693 results about "Chemical structure" patented technology

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 drug relocation method and a drug relocation system. The method comprises the following steps: predicting an expression profile of a drug after cell line disturbance according to a chemical structure of the drug, dose information of the drug and an undisturbed expression profile. And calculating the differential expression profile of the gene in the cell line based on the expression profiles before and after the cell line is disturbed by the drug. And for each drug, according to the differential expression profiles of the genes, calculating an average value of the differential expression profiles of the genes after the drugs disturb different cell lines, and sorting the genes based on the average value to obtain a sorting list of the differential expression profiles of the drugs. And according to the gene characteristics of the target disease and the sorting list, calculating the enrichment score of each drug on the target disease, and according to the enrichment score, evaluating the potential efficacy of the drug on the target disease. The drug relocation method provided by the invention can be used for giving disease specific gene characteristics for drug library screening.

The invention relates to the technical field of biogenic amine detection, in particular to a dicyanoethylene-based fluorescent material as well as a preparation method and application thereof. After the biogenic amine is in contact with the glass surface fluorescent probe, due to the nucleophilic characteristic of the biogenic amine, the biogenic amine can be subjected to nucleophilic substitution reaction with a probe molecule dicyanovinyl group to generate an imine chemical structure. As the electron-withdrawing capability of dicyanoethylene is stronger than that of imine, the intramolecular charge transfer effect of probe molecules is greatly reduced, so that blue shift of absorption and luminescence properties of the probe molecules is caused, and after the fluorescent probe is loaded on the surface of glass, the fluorescence intensity of the fluorescent probe is improved. The formed dicyanoethylene-based fluorescent material provides a solution for the technical problem that the existing biogenic amine detection mode is inconvenient.

A drug design method based on an autoregressive model is provided, which relates to the field of drug design technologies. The method includes: applying a sub-word tokenization algorithm to biological text processing, training protein and ligand information in data sets to obtain a protein tokenizer and a ligand tokenizer, and constructing a tokenizer of the autoregressive model; processing and transforming original data in the data sets into a text form, and encoding by the tokenizer to construct a training data set for the autoregressive model; training the autoregressive model by the training data set, so that the autoregressive model can understand SMILES representations of ligands and learn an interaction mode between proteins and ligands; generating predicted ligands by using the trained autoregressive model, and post-processing through a chemical information tool to acquire candidate ligands with specific chemical structures; and evaluating and optimizing the candidate ligands to determine target candidate molecules.

A computer-implemented method can include: receiving input of a biological target; receiving a generative model (e.g., tensorial reinforcement learning (GENTRL) model or other model) trained with reference compounds, wherein the reference compounds include: general compounds, compounds that modulate the biological target, and compounds that modulate biomolecules other than the biological target; generating structures of generated compounds with the generative model; prioritizing structures of generated compounds based on at least one criteria; processing prioritized chemical structures of the generated compounds through a Sammon mapping protocol to obtain hit structures; and providing chemical structures of the hit structures. One or more non-transitory computer readable media are provided that store instructions that in response to being executed by one or more processors, cause a computer system to perform operations, the operations comprising performing the computer methods described herein for providing chemical structure of hit structures generated by the generative model.

The invention belongs to the field of microbial natural product pesticides, and particularly discloses a polyketone compound as well as a preparation method and application thereof. The compound disclosed by the invention is prepared from a fermentation crude extract of a strain of tobacco endophytic fungus Aspergillus japonicus TE-739D, and the compound disclosed by the invention is prepared from the fermentation crude extract of the tobacco endophytic fungus Aspergillus japonicus TE-739D. The chemical structure of the compound is identified through a modern spectroscopy technology, such as a high resolution mass spectrum HRESIMS and a superconducting nuclear magnetic resonance spectrum NMR, and the compound is named as japonidiene C. The invention further discloses a preparation method of the compound. The polyketone compound japondiene C has an obvious insecticidal activity on 2-instar larvae of spodoptera frugiperda, and can be used for preventing and treating the second instar larvae of the spodoptera frugiperda.

The invention relates to a brand-new microbial natural metabolite, namely Fcl-8, which is derived from a fermentation solution of Xenorhabdius budapestensis XBD8, and is characterized in that the Fcl-8 is a new microbial natural metabolite, namely Fcl-8, which is derived from the fermentation solution of the Xenorhabdius budapestensis XBD8. The purified Fcl-8 is a snow white flocculent crystal and is very easy to dissolve in water, and the solid state of the purified Fcl-8 is as shown in Figure 1. The chemical structural formula of the Fcl-8 is as shown in a figure 2, the chemical formula is C57H102N14O12, the molecular weight is 1175.78744, and the typical secondary mass spectrum information of the Fcl-8 is as shown in a figure 3. The Fcl-8 is derived from a microbial natural metabolite, is novel in structure and high in biological activity, and has very high potential to be developed into a novel biopesticide.

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

The invention belongs to the technical field of synthetic chemistry, and provides a muscarinic acetylcholine receptor 3 fluorescent probe as well as a preparation method and application thereof. The chemical structural formula of the fluorescent probe is shown in the specification, and the fluorescent probe can be used for M3 receptor tracing in cells or organisms. The probe provided by the invention has a good fluorescent property and can be influenced by the polarity and viscosity of an external environment; and the probe has good biocompatibility, and the IC50 of the probe is 43 times of the concentration of a working solution, so that the probe can meet the requirements of cell fluorescence imaging. In the aspect of cell fluorescence imaging, the probe can realize selective fluorescence labeling of the M3 receptor in the cell, washing is not needed in the dyeing process, and the operation is simple and convenient, so that visual research on positioning of the M3 receptor in the cell is realized.

The invention discloses a radionuclide-labeled collagen hybrid peptide probe as well as a preparation method and application thereof. The chemical structural general formula of the radionuclide-labeled collagen hybrid peptide probe is (metal nuclide-chelating agent)-Linker-CHP, wherein the metal nuclide is selected from any one of Al18F, 64Cu, 67Cu, 67Ga, 68Ga, 99mTc, 89Zr, 111In, 177Lu, 186Re and 225Ac, and the metal nuclide is selected from any one of Al18F, 64Cu, 67Cu, 67Ga, 68Ga, 99mTc, 89Zr, 111In, the chelating agent is selected from any one of NODAGA, NOTA2 and DOTA; linker is a connecting joint, and CHP is a collagen hybrid peptide with a polypeptide sequence of (GfO) n or (GPO) n. The probe not only has the characteristic of radiochemical marking stability, but also has the excellent biological distribution characteristics of high uptake in target organs such as tumors and low uptake in non-target organs such as liver and kidney, and has a good clinical application prospect.

The invention relates to the technical field of medical informatics, in particular to a method for determining a drug code, which comprises the following steps of: acquiring a specification text, a chemical structure, an active component and target information, and integrating into task input; respectively extracting a molecular topological vector and a semantic topological vector based on topological coherence, injecting the molecular topological vector and the semantic topological vector into a sparse high-dimensional super-vector, and mapping the sparse high-dimensional super-vector into a continuous vector through binding transformation; generating a preliminary code by using a generative neural network combining diffusion denoising and a converter, and calculating a super-dimensional residual error; constructing a causal model in the pharmacological knowledge graph, and outputting a correction code after residual error calibration; the joint probability variance is evaluated through Monte Carlo discarding, and automatic confirmation, manual recheck or anti-fact search are realized through two-stage threshold values; and writing the combined super vector, the final code and the variance into an experience library, and optimizing the generation network and the causal model at the same time by using a hierarchical Bayesian near-end strategy. According to the invention, the extrapolation capability and interpretability of new drugs are considered, the coding accuracy is obviously improved, and the labor cost is reduced.

