860 results about "Drug molecule" patented technology

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 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.

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 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 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 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 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.

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 discloses a drug target binding affinity prediction method based on multi-scale feature fusion, and the method comprises the steps: introducing the multi-scale structural features of atoms, atomic groups and molecular levels in the expression of drug molecules, and combining a graph neural network and a sequence modeling module as a feature extractor; and deep interaction and fusion between different scale features of the protein and the drug are realized by using a multi-head bilinear cross attention mechanism, so that potential binding site information is effectively captured, and the accuracy and interpretation capability of affinity prediction are improved. The method not only overcomes the problems that a traditional machine learning method depends on manpower and is low in efficiency in feature construction, but also solves the technical bottlenecks that an existing deep learning method is only limited to local neighborhood information and cannot model global structural features, and the interaction relationship modeling capability is insufficient due to direct splicing of drugs and protein representation.

The invention discloses a chiral alpha alkyl beta hydroxyl phosphate compound as well as a preparation method and application thereof, and belongs to the technical field of chemical synthesis. In the presence of a catalyst, carbonyl of a substrate is reduced into hydroxyl to obtain a chiral alcohol compound, alpha-alkyl beta-ketophosphonate is reduced by an asymmetric catalytic hydrogen transfer method, and a reduced product can be used as a core skeleton of various drug molecules, so that the chiral alcohol compound is prepared. The compound has an obvious promotion effect on migration and repair of keratinocytes, and has great application potential in the aspects of wound healing, skin tissue soothing, repair, aging resistance and the like.

The present invention provides a binary or ternary complex. The complex can improve the stability of a drug, and in particular, inhibit the transesterification reaction within drug molecules. The complex can also improve the efficacy formulation performance of the drug, improving the safety and effectiveness of the drug.

The invention relates to the field of biological medicine, in particular to a prodrug structure with ROS signal response performance and a preparation method and application thereof. The prodrug molecule with ROS signal response performance developed by the invention can quickly release living active molecules in a disease signal ROS-enriched environment, and plays a role in targeted therapy. The method has good universality, prodrug modification can be carried out on multiple molecules, four kinds of drug molecules approved to appear on the market are included, and it is verified that the solubility and biocompatibility of drugs can be improved, cytotoxicity can be reduced, and the effect of targeted therapy can be improved. Based on the structural characteristics of the molecules, the molecules can also be complexed with PVA hydrogel, and responsive controllable release of the medicine is further achieved.

The invention belongs to the field of biological medicine, and provides a preparation method and application of a fluorescent probe molecule. The synthesized fluorescent probe molecule can be combined with an intramolecular fluorescence quenching principle to realize real-time monitoring of drug molecule release of the polypeptide drug conjugate in serum, and can also realize tracing of polypeptide in the polypeptide drug conjugate at a cellular level and monitoring of drug molecule release and dynamic distribution. The comprehensive visual research on the hybrid peptide is realized. The preparation method of the fluorescent probe is simple and easy to implement. The method provides an effective visual tool molecule for research and development of the polypeptide drug conjugate. The monitoring method disclosed by the invention is relatively simple and convenient to operate, good in monitoring result stability, strong in monitoring capability and mild in condition, provides an efficient tool molecule for development of the polypeptide drug conjugate, and has relatively high practical application value.

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 belongs to the technical field of artificial intelligence algorithm and bioinformatics crossing, and relates to a drug interaction prediction method based on multi-modal molecular characterization. According to the method, through the edge perception GCNII architecture and the Hop2Token multi-hop coding mechanism, effective modeling of atomic-level and bond-level local environments and a cross-substructure high-order dependency relationship in drug molecules is realized, and the accuracy and robustness of drug interaction prediction are improved. According to the method, Mol2Vec and MolT5 cross-modal molecular characterization is integrated, fusion of molecular overall semantics and substructure grammar semantic association is achieved, and the generalization ability of the model to complex molecules and unknown medicine combinations is remarkably improved. According to the method, the dynamic feature screening algorithm driven by the SHAP value of the artificial intelligence technology is adopted for biological verification, the feature redundancy problem is effectively solved, the molecular biological information analysis processing calculation efficiency and the model transparency are improved, and the traceability of the prediction process is guaranteed.

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 embodiment of the invention discloses a drug molecule discovery method and device, a medium and equipment, and the method comprises the steps: extracting an entity of an input text, recognizing the intention of the input text, and scheduling one or more processes in drug molecule discovery processes related to the entity according to an intention recognition result, so that a user only needs to give the input text, and the user experience is improved. The subsequent operation of the drug molecule discovery process can automatically schedule one or more processes in the drug molecule discovery processes related to the entity in the input text according to the intention result of the input text, so that a user is prevented from manually importing and exporting data between different processes, and the processing efficiency of drug molecule discovery is improved.

The invention provides a compound or an N-oxide, a solvate, a pharmaceutically acceptable salt or a stereoisomer thereof, and also provides a composition containing the compound and application of the compound and the composition to delivery of a therapeutic agent or a prophylactic agent. According to the invention, the types of cationic lipid compounds are enriched, and more choices are provided for effective delivery of nucleic acid drugs, gene vaccines, small molecule drugs, polypeptides or protein drugs. After the lipid nanoparticles are formed with other lipid components, mRNA or drug molecules can be effectively delivered into cells to exert biological functions.

