1028 results about "Chemical structure" patented technology

The invention discloses a soil organic pollutant identification method and system based on combination of AI and high-throughput screening, and relates to the technical field of pollutant screening, and the method comprises the following steps: extracting a substance peak from high-resolution mass spectrum data of a soil sample; the method comprises the following steps: establishing an organic pollutant mass spectrum database, extracting spectrogram characteristics and structural characteristics of compounds in the organic pollutant mass spectrum database, establishing a molecular fingerprint prediction model, and establishing a mapping relation from a spectrogram to a structure; constructing a spectrogram vector prediction molecular fingerprint according to the extracted substance peaks, and retrieving by using an organic pollutant mass spectrum database to obtain candidate chemical structures; scoring the candidate chemical structures by predicting the molecular fingerprints, selecting the chemical structures meeting a preset standard, constructing a recognition basis according to the selected chemical structures, and obtaining an organic pollutant recognition result of the soil sample. According to the method, non-targeted intelligent analysis is carried out through cross fusion of artificial intelligence and mass spectrometry, and rapid and accurate identification of soil organic pollutants is realized.

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 provides a nano photosensitizer modified by outer membrane vesicles of genetically engineered bacteria as well as a preparation method and application of the nano photosensitizer. The nano photosensitizer is a nano material with the particle size of 80-120 nm, and the nano photosensitizer modified by the genetically engineered bacterium outer membrane vesicle comprises dipalmitic acid phosphatidylcholine (DPPC), distearoyl phosphatidylcholine (DSPC), dioleoyl lecithin (DOPC), cholesterol, a small molecule dye and CD47nb-OMV membrane protein. The chemical structure of the small molecule dye is shown as a formula I or a formula II. The nano photosensitizer OBDlp modified by outer membrane vesicles of genetically engineered bacteria serves as a novel nano photosensitizer, tumor targeting PTT is mediated through NIR II FL / PA imaging, tumor immunogenicity is improved, antigen presenting cells are activated, and infiltration and functionalization of CD8 + T cells are caused. And OBDlp and laser are combined to induce release of a large amount of DAMPs and trigger ICD to promote maturation of cDC1s. Meanwhile, a CD47 nano antibody carried by the nano photosensitizer induces polarization of M1 phenotype macrophages by blocking a CD47-SIRP alpha signal transduction axis, and an immunosuppression tumor microenvironment is reversed.

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.

Methods for identifying a chemical structure of a compound based on mass spectrometry (MS) data using one or more computing devices are disclosed. The methods include receiving mass spectrometry (MS) data that includes a plurality of mass-to-charge values associated with fragments obtained from mass spectrometry performed on the compound, inputting the plurality of mass-to-charge values into a tokenizer trained to generate a plurality of tokens based on the plurality of mass-to-charge values, and determining one or more chemical structures of the compound based at least in part on the plurality of tokens.

The present invention provides a zirconium-based metal-organic framework material and a preparation method therefor and the use thereof, and an adsorption separation device and method. The zirconium-based metal-organic framework material has a chemical structural formula of [C18H6O16Zr3]n, and comprises zirconium and an organic ligand forming a coordination bond with zirconium, wherein the organic ligand is diphenylethyne-3,3′,5,5′-tetracarboxylic acid. The molecular structure of the zirconium-based metal-organic framework material of the present invention is a three-position network structure having a one-dimensional channel; and in the present invention, the size of the one-dimensional channel is accurately controlled by changing the aspect ratio of the organic ligand, such that the zirconium-based metal-organic framework material efficiently separates a hexane isomer by means of a kinetic effect.

The invention discloses a molecular probe for in-vivo copper ion detection and a preparation method and application thereof, the chemical structural formula of the molecular probe is as follows: # imgabs0 #, the molecular probe provided by the invention can specifically respond to copper ions in a tumor subacid environment so as to cause change of an absorption spectrum, and a photoacoustic signal is started so as to detect the copper ions in the tumor subacid environment. Normal tissues and tumor tissues can be accurately distinguished, and accurate detection of the copper ions in the living tumor is realized.

A series of novel donor-acceptor type TADF luminogens have been designed with the aim of developing stable OLEDs with enhanced operational stability and improved color purity. These materials can be utilized in full color displays and lighting applications.

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.

The invention relates to the field of ceramic materials, in particular to a ceramic material and a preparation method and application thereof in piezoelectrics, the chemical structural general formula of the ceramic material is as follows: (1-alpha-beta) (Ba1-xMx) [Ti1-y (W1 / 2Cu1 / 2) y] O3-alpha Bi (Sb1 / 3Ln2 / 3) O3-beta Sr2Fe1-zAlzMoO6, M is an alkaline earth metal element; ln is a lanthanide rare earth element; x is equal to 0.12, y is equal to 0.04, z is equal to 0.2, alpha is larger than or equal to 0.01 and smaller than or equal to 0.1, beta is larger than 0 and smaller than or equal to 0.05, and the ceramic material is good in compactness and excellent in piezoelectric performance and can meet the application requirements of piezoelectric ceramic sensors, piezoelectric ceramic transducers and other devices.

In the present invention, information relating to a plurality of chemical substances and products to be reacted, which is acquired, is similar to information relating to a plurality of chemical substances and products, which is set for reaction conditions. A plurality of reaction conditions obtained when a plurality of chemical substances to be reacted are reacted are set from reaction items set for the reaction conditions. Furthermore, an estimation model is used which is obtained by performing machine learning using, as training data, chemical structure information and physical property information of a plurality of chemical substances that have been reacted in the past, reaction conditions in the reaction, chemical structure information and physical property information of a product that has been generated, and a yield when the reaction is performed under the reaction conditions. A yield is estimated for each of a plurality of reaction conditions, and a reaction condition for which the yield satisfies a predetermined condition among the estimated yields is displayed (output).

A mechanism is provided in a data processing system comprising at least one processor and a memory comprising instructions which, when executed by the at least one processor, causes the at least one processor to implement a real-time prediction engine for real-time predication of chemical properties through combining calculated, structured, and unstructured data at large scale. Offline components executing within the real-time prediction engine store a computational representation for each of a plurality of chemical structures in a unified storage. Each computational representation maps a respective chemical structure to a vector of calculated chemical structure features and properties, unstructured chemical features and properties, and structured chemical features and properties. The offline components train a computational real-time predictive model based on the computational representations. A user interface executing within the real-time prediction engine receives a request specifying one or more chemical compounds. An analytics jobs manager executing within the real-time prediction engine predicts one or more properties of the one or more chemical compounds using the computational real-time predictive model. The analytics jobs manager outputs the one or more properties of the one or more chemical compounds to the user interface.

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 relates to the technical field of medical materials, and discloses a photosensitive material with a vascular repair function, a preparation method of the photosensitive material and a balloon dilatation catheter. A chemical structural formula of the photosensitive material comprises a first functional group and a second functional group, the first functional group has a function of triggering amino acid in a blood vessel wall to be crosslinked under light excitation, and the second functional group has a function of inhibiting cell proliferation. The photosensitive material provided by the invention has good fat solubility, can accurately anchor a diseased region, exerts a vascular repair function, also can exert an anti-cell proliferation effect together, and further reduces the risk of intravascular restenosis.

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.

The present disclosure relates to the field of mass spectrometry, and particularly provides a method for determining a chemical structure of a lipid and an ion mobility-tandem mass spectrometer. The method for determining a chemical structure of a lipid includes: an ionization step of ionizing a sample to obtain sample ions; an ion mobility-based separation step of separating target lipid ions from the sample ions based on ion mobility; a first dissociation step of dissociating the target lipid ions with dissociation energy adjusted to break a first chemical bond of the target lipid ions; a mass-based selection step of selecting the target lipid ions, whose first chemical bond is broken, based on a mass number to obtain fragment ions; a second dissociation step of dissociating the fragment ions to at least break a second chemical bond of the fragment ions which has bond energy higher than the first chemical bond, to obtain diagnostic ions; and a mass analysis step of performing a mass analysis on the diagnostic ions.

The invention relates to computer implemented methods for the prediction of the smell of a molecule, comprising providing a first database comprising the structure, preferably the three- dimensional (3D) and / or chemical structure, of one or more olfactory receptor (OR), training a machine learning model or AI on the data comprised within the first database, providing a second database comprising (i) the (3D) structure of one or more odorant molecule and (ii) a respective textual description of the odor sensation induced by said one or more odor molecule in a human subject, training a machine learning (ML) model or artificial intelligence (AI) on the data comprised within the second database, fitting the (3D) structure of the one or more odor molecule to the (3D) structure of the one or more OR, thereby determining an affinity score for each fitted combination of the one or more odor molecule with a respective olfactory receptor, wherein the fitting comprises the use and training of a ML model or AI, wherein the affinity score is indicative for the degree of fit of a respective odor molecule to the OR, creating a comprehensive third database or third data embedding structure / layer comprising the data of the first database, the second database and the affinity score determined for each fitted combination of a respective odor molecule with a respective olfactory receptor, providing the (3D) structure of at least a first odor molecule of interest, which is not comprised within the second database, and predicting an odor sensation induced by the at least first odor molecule of interest in a human subject using machine learning or AI considering the data comprised within the third database or third data embedding structure / layer.

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.

A computer-implemented method for estimating the glass transition temperature of a polymer from the chemical structure of the corresponding polymer is provided. The invention provides a method for graphically representing the chemical structure of a polymer. And a method and system for calculating a glass transition temperature of a corresponding polymer from graphic data of the polymer by machine learning a chemical structure of the polymer based on information defining an interconnection relationship between each atom constituting a repeating unit of the polymer and a connection node to which the repeating unit of the polymer is connected, wherein in addition to the chemical structure of the polymer, the molecular weight information is also reflected.

A Tröger's base (TB) complex including a chemical structure I where ‘X’ is O or N—R1, R1 is an aryl group, a substituted aryl group, a heteroaryl group, a Tröger's base compound, or a substituted Tröger's base compound. Further, a polyimide including the TB complex. The TB complex can be used in membrane films.

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 an amphiphilic molecule as well as a preparation method and application thereof. The general formula of the chemical structure of the amphiphilic molecule provided by the invention is shown as a formula I: # imgabs0 #, R comprises a Lewis base group, X comprises at least one of hydrogen and halogen atoms, and n is an integer greater than or equal to 0. The amphiphilic molecule provided by the invention has a Lewis acid group and a Lewis base group at the same time, so that the amphiphilic molecule can be used for passivating two defects of perovskite crystal boundaries, namely anions with insufficient coordination and cations with insufficient coordination; therefore, when the amphiphilic molecule is used in a perovskite material, the conversion efficiency and the stability of perovskite can be remarkably improved.

A polycyclic compound represented by formula (1) provides a highly efficient and long-life organic electroluminescent element, wherein at least one of Cy1 and Cy2 has a chemical structure represented by general formula (A1) or (B1). In general formulae (1), (A1), and (B1), R1 to R31, Cy1 to Cy2, L1, L2, and X1 to X3 are completely the same as defined in the description. # imgabs0 #

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 UV-resistant perfluoropolyether siloxane as well as a preparation method and application thereof, the chemical structural formula of the perfluoropolyether siloxane is as shown in formula I, and in the formula I, m and n are independently integers greater than or equal to 1. According to the invention, two functional groups with different functions, such as the micromolecular UV absorbent and the amino siloxane, are respectively introduced into the dihydroxyl perfluoropolyether, so that the UV resistance, the wear resistance and the like of the coating are remarkably improved, and excellent hydrophobic and antifouling effects are achieved. In addition, due to the existence of multiple silicon-oxygen bonds, the adhesive force of the coating can be effectively enhanced, and the durability is improved. And the formula I of the # imgabs0 # is shown in the specification.