65 results about "Molecular interactions" patented technology

The invention relates to a UCNPs probe-based in-situ dynamic monitoring molecular interaction method, which comprises: S1, providing a UCNPs probe, the core layer of the UCNPs probe being a rare earth up-conversion nanocrystal, the shell layer of the UCNPs probe being an anti-quenching inert material, and the anti-quenching inert material coating the surface of the rare earth up-conversion nanocrystal; the surface of the anti-quenching inert material is modified with ligand molecules, and the ligand molecules are used for being specifically combined with target molecules in a to-be-detected sample; s2, mixing or contacting the UCNPs probe with a to-be-detected sample containing target molecules to construct a to-be-analyzed molecular interaction system; s3, irradiating the molecular interaction system by adopting near-infrared pulse laser, and collecting the change of the fluorescence lifetime tau of the UCNPs along with the time t and the change of the fluorescence intensity I along with the time t in real time to obtain a tau-t curve and an I-t curve; and S4, according to the tau-t curve and the I-t curve, realizing in-situ dynamic monitoring on the molecular interaction state. According to the method, a molecular binding / dissociation event is directly converted into a quantifiable and anti-interference optical signal, and the technical defect that in-situ dynamic monitoring of molecular interaction cannot be achieved in a traditional technology is overcome.

The invention belongs to the technical field of biomolecular interaction detection, and particularly relates to a method for synergistically analyzing a molecular interaction state based on multimodal optical parameters of a rare earth up-conversion nano material. The method comprises the following steps: S1, providing a ligand coated rare earth up-conversion nano material UCNPs probe; s2, enabling the rare earth up-conversion nano material UCNPs probe to be in contact with a solution sample to be detected; s3, exciting the rare earth up-conversion nano material UCNPs probe by using near-infrared pulse laser, and collecting the following optical parameters: luminous intensity change, molecular layer thickness / mass change delta D, luminous life change delta tau and emission peak displacement delta lambda; and S4, based on at least two of the parameters, cooperatively analyzing and judging the interaction state of the ligand on the surface of the probe and the molecules in the solution sample to be detected. The molecular interaction state is synergistically analyzed based on changes of multiple optical parameters, and the technical problem that an existing molecular interaction analysis technology cannot be applied to complex biological samples (serum and cell lysis buffer) is solved.

The invention discloses a multi-mode chemical reaction yield prediction method based on adaptive data screening, and belongs to the technical field of chemical synthesis and machine learning crossing. According to the method, a multi-modal input system containing one-dimensional chemical attribute data, a two-dimensional molecular structure map and a three-dimensional spatial configuration is constructed, so that multi-dimensional analysis of molecular interaction is realized; and meanwhile, a multi-stage training process is adopted to simulate a human cognitive rule, so that the model progressively learns layer by layer from a basic reaction feature to a complex reaction mechanism. According to the method, the screening threshold is dynamically adjusted, noise interference is effectively suppressed, and the adaptability to long-tail distribution data is improved. Compared with a traditional single-mode prediction model, the method has the advantages that the prediction accuracy on a noisy data set is improved, the trial and error cost of chemical synthesis experiments is remarkably reduced, and reliable technical support is provided for efficiently screening reaction conditions and accelerating research and development of new compounds.

The invention belongs to the technical field of dynamic simulation, and discloses a construction method of a molecular dynamic model for different bubble contents of ice and methane hydrate, and the method comprises the following specific steps: 1, selecting a crystal structure of the ice and methane hydrate as a model basis for initial structure construction; by constructing a molecular dynamic model of an ice and methane hydrate coexistence system, the problem of extensive quantitative regulation and control of bubble content is solved, accurate association of microscopic molecule removal and macroscopic volume fraction is realized, force field parameter suitability of a cross-phase interface is optimized, interaction of a hydrogen bond network and molecules at an ice and methane hydrate interface is accurately described, and the method has the advantages of simple operation and low cost. The simulation precision of the molecular diffusion behavior and the structural stability of the interface area is improved, meanwhile, the bubble form diversity and the temperature and pressure coupling environment are comprehensively considered, a multi-dimensional experimental verification system is established, and the influences of different bubble contents on the thermodynamic property, the dynamic behavior and the mechanical property of the system can be systematically evaluated.