The invention discloses a method and device for predicting the transmembrane capability of a substance molecule, terminal equipment and a storage medium, and the method comprises the steps: obtaining a target molecular substance, and determining a molecular structure diagram corresponding to the target molecular substance; according to a pre-trained prediction model, determining a permeation free energy prediction value of the target molecular substance; according to the prediction model, multilevel feature extraction is carried out on a molecular structure diagram of sample substance molecules by utilizing a graph neural network model, contribution values of atoms in the sample substance molecules to transmembrane behaviors are evaluated through a gradient weighted class activation mapping method, and permeation free energy prediction values corresponding to the sample substance molecules are output. The graph neural network is trained to obtain the prediction model, and the prediction model is adopted to predict the transmembrane capability of the target molecular substance, so that intelligent screening of the transmembrane capability of a large-scale drug molecular library can be realized, the prediction efficiency and flux are remarkably improved, and intelligent prediction of the transmembrane free energy of substance small molecules is realized.

The invention discloses a multi-target drug molecule generation model construction method and a multi-target drug design method, and relates to computer drug design and bioinformatics. Determining a corresponding two-dimensional molecular map based on the SMILES sequence; the fully-connected pharmacophore diagram and the two-dimensional molecular diagram are used as input of a GatedGCN module, each atomic node in the two-dimensional molecular diagram is connected with all pharmacophore nodes in the fully-connected pharmacophore diagram in message transmission, and information exchange between different nodes is achieved; the masked SMILES sequence serves as the input of an encoder, and the decoder generates an SMILES sequence conforming to pharmacophore characteristics in an autoregression mode; freezing the network parameters of the GatedGCN module and the encoder; and training the multi-target drug molecule generation model through the elite multi-target molecule set, and reversely optimizing and updating decoder network parameters according to an output result. According to the method, the model fully learns the multi-target molecular structure characteristics, and the multi-target drug molecule generation accuracy is improved.

The invention discloses a lipid nanoparticle, a targeted lipid nanoparticle, and a preparation method and application thereof. The lipid nanoparticle has a core-shell structure, the core is formed by PLGA nanoparticles containing drug molecules, and the shell is formed by raw materials including phospholipid, cholesterol and a modification material; wherein the PLGA nanoparticles containing the drug molecules are formed by raw materials comprising PLGA and the drug molecules; the mass ratio of the PLGA to the phospholipid is (1-5): 1; the mass ratio of the phospholipid to the cholesterol is (3-10): 1, and the mass ratio of the phospholipid to the modification material is (3-10): 1; the drug molecules are selected from one or more of insoluble drugs, water-soluble drugs, genes and proteins; and the modification material is PEG-DSPE (Polyethylene Glycol Distearoyl The hardness of the lipid nanoparticles can be regulated and controlled, and cellular uptake can be improved by regulating and controlling the hardness; the targeted lipid nanoparticles can better improve cellular uptake, promote intestinal epithelial cell absorption and improve oral curative effect.

The invention discloses a system and a method for rapidly detecting trace active ingredients of a medicine, belongs to the technical field of medicine detection, and relates to cross application of surface enhanced Raman spectroscopy and artificial intelligence. The system comprises a sample preparation module, a micro-fluidic enrichment module, a Raman spectrum acquisition module, a self-adaptive spectrum analysis module, a multi-component identification module and an intelligent report generation module, and efficient enrichment and nanogram-level detection of drug molecules are realized through an enhanced surface Raman scattering micro-fluidic chip. Intelligent identification and accurate quantification of complex spectral characteristics are realized by adopting a deep learning algorithm and a self-adaptive weight calculation method, synchronous detection of 5-10 active pharmaceutical ingredients is supported, the detection sensitivity reaches nanogram / milliliter level, the quantification error is less than 5%, the detection time of the whole process is 5-10 minutes, and the detection accuracy is high. The sensitivity, the accuracy and the efficiency of medicine quality detection are obviously improved.

The invention relates to a drug interaction prediction method and system based on multi-view comparative learning, and belongs to the technical field of natural language processing. According to the method, two channels of a drug molecular map and a biomedical knowledge map are constructed in parallel, structural and semantic features are extracted by using a pre-trained heterogeneous map neural network, and multi-view comparative learning guided by information gain is introduced for joint optimization, so that the generalization ability and robustness of the model to unknown drug pairs are enhanced. According to the method, system evaluation is carried out on the performance of the system in two types of prediction tasks (multi-type and multi-label) and three prediction scenes. Experimental results show that the method has excellent performance in all tasks and scenes. Further case analysis also verifies the effectiveness of the system in predicting the interaction type of the unseen drug pair.

The invention belongs to the technical field of biological medicines, and particularly discloses an alkynyl pyrimidine derivative as well as a preparation method and application thereof. The structure of the alkynyl pyrimidine derivative is shown as a formula I. The novel alkynyl pyrimidine derivative capable of efficiently inhibiting the LSD1 activity is provided, the preparation process is simple and easy to implement, the good effect of resisting colorectal cancer cell proliferation is shown on the animal level, and safe and effective candidate drug molecules are provided for targeted therapy of colorectal cancer.