attention mechanism, the complex relationship between the drug molecules and the target protein is captured more accurately, and the accuracy of drug-target interaction prediction is improved.

There is provided a method for a machine learning based method of analysing drug-like molecules by representing the molecular quantum states of each drug-like molecule as a quantum graph, and then feeding that quantum graph as an input to a machine learning system.

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.

The invention discloses a drug interaction prediction method based on a double-view graph neural network, and the method comprises the steps: extracting features in a single drug view in a self-adaptive manner through a directional message passing neural network with a substructure attention mechanism, and extracting interaction features between the drug and the view through a graph attention network; and fusing to generate drug substructure features, so as to more accurately predict the interaction between drugs. And through the common attention mechanism module, drug interaction is converted into pairwise interaction between drug substructures, so that the capability of capturing drug molecular characteristics is greatly enhanced, and the prediction precision is improved. The invention can make full use of the interaction information between drugs and the interaction information of chemical substructures, studies the classification and identification problem of drug interaction prediction, and provides an efficient drug interaction prediction method.

The invention discloses a small molecule drug dynamic analysis system based on a spiking neural network, and relates to the field of drug analysis, and the system comprises a management module which is used for controlling the editing and sending of control instructions of all function modules and units; the data collection module is used for collecting known drug compound structure data and corresponding in-vivo performance data; the model construction module is used for constructing a pulse graph neural network model architecture, and taking the drug compound structure data collected by the data collection module and the corresponding in-vivo performance data as training samples for training; the pulse calculation of the biological neural network can better simulate the characteristics of a biological system and the dynamic process of the biological system, so that the prediction precision of complex drug molecule behaviors is improved, training is performed on input data distribution through dynamic adjustment, the pulse graph neural network has higher generalization ability, error sources are captured, and the prediction precision is improved. Model structures and parameters are continuously adjusted from model identification output, and dynamic optimization is achieved.

The invention relates to the technical field of biological medicine, in particular to a ferroptosis inhibition type phospholipid-like material and application thereof. The ferroptosis inhibition type phospholipid-like material is one of the following structural general formulas (1)-(5). The biomimetic phospholipid-like ferroptosis inhibitor has a long retention characteristic in main positions (cell membranes, endoplasmic reticulum and other organelle membranes) of cell ferroptosis, so that the ferroptosis inhibition efficiency is remarkably improved. The novel phospholipid-like material not only can be used as an active drug molecule, but also can be used as a pharmaceutic adjuvant for constructing drug delivery carriers such as lipidosome and micelle and implant coatings, and is suitable for various administration routes such as oral administration, injection and local administration. The novel biomimetic ferroptosis inhibitor can efficiently relieve cell ferroptosis and has a wide application prospect in the field of treating or retarding ferroptosis-related diseases.

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 relates to the technical field of medicines, and discloses a mannose / trimethyl chitosan modified MXene material (MTC / MXene), a preparation method and application of the mannose / trimethyl chitosan modified MXene material in prevention and treatment of inflammatory bowel diseases. The MTC / MXene material is obtained by preparing mannose-modified trimethyl chitosan and modifying the mannose-modified trimethyl chitosan on a single-layer MXene nanosheet, compared with a pure MXene material, MTC modification endows MXene with hydrophilicity, rich reaction sites are provided for drug loading, higher oxidation resistance is achieved, meanwhile, through surface functional groups and interlayer gaps of the MXene material, the MXene / MTC / MXene material can be used for preparing the drug, and the drug loading efficiency is improved. Different types of drug molecules are loaded and are used for realizing targeted delivery and controlled release of drugs.

The invention discloses a drug-target interaction prediction method based on graph neural network blocks. Accurate discrimination of the interaction between the drug and the target is a key stage of drug discovery, and the core of prediction of the interaction between the drug and the target is reasonable molecular characterization learning. The previous research has proved that GNN is effective in the aspect of drug feature coding. However, the GNN-based methods cannot effectively give consideration to local substructure characteristics of drug molecules and overall structure attributes of the molecules. For the current difficulty, a framework named GNNBlockDTI is provided. According to the GNNBlock DTI, multiple layers of GNNs are combined into a GNNBlock unit, and a feature enhancement strategy is introduced, so that a substructure mode of a drug map in a local range is captured. And filtering redundant information among the GNNBlocks by using a gating unit mechanism so as to obtain effective overall structure information of the medicine. According to the method, the global and local structure attributes of the drug molecules can be effectively coded, and the accuracy of drug-target interaction prediction is improved.

The invention relates to the technical field of drug research and development, in particular to a target AKT1 drug molecule discovery and delivery integrated system and method based on deep learning. Aiming at the problems of molecular design, optimization and delivery link separation and low research and development efficiency of drugs in the prior art, the system constructs a multi-module collaborative framework, and comprises a target analysis module for analyzing a target structure and formulating a generation strategy; the molecule generation and optimization module is used for generating and optimizing candidate molecules in combination with the generation model and reinforcement learning; the delivery scheme design module is used for matching a delivery carrier based on molecular physicochemical properties; and a verification module that predicts and evaluates the molecule-deliverer combination using molecular docking and ADMET. An evaluation result of the verification module is fed back to the molecule generation and optimization module to form a closed-loop optimization mechanism, so that an automatic process from target analysis to output of candidate drug molecules and matched delivery schemes thereof is realized. Compared with the prior art, the efficiency and success rate of early drug discovery can be improved.

The invention provides a novel chiral ferrocene bridged binaphthyl skeleton phosphine ligand and application thereof. The ligand has the advantages of low cost, simplicity in synthesis, air stability and the like. The ligands have good catalytic activity and enantioselectivity in the asymmetric hydrogenation reaction of ketone, and have certain industrial application prospects in the synthesis of pharmaceutical molecules.