545 results about "Drug molecule" patented technology

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 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 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 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 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.

The invention discloses a biological patch material with dry-state adhesion and wet-state easy stripping characteristics and application thereof, and belongs to the field of biological medicine. The biological patch material is prepared according to the following steps: by taking water-soluble protein as a template and copper ions in water-soluble copper salt as a metal coordination center, carrying out self-assembly on the template and drug molecules with phenolic hydroxyl structures to form nanoflowers; mixing the acrylamide, the sodium alginate, the water-soluble calcium salt and the nano-flowers to obtain the water-soluble calcium-based water-soluble nano-flower fertilizer. The biological patch material is adhered to the skin through electrostatic interaction, covalent bonds, physical interpenetration and topological tissues. The patch has skin adhesion in a dry state, so that the patch is convenient to fix; after the patch is in contact with burnt skin, the patch absorbs water and swells, the adhesive force is obviously reduced, and painless, easy and non-invasive stripping is realized. In addition, the patch has immunoregulation performance, can prevent stimulation and anaphylaxis caused by long-term contact with the skin, and promotes burn repair.

The invention belongs to the technical field of biological medicines, and particularly discloses a quinazoline derivative as well as a preparation method and application thereof. The structure of the quinazoline derivative is shown as a formula I. The invention provides a novel quinazoline derivative capable of efficiently inhibiting LSD1 activity, the preparation process is simple and easy to implement, a 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.

The invention discloses a generative adversarial network optimization method for small molecule drug virtual screening, and belongs to the field of computer-aided drug design. The method comprises the following steps: constructing a small molecule drug data set; building a GAN basic model comprising a generator and a discriminator; performing multi-objective optimization training (optimizing chemical effectiveness, combining affinity and structural diversity) on the model through a joint loss function; generating candidate molecules by using the optimized model, and screening through a threshold value; and carrying out molecular docking verification on the screening result and outputting a final result. The quality of generated molecules is improved through multi-objective optimization, the drug research and development cycle is shortened, and the method is suitable for efficiently screening potential drug molecules.

The present application belongs to the technical field of the synthesis of chiral boron compounds, and in particular relates to an MOF-Cu(II) / CMC composite material, and a preparation method therefor and the use thereof. The preparation method for an MOF-Cu(II) / CMC composite material of the present application comprises: dropwise adding an aqueous solution of sodium carboxymethyl cellulose to an aqueous solution of a bivalent copper salt, performing solid-liquid separation, mixing obtained composite microspheres with an organic solution of trimesic acid, heating the resulting mixture, and subjecting same to solid-liquid separation, so as to obtain an MOF-Cu(II) / CMC composite material. The MOF-Cu(II) / CMC composite material is used in an asymmetric chiral boron addition reaction. The reaction is performed at room temperature, involves mild conditions, is applicable to a wide range of substrates and has a high yield and enantioselectivity. Moreover, the method can achieve the synthesis of a chiral organic boride in a solvent having simple components, providing an efficient new method for the preparation of drug molecules and active intermediates, and also broadening the range of substrates used for the preparation of chiral organic borides.

The invention belongs to the technical field of biological medicines, and particularly discloses a pyrimidine derivative as well as a preparation method and application thereof. The structure of the pyrimidine derivative is as shown in formula I in the specification. The novel 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.

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 preparation method of olmacycline tosylate for injection, belongs to the technical field of pharmaceutical preparations, and provides a special preparation and a preparation method of the olmacycline tosylate for injection in order to solve the problems that the olmacycline tosylate for injection is prone to epimerization and oxidative degradation and the room temperature stabilization time is short after redissolution. According to the preparation, omagazines tosylate is used as a main component and matched with glycerol, vitamin C, thioglycerol, sodium acetate and water for injection, and the pH is adjusted to a proper range through hydrochloric acid or sodium hydroxide. According to the method, by means of the synergistic effect of glycerin and vitamin C, glycerin fixes medicine molecules and protects vitamin C, vitamin C removes oxidation inducements, and thioglycerin is matched to assist in inhibiting degradation, so that the medicine stability is greatly improved, the redissolved liquid medicine can be stabilized at 25 DEG C for 72 hours, the total impurities are controlled at a low level, and the clinical actual requirements are met.

Embodiments of this disclosure provide a method and apparatus for drug design, a device, a medium, and a program product. The method for drug design includes: obtaining protein data representing a three-dimensional structure of a protein and initial molecule data representing an initial molecule to be bound to the three-dimensional structure of the protein. The method further includes: determining first molecular fragment data representing a first molecular fragment in the initial molecule based on the protein data and the initial molecule data. Generating target molecule data representing a target molecule based on the first molecular fragment data and the initial molecule data. A molecular fragment is automatically determined in the initial molecule, and the initial molecule is optimized based on the determined molecular fragment, such that fragment-based artificial intelligence optimization of a drug molecule can be implemented in a targeted manner, thereby reducing time and labor costs of drug discovery.