This invention discloses a ferroptosis target identification system and method based on an attention mechanism, belonging to the field of artificial intelligence technology. The system includes modules for extracting multi-source features of ferroptosis regulation, labeling weak signal targets, calculating attention potential energy mapping, and ranking ferroptosis targets. It extracts multimodal features from transcriptome expression, molecular interaction networks, and ferroptosis pathway annotations. Targets with expression intensities below a preset threshold that participate in the ferroptosis pathway are labeled with weak signals. Weak signal characteristics are injected, and a potential energy bias is constructed to complete attention potential energy mapping. Then, through pathway mapping and cross-pathway linkage identification and fusion weights, the target linkage degree is calculated and ranked. This system solves the problem of traditional methods easily neglecting weak signal targets, and can identify targets with insignificant expression changes in the ferroptosis regulatory network. Through attention and pathway-level linkage modeling, the comprehensiveness of target identification is improved, and the obtained ranking results have stability and discriminative power, making it suitable for the identification and research of ferroptosis-related targets.

The invention discloses a method and system for predicting the effect of repairing lung injury by isoliquiritigenin based on machine learning, and relates to the field of drug effect prediction.The method comprises the steps that biomarker data and historical experiment repairing effect data are obtained; on the basis of molecular structure data and biomarker data, calculating a molecular interaction force index and a cell permeability index of the isoliquiritigenin, and analyzing through a multiple regression analyzer in combination with historical experiment repair effect data to obtain a repair effect coefficient; according to the repair effect coefficient, configuring an effect confidence interval, and performing range optimization on the molecular interaction force index and the cell permeability index to obtain an optimized biological activity index; and constructing an effect prediction model based on a neural network, inputting the optimized biological activity indexes into the effect prediction model, and outputting to obtain a lung injury repair effect prediction value. The problems that the prediction precision is insufficient and the prediction is too one-sided due to the lack of comprehensive analysis on a molecular mechanism and a biomarker are solved.

The invention relates to a postoperative acute pain prediction method, belongs to the technical field of medical data analysis and processing, and solves the problem of insufficient postoperative acute pain prediction precision in the prior art. The method comprises the following steps: acquiring multi-omics data and clinical data of a to-be-predicted patient; constructing a primary network of each omics based on the anchor point molecules of each omics; the second-level network and each later-level network are obtained by expanding the previous-level network based on the molecular interaction relationship; taking each level of network as a multi-level molecular interaction network of the to-be-predicted patient; adopting a trained deep learning model to extract high-dimensional biological characteristics of a to-be-predicted patient based on the multistage molecular interaction network; and predicting the pain type of the to-be-predicted patient based on the high-dimensional biological characteristics, the multi-omics data and the clinical data. And the accuracy of postoperative acute pain prediction is improved.

The present disclosure provides an affinity prediction method, related devices and media. The method comprises: determining a first molecular structure graph and an atomic structure graph of a first biomolecule, and a second molecular structure graph of a second biomolecule; intercepting a binding region graph from the second molecular structure graph; extracting a first graph feature of the first molecular structure graph, a second graph feature of the atomic structure graph, a third graph feature of the second molecular structure graph, and a fourth graph feature of the binding region graph based on a feature extraction network of a preset affinity prediction model; determining a molecular interaction feature based on the first graph feature, the second graph feature, the third graph feature, and the fourth graph feature; and performing affinity prediction on the first biomolecule and the second biomolecule based on the molecular interaction feature to obtain a binding affinity prediction result. The present disclosure can improve the prediction accuracy of the binding affinity between biomolecules. The present disclosure can be applied to various scenarios such as big data, artificial intelligence, cloud technology, etc.