The invention discloses a construction method and device of a neural network model for target design drug molecules. According to the method, target pockets are characterized in three levels of atoms, residues and among the residues, so that the information of the target is more comprehensively considered; a molecular fragment obtained by a chemical structure inverse synthesis splitting algorithm is taken as a unit, the drug molecules are characterized and generated, and the generated molecules have higher drug forming potential; the model designed by the invention is composed of two neural network encoders and a gated self-attention decoder, the encoders are used for extracting features of a target and ligand molecules, the decoder realizes association of the two features and generates a molecular fragment probability, and the model can effectively design drug molecules for a specific target. Molecules designed by the model constructed by the invention have an average synthesis accessibility score of 0.769 and an average drug-likeness score of 0.607, which are higher than those of a baseline model PMDM.

Disclosed are systems and methods for identifying a target molecule by receiving a molecule dataset from a library of molecules comprising chemical-structural representations of candidate molecules for a material application; determining one or more target molecular property values derived from a set of predictive molecular descriptor values determined from a candidate molecule of the molecular dataset applied to a trained machine learning model, wherein the trained ML model is configured to output the set of predictive molecular descriptor values for a given molecule data input, and wherein the trained ML model was trained for a set of training data and the set of molecular descriptors generated from a molecular descriptor modeling application; and outputting, via a report or to a data store, the one or more target molecular property values for each of the molecular dataset.

The invention discloses a preparation method and application of a metal covalent organic framework material, and the prepared metal covalent organic framework material can be used for adsorption separation of elemental iodine pollutants and shows excellent iodine adsorption capacity and rate. According to the preparation method, 1H-pyrazole-4-formaldehyde and copper nitrate trihydrate are subjected to a solvothermal reaction to prepare a copper ion ring trinuclear metal complex with an aldehyde group, and the copper ion ring trinuclear metal complex and 1, 3, 5-tri (4-aminophenyl) benzene are further subjected to a Schiff base reaction to prepare the metal covalent organic framework material containing copper ions. According to the metal covalent organic framework material disclosed by the invention, copper ion active adsorption sites in a chemical structure are also retained while a developed microporous structure is maintained, and the interaction strength of pore channels to iodine is remarkably enhanced, so that the iodine adsorption rate and capacity are remarkably improved. The preparation method is simple, convenient, universal and high in controllability, and large-scale production and manufacturing of the metal covalent organic framework adsorption material can be achieved.

The invention discloses an ultra-sensitive viscosity fluorescent probe based on a carbon-nitrogen double bond. The chemical structural formula of the probe is as follows: # imgabs0 #. The fluorescent probe is synthesized through Schiff base reaction, and is simple to operate and easy to synthesize. The probe can freely rotate at carbon-carbon double bonds and carbon-nitrogen double bonds, and when the probe is in high viscosity, free rotation of molecules is limited, so that twisted intramolecular charge transfer is inhibited, fluorescence of the probe is remarkably enhanced, and 650 nm near-infrared fluorescence is emitted under the excitation wavelength of 600 nm. In addition, the probe has ultrahigh sensitivity, excellent selectivity, excellent light stability and pH tolerance, and has huge application prospects in the technical fields of analytical chemistry, life science, biomedical treatment and the like.

The invention discloses a yak milk-derived antioxidant peptide and a screening method and application thereof, and belongs to the technical field of bioactive peptides, the yak milk-derived antioxidant peptide is DGGY, the amino acid sequence of the yak milk-derived antioxidant peptide is shown as SEQ ID NO.1, the molecular formula of the yak milk-derived antioxidant peptide is C17H22N4O8, the chemical structural formula of the yak milk-derived antioxidant peptide is # imgabs0, and the invention further discloses the screening method and application of the yak milk-derived antioxidant peptide. The DGGY with the amino acid sequence as shown in SEQ ID NO.1 is proposed for the first time, has good antioxidant activity, is high in hydroxyl radical and ABTS free radical scavenging activity, is derived from yak milk, belongs to a natural antioxidant, is good in safety, can be used for preparing antioxidant products, and has wide application prospects.

The invention discloses an enzyme catalysis constant kcat prediction method and system based on multi-modal feature fusion, and the method comprises the steps: S1, obtaining an enzyme-substrate pair which comprises an amino acid sequence of a target enzyme and a chemical structure expression of a corresponding substrate; s2, inputting the amino acid sequence into a pre-trained protein language model, and carrying out average pooling treatment on hidden layer state vectors of amino acid residues in the sequence by the protein language model to obtain enzyme feature vectors for representing functional features of enzyme; s3, on the basis of the chemical structure representation of the substrate, coding the chemical structure through the set radius parameter and bit length parameter by adopting an extended connectivity chemical fingerprint algorithm to obtain a substrate feature vector for representing the structural features of the substrate; s4, splicing the enzyme feature vector and the substrate feature vector to obtain a fused feature vector; and S5, inputting the fusion feature vector into a random forest regression model, and outputting a predicted value of the catalytic constant kcat of the enzyme-substrate pair.

System, methods, apparatuses, and computer program products are disclosed for searching a chemical structure database based on centroids. A query is executed against a chemical structure database by first vectorizing a molecular representation associated with the query into a query feature vector. The query feature vector is compared to centroid feature vectors to determine a centroid feature vector associated with a chemical structure representation that satisfies a similarity condition with the query feature vector. A first subset of the chemical structure database that includes chemical structures of the chemical structure database associated with the determined centroid feature vector is searched to determine whether the molecular representation associated with the query is present in the first subset of the chemical structure database.

The invention discloses an organic silicon cooling liquid and a preparation method thereof, the chemical structural formula of the organic silicon cooling liquid is shown in the following formula (1), (2), (3) or (4), and the organic silicon cooling liquid is prepared by mixing micro-molecular siloxane containing methoxy or ethyoxyl and a single silicon hydrogen reagent containing long-chain alkyl in a solvent and carrying out condensation reaction under the action of a catalyst. Experimental results show that compared with existing organic silicon cooling liquid, the organic silicon cooling liquid has excellent thermal stability, the overall performance of the organic silicon cooling liquid cannot be affected by low polymers or high polymers generated in the reaction process, and the organic silicon cooling liquid has good repeatability in production; the organic silicon cooling liquid has an exact molecular structure, is a single compound, and can be applied to liquid cooling in the fields of data centers and the like, so that the application range of organic silicon molecules with exact structures is expanded; in addition, the preparation method of the organic silicon cooling liquid is simple in post-treatment and relatively low in energy consumption, so that the organic silicon cooling liquid has a wide application prospect.

The invention provides a task processing method based on intermolecular interaction, a training method based on intermolecular interaction and a device thereof. Based on a connecting edge formed between a first node in the first molecular graph and a second node in the second molecular graph, respectively performing embedding feature representation on the first node and the second node to obtain a first node embedding feature and a second node embedding feature; identifying a first action node feature and a second action node feature; based on the first action node feature and the second action node feature, determining target substructures in chemical structures indicated by the first molecular graph and the second molecular graph respectively; the task matched with the first molecule and the second molecule is a regression task, and performing regression prediction on the target substructures corresponding to the first molecule graph and the second molecule graph to obtain a processing result; the task matched with the first molecule and the second molecule is a classification task, classification is carried out based on the target substructures corresponding to the first molecule graph and the second molecule graph, and a classification result is obtained.

The invention relates to nitrogen-containing spirofulvene as well as a preparation method and application thereof, the chemical structural formula of the nitrogen-containing spirofulvene is # imgabs0 #, R is one of C1-C10 alkyl, alkenyl and aryl, n is equal to 0 or 1, and m is equal to 1 or 2. Compared with the prior art, the preparation method disclosed by the invention has the advantages of simple process, simplicity in operation, simplicity and convenience in separation, high reaction rate, adjustable spiro structure and the like.

The invention relates to a method for measuring thermodynamic parameters of a corrosion product under zinc injection, which comprises the following steps: detecting the characteristic peak position of the corrosion product through Raman spectrum, comparing with a standard Raman spectrum database to obtain the chemical structure of the corrosion product, calibrating the peak intensity of a standard sample, and obtaining the concentration of the corrosion product according to the relationship between the peak intensity and the concentration of the standard sample. The conductivity of the coolant containing the corrosion product is detected through alternating current conductivity, and the ionization constant of the corrosion product is obtained in combination with concentration information provided by the Raman spectrum. According to the invention, the thermodynamic parameters of the corrosion product under the chemical condition of zinc injection water can be measured in situ, including the chemical structure, concentration and equilibrium constant of the corrosion product, and the problem of lack of the thermodynamic parameters of the corrosion product under the current chemical condition of zinc injection water is solved; and a foundation is laid for accurate prediction of the corrosion product deposition law and model development under the zinc injection water chemical condition.

The invention discloses a fluorescent probe aiming at D-glucosamine as well as a preparation method and a detection method thereof. The specific chemical structural formula of the fluorescent probe is as follows: # imgabs0 # fluorescent probe has the advantages of simplicity in preparation, high detection sensitivity and good selectivity.

The invention belongs to the technical field of industrial silane preparation, and provides a natural silane rotor and a preparation method and application thereof.The preparation method comprises the steps that a pyridoxal phosphate solution and a 4-amino ethyl cinnamate solution are mixed and then react to obtain pyridoxal phosphate cinnamyl ester; and mixing the pyridoxal phosphate cinnamyl ester solution and the amino silane solution, and reacting to obtain the natural silane rotor. The silane rotor has a good conjugated structure, is stable in chemical structure, can be stored for a long time, is good in light stability, and is not liable to cause a photobleaching phenomenon. Besides, conjugated single and double bonds capable of freely rotating exist in the molecular structure, a typical flexible intramolecular rotation characteristic is presented, the conjugated structure can freely rotate in a dilute solution, a surface sightseeing signal is weak, the free rotation phenomenon is limited in a thick solution, the surface sightseeing signal is strong, and the surface sightseeing signal is strong. Therefore, the device can be used as a molecular tool for monitoring the change of the thin consistency of the silane, and can provide real-time, lossless and visual detection support for the development of a silane modulation process.

The invention belongs to the technical field of analysis and detection of liquid nutritional agents, and provides a coniferous and cypress hydroformylation salicyl ester molecular device and a preparation method and application thereof. The method comprises the following steps: mixing a coniferyl aldehyde solution, a dehydrating agent dispersion liquid and a salicyl ester derivative solution, and carrying out a dehydration condensation reaction on the mixed solution to obtain the coniferyl hydroformylated salicyl ester molecular device. The coniferous hydroformylated salicylate molecular device has the advantages of high viscosity sensitivity coefficient, stable chemical structure, strong visualization effect, release of stable optical signals in a wide pH range, insensitivity to the polarity of the solvent, and good solvent tolerance; the device can be used as a molecular tool for in-situ measurement of viscosity in a nutritional agent microenvironment, detection can be realized without depending on shearing and relative movement in the measurement process, consumption of a large amount of samples can be avoided, the whole process is rapid, efficient and convenient, and viscosity change of a liquid nutritional agent containing a large amount of pseudoplastic fluid can be effectively measured.

A compound having a ligand LA of Formula I,is disclosed. In Formula I, ring B is a 5-membered or 6-membered ring; K is a direct bond, O, or S; X is O, S, Se, NR, CRR′, SiRR′, or GeRR′; R1 and R2 are each independently selected from a variety of substituents; each R, R′, RA, and RB is independently hydrogen or a substituent; LA is coordinated to a metal M selected from Os, Ir, Pd, Pt, Cu, Ag, or Au by the dashed lines; any two adjacent of R1, R2, R, R′, RA, or RB can be joined or fused to form a ring, with the proviso that when ring B is a 6-membered ring, two RB substituents are not joined to form a fused 6-membered aromatic ring. Organic light emitting devices, consumer products, formulations, and chemical structures containing the compounds are also disclosed.

The invention relates to the technical field of computers, in particular to a database construction method and device, a computer program product and electronic equipment. The database construction method comprises the steps that an initial database is constructed according to collected data corresponding to a target material, the initial database comprises reference attribute information and a chemical structure corresponding to the reference attribute information, and the reference attribute information is associated attributes of the target material; splitting the chemical structure in the initial database through a preset splitting strategy to obtain a plurality of sub-structures, and performing correlation analysis according to the reference attribute information corresponding to the chemical structure and the plurality of sub-structures to obtain a corresponding relationship between the sub-structures and the material attributes; and according to the target material and the corresponding relationship, combining the substructures to generate a target molecule and determine the molecular attribute of the target molecule, so as to construct a target database according to the target molecule and the molecular attribute. The construction efficiency and accuracy of the material database can be improved.

The invention relates to the technical field of structural analysis and learning of pharmaceutical activity and pharmaceutical molecules, in particular to a prediction method of pharmaceutical activity. According to the technical scheme, the method comprises the steps of extracting and preprocessing data features, conducting joint modeling on drug molecular features through a Gaussian correlation embedding mechanism to form a prediction model so as to process the complex relation between different modals and heterogeneous data features, and obtaining a drug function activity prediction network through the prediction model. The method can effectively capture and model multi-dimensional dependency in drug molecule features, and can map a multi-dimensional nonlinear dependency structure to a Gaussian distribution space by introducing a Gaussian correlation function, so as to generate uniform and consistent feature embedding representation among the multi-dimensional features. According to the method, heterogeneity data such as chemical structures, biological activity and drug interaction of compounds can be effectively integrated, and the deep dependency relationship of the heterogeneity data is captured, so that the precision and robustness of drug characteristic prediction and candidate drug screening are remarkably improved.

The invention discloses a measurement and evaluation method and system for double analysis of the aging state of a cable, and belongs to the technical field of nondestructive testing of the insulation state of electrical equipment, and the method comprises the steps: obtaining a broadband dielectric spectrum of a corresponding aging sample in any aging period, and constructing low-frequency dielectric loss data related to a plurality of aging samples; obtaining group data of a standard measurement sample corresponding to any aging sample, and constructing oxidation data about a plurality of standard measurement samples; acquiring a dielectric spectrum space charge distribution diagram based on the low-frequency dielectric loss data, and acquiring an oxidation product concentration thermodynamic diagram based on the oxidation data; obtaining an aging grading result based on the low-frequency dielectric loss data and the oxidation data; and outputting a three-dimensional aging measurement evaluation report based on the dielectric spectrum space charge distribution diagram, the oxidation product concentration thermodynamic diagram and the aging grading result. According to the measurement and evaluation method and system for double analysis of the aging state of the cable, accurate quantification of the aging degree is achieved by synchronously analyzing the dielectric response and the chemical structure change.

To provide a method enabling creation of a polymer material model having a crosslinked structure close to reality.SOLUTION: A method for creating a polymer material model is provided. The creation method includes: a first step S1 of inputting, to a computer, a pre-crosslinking coarse grained molecular model in which a structural unit of a molecular chain before crosslinking is substituted with coarse grained particles; a second step S2 of analyzing the molecular chain after crosslinking; a third step S3 of inputting, to the computer, a chemical structure of the structural unit after crosslinking on the basis of a result of the analysis; a fourth step S4 of inputting, to the computer, a post-crosslinking structural unit model in which the chemical structure of the structural unit after crosslinking is modeled as an entire atomic model or a united atom model; a fifth step S5 of the computer calculating structural relaxation based on a molecular dynamics method for the pre-crosslinking coarse grained molecular model; and a sixth step S6 of the computer allocating a post-crosslinking structural unit model to the coarse grained particles of the pre-crosslinking coarse grained molecular model after the structural relaxation, to create a post-crosslinking molecular model in which the molecular chain after crosslinking is modeled as the entire atomic model or the united atom model.SELECTED DRAWING: Figure 3