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.

This invention discloses a highly efficient asymmetric synthesis method for aryl propionic acid drug molecules. The method uses racemic aldehydes as substrates and thiamine pyrophosphate-dependent enzymes as biocatalysts. Electrolysis is performed in a diaphragm-free electrolytic cell under conditions of an external redox dielectric and constant current, in a buffer salt solution and nitrogen atmosphere. This method achieves dynamic kinetic oxidation of aldehydes via bioelectrocatalysis, enabling the high-efficiency and high enantioselectivity synthesis of aryl propionic acid drug molecules. The method eliminates the need for redox agents and additives, is simple to operate, operates under mild reaction conditions, and is green and sustainable. This invention applies bioelectrocatalysis to asymmetric synthesis, providing a green and efficient new enzymatic synthetic route for the asymmetric synthesis of aryl propionic acid drug molecules, which has significant application prospects in the pharmaceutical industry.

The application discloses a preparation method of enciferstat bulk drug and belongs to the field of drug synthesis. 9,10-dimethoxy-2-(2,4,6-trimethylphenylimino)-3,4,6,7-tetrahydro-2H-pyrimido[6,1-a]isoquinolin-4-one (SM) is used as a raw material, and the raw material is reacted with cyclohexylamine under alkaline conditions, and the reaction is washed with water, dried, and concentrated to obtain an intermediate I; the intermediate I is reacted with urea under pressurized heating conditions to prepare an enciferstat drug molecule, the two-step yield is more than 67%, and the purity is more than 99%, the method has a simple operation process, the product is easy to purify, the yield is high, the product purity is high, and a new method for preparing enciferstat bulk drug is provided.

Methods and systems are provided for training a machine learning algorithm using at least one hardware processor to identify one or more predictive drug molecule features and / or predictive genetic biomarkers for cancer treatment. In some embodiments, the method uses at least one cancer drug discovery data set to train at least one learning algorithm in a predictive model, and uses a xenograft mouse model to validate the predictive model, and feeds back a biological response to the predictive model to further train the learning algorithm.

The application discloses application of a naphthalene nitrile derivative in preparation of an antitumor pharmaceutical composition, in particular, application of a 2-(imino)-naphthalene nitrile derivative in preparation of an antitumor pharmaceutical composition. The 2-(imino)-naphthalene nitrile derivative provided by the application has a significant inhibiting effect on colon cancer, liver cancer, lung cancer, prostate cancer, cervical cancer, neuroglioma and breast cancer, exhibits excellent antitumor characteristics, can be used as a leading drug molecule for resisting tumors, and has a good development and application prospect in development of antitumor drugs.

A method for improving drug solubility and a drug composite material having improved solubility. The method comprises preparing a silica colloidal suspension from silica nanoparticles having a diameter between 2 nm and 100 nm; rapidly evaporating a solvent by means of controlling the temperature and pressure, so as to obtain a porous surface material with ultra-high density silanol groups retained on the surface; and loading drug molecules. By controlling the size of nanoparticles and the density of silanol groups, drug molecules can be effectively adsorbed under anhydrous conditions, and rapidly desorbed and released in an aqueous environment.

The invention belongs to the technical field of crystal form medicine molecules, and particularly provides a baricitinib-salicylic acid eutectic crystal and baricitinib-5-sulfosalicylate, and the stability and solubility of the baricitinib-salicylic acid eutectic crystal and the baricitinib-5-sulfosalicylate are investigated. It is found that the stability or solubility of the compound is improved to a certain extent. And the two crystal forms are simple in preparation process. Good industrialization prospects are realized.

The application discloses a kind of using cobalt catalyst by unreported double migration coupling strategy, realizes migration asymmetric nitrogen hetero Nozaki-Hiyama-Kishi (aza-NHK) reaction, and the selective preparation α-chiral (E)-allyl amine starting from simple and easy to obtain ortho halogen phenyl ethylene or corresponding E / Z-alkyl bromide or its mixture belongs to the field of organic chemistry and medicinal chemistry.The application under the action of metal cobalt source, ligand, organic additive, inorganic additive etc., makes cyclic sulfonamide and ortho alkenyl substituted aryl halide or alkenyl halide react in organic solvent, to obtain a series of polysubstituted olefins with excellent regioselectivity, stereoselectivity and good yield, which can be used as active drug molecules and important synthetic intermediates.The application uses cheap transition metal cobalt as catalyst, and uses ortho alkenyl substituted aryl iodine and cyclic sulfonamide as raw material, and the reaction condition is warm, simple operation, and good functional group compatibility.

The application relates to the technical field of drug molecule design, in particular to a drug molecule directional generation method and system.The method comprises the following steps: firstly, a molecular representation base model is constructed, chemical space information of a drug molecule structure is transformed, updated, pooled and integrated, and a one-dimensional molecular code vector capable of reflecting molecular structure and pharmacodynamic attribute information is generated; a molecular structure generation model and a molecular attribute prediction model are constructed, and the one-dimensional molecular code vector is segmented for training, the former is subjected to dimension expansion, feature conversion and decoding to restore the vector into a drug molecule structural formula, and the latter predicts the molecular attribute; according to the molecular attribute prediction result, a new drug molecule structure with a target pharmacodynamic attribute can be directionally optimized and generated. Through accurate modeling of the distribution law of the drug chemical space, the drug research and development efficiency and success rate are effectively improved, and strong technical support is provided for the field.

The invention belongs to the technical field of crystal form drug molecules, and particularly relates to a baricitinib-trimesic acid eutectic crystal and a preparation method thereof. The baricitinib-trimesic acid eutectic crystal provided by the invention is good in stability and high in solubility, and has improved pharmacokinetics, so that subsequent development requirements of drugs are met; the preparation method is simple to operate, easy to control the crystallization process, good in reproducibility and suitable for industrial production.

The invention relates to the technical field of molecular generation, and discloses a reverse design method using a molecular spectrum as a medium to be applied to virus protein drug molecules, which comprises the following steps: firstly, constructing a data set containing a molecular SMILES identifier and a corresponding molecular spectrum, and carrying out drug-likeness and false positive preliminary screening on compounds by adopting Lipinski five rules and a PAINS mode; then training a conditional diffusion model, and generating a target molecular spectrum by taking specific drug properties as conditions; the spectrum is mapped into a molecular SMILES structure through a Transform decoder, so that accurate conversion from the spectrum to the molecule is realized; finally, through conformation optimization and stability verification, candidate drug molecules meeting specific treatment requirements are output. The method effectively avoids the invalid structure problem caused by cross-dimension generation, significantly improves the synthesizability and patent medicine potential of generated molecules, and is suitable for efficient molecular design of antiviral drugs, inhibitors and the like.

This invention discloses a molecular event visualization method and electronic device, proposing the concept of a molecular film. The method acquires the topological structure file of the drug molecule-target protein and the original trajectory file generated after molecular dynamics simulation of the drug molecule-target protein; based on the original trajectory file and topological structure file, it obtains the keyframe sequence of the molecular dynamics simulation of the drug molecule-target protein; it monitors the keyframe sequence to identify key event sequences of the drug molecule-target protein, and annotates the key event sequences to obtain annotated key event sequences; and it renders the annotated key event sequences to visualize and output the generated rendering data as molecular events, significantly improving the efficiency and depth of understanding molecular mechanisms of action in drug discovery and optimization.

The application discloses a dehydrocostus lactone derivative, a preparation method and medical application thereof, and belongs to the technical field of chemical synthesis. The dehydrocostus lactone derivative is shown as a general formula I. The compound has certain inhibiting effect on HBsAg and HBeAg, can be used for preparing anti-HBV drugs, and provides more efficient and safe candidate drug molecules for clinical anti-HBV treatment.

The invention discloses a rapid and accurate high-throughput drug screening system based on deep learning, and relates to the field, and the method comprises the steps: obtaining molecular structure data containing two-dimensional structural features and three-dimensional conformation features in a to-be-screened drug molecular library, and converting the molecular structure data into a numerical feature matrix; and constructing a feature association network based on the feature matrix, identifying influence nodes through node connectivity, and determining a dynamic weight coefficient. And performing weighted reconstruction on the feature matrix by using the dynamic weight coefficient to generate optimized feature data, and calculating the binding strength and spatial complementarity of the candidate drug molecules and the target sites to obtain molecular activity distribution data. Constructing a map according to the activity distribution data, determining an initial screening interval, and optimizing and adjusting the screening interval by calculating a structural skeleton difference value between candidate molecules, so as to screen out target drug molecules. According to the method, large-scale drug molecule screening work can be rapidly and accurately completed, and the drug research and development efficiency is improved.

The invention provides a Chinese and western medicine taste prediction method based on deep learning and a chemical language, and the method comprises the steps: S1, initializing a chemical language pre-training model ChemBERTa based on a RoBERTa architecture, and enabling the model to serve as a semantic encoder for simplifying a molecular linear input specification expression; s2, constructing a standardized medicine taste data set covering the taste information of the traditional Chinese medicine and the western medicine; s3, on the basis of the model framework in the S1, building a basic pre-training model for medicine taste prediction; s4, adopting a transfer learning strategy, and utilizing the medicine taste data set constructed in the S2 to carry out retraining and domain adaptation on the basic pre-training model in the S3 to obtain an intelligent medicine taste prediction model; and S5, predicting the drug taste of the drug molecules by using the model in S4. According to the method, deep semantic modeling of the medicine flavor is achieved, the method is a universal, explainable and extensible medicine flavor intelligent identification technology for Chinese and western medicines, and analysis of compatibility rules of the Chinese and western medicines and clinical precise medication are facilitated.