The invention discloses a difunctional aqueous zinc ion battery high-voltage electrolyte and a preparation method and application thereof, and belongs to the technical field of aqueous zinc ion batteries. The electrolyte is prepared from the additive with the structural general formula that R groups are hydroxyl, carboxyl, acetyl and methoxyl, and the additive contains oxygen atom-containing functional groups capable of serving as hydrogen bond acceptors, so that the water-phase solubility of molecules can be remarkably improved, the electrolyte ion concentration can be adjusted through specific interaction with solvent molecules, and the service life of the electrolyte is prolonged. The solvation sheath structure of zinc ions is regulated and controlled by reducing the number of solvent molecules. Meanwhile, the functional group containing the oxygen atom serving as the hydrogen bond acceptor is a strong electron withdrawing group and can interact with water molecules to form intermolecular hydrogen bonds, the oxygen atom can serve as an electron donor, and the hydrogen atom of water serves as a proton donor to destroy the original hydrogen bonds and tetrahedral structures of the water molecules, so that the water molecules can be absorbed. And the formation of a hydrogen bond network of water molecules is blocked, so that the corrosion side reaction and hydrogen evolution behavior caused by water are effectively inhibited.

A contrast agent for magnetic resonance imaging (MRI) is proposed, comprising a polarizable agent (tracer) possessing a nuclear spin, wherein tracer molecules can be brought into a polarized state by polarizing their nuclear spins, and a retarder, wherein a molecular interaction of retarder molecules with the tracer molecules results in a prolongation of the T1 relaxation time of the tracer. Furthermore, a method for the production of a hyperpolarized contrast agent, a retarder for use in a contrast agent for MRI, a contrast agent for use in a diagnostic procedure in vivo and / or in vitro, and the use of a retarder in a contrast agent for MRI to prolong the T1 relaxation time of the tracer are proposed.

Disclosed is a method for calculating an eigenstate of a Hamiltonian, comprising the steps of providing at least one classical computing unit and at least one quantum computing unit comprising a plurality of qubits, providing a plurality of physical properties of at least one molecule, initializing an initial state on the at least one quantum computing unit to model the properties of the provided physical properties of the at least one molecule, selecting a plurality of operators from a pool of operators using the at least one quantum computing unit performing measurement operations on the diagonal of the Hamiltonian to calculate the eigenstate of the Hamiltonian describing the at least one molecule, estimating an energy of the eigenstate of the Hamiltonian using the selected plurality of operators, and repeating the previous steps until a convergence criterion is met.

The present invention relates to a method of detecting intermolecular interaction kinetics, the method comprising a first molecule and a second molecule to be analyzed, dispersing the first molecule in a solution as a detection solution, attaching the second molecule to a biosensor and being freely translatable over the surface of the sensor; the biosensor comprises a tethered bilayer phospholipid membrane and a solid conductive surface, a lower layer membrane of the tethered bilayer phospholipid membrane is fixed, and an upper layer membrane of the tethered bilayer phospholipid membrane can freely translate; the phospholipid membrane contains ion channel protein; and contacting the detection solution with the biosensor, detecting the conductivity change of the tethered bilayer phospholipid membrane, and analyzing the interaction kinetics of the first molecule and the second molecule. The method for detecting intermolecular interaction kinetics based on tethered bilayer phospholipid membrane and ion channel switch technology development can realize accurate quantitative detection in a wide concentration range, does not need sample flow, and is suitable for the fields of antibody screening, drug screening and life science.

Provided herein are computer-implemented methods for scoring a binding affinity between molecular subsystems. The methods include representing an interaction structure of the molecular subsystems, calculating the molecular interactions between the molecular subsystems, and generating an interaction energy score between the molecular subsystems.

The invention discloses a Leidenfrost effect three-dimensional numerical simulation method based on a double-distribution function lattice Boltzmann method coupled with a finite difference method. The method comprises the following steps: 1, constructing a three-dimensional computational domain and defining lattice point types; 2, initializing a lattice point type, a fluid domain density field, a velocity field, a temperature field and a distribution function; 3, calculating fluid domain density, pressure and thermal diffusion coefficient according to the distribution function; 4, calculating resultant force of molecular interaction force and volume force among different phase states based on a pseudo-potential model; 5, calculating a fluid domain collision process; 6, calculating a migration process; 7, treating wall surface boundary conditions; 8, updating the fluid domain temperature field by using the temperature distribution function, and updating the solid domain temperature field by using a finite difference method; and 9, carrying out loop iteration on the steps 3-8 until the preset simulation time is reached. According to the invention, the whole process of the Leidenfrost phenomenon generated when the liquid drops impact the high-temperature wall surface can be stably and accurately simulated, and an accurate and reliable numerical simulation tool is provided for the design of efficient heat dissipation schemes such as spray cooling and the like.

The invention relates to the field of core-spun yarns, and particularly discloses a core-spun yarn for selectively etching a nylon amorphous region by ionic liquid and a low-temperature preparation process, and the low-temperature preparation process comprises the steps of selective etching, quenching treatment, post-treatment and core-spun yarn spinning. According to the method, the molecular recognition characteristic of the ionic liquid on a nylon amorphous region is utilized, directional dissolution of the amorphous region is realized in a low-temperature environment to form a honeycomb porous network, and a water quenching and cold shock process is further introduced to induce microcracks; the surface of the core yarn is coarsened to enhance mechanical interlocking and molecular action, so that the core and the sheath are forced to bear synchronously, interface reinforcement of a nylon / lyocell or nylon / cotton core-spun system is realized, and the problem of insufficient strength of the core-spun yarn caused by interface failure of the nylon core yarn and the outer cladding layer is solved.

A sensor has a radiation emitter, a plurality of electrodes, a sensing material (transducer), and a recognition element (receptor, bioreceptor). The radiation element irradiates the sensing material, the plurality of electrodes are in electrical contact with the sensing material, and the recognition element is in contact, either directly or through an interface, with the sensing material. The sensing material receives radiation from the radiation source and interacts with the target material largely through the receptor. Further, the sensing material generates a measurable electrical signal upon interaction with a target molecule, when a potential is applied to the electrodes.

The invention relates to a method and a system for constructing a free energy surface of a molecular binding / dissociation path. The construction method of the free energy surface of the molecule binding / dissociation path comprises the following steps: quantifying the relevance of an atom pair distance / angle and a binding state by adopting a mutual information analysis method so as to screen out candidate reaction coordinates; performing short-time pre-simulation verification on the candidate reaction coordinates to determine dominant reaction coordinates; taking the dominant reaction coordinates as navigation, and carrying out adaptive path sampling through reinforcement learning to obtain an optimized path; and calculating free energy of the optimized path, and constructing a free energy surface of the molecular binding / dissociation path based on the obtained free energy. According to the method, the dominant reaction coordinates are automatically identified, and the adaptive path sampling technology is adopted, so that the problems of subjectivity and intermediate state omission of a traditional fixed reaction coordinate method can be solved, the calculation precision and efficiency of a free energy surface can be improved, and a more reliable theoretical tool is provided for molecular interaction mechanism research.

This invention discloses a dual-functional aqueous zinc-ion battery high-voltage electrolyte, its preparation method, and its application, belonging to the field of aqueous zinc-ion battery technology. The invention uses additives with the general structural formula R consisting of hydroxyl, carboxyl, acetyl, and methoxy groups to prepare the electrolyte. These additives contain oxygen-containing functional groups that can act as hydrogen bond acceptors, significantly improving the aqueous solubility of the molecules. Furthermore, they can regulate the electrolyte ion concentration through specific interactions with solvent molecules, thereby controlling the solvation sheath structure of zinc ions by reducing the number of solvent molecules. Simultaneously, these functional groups containing oxygen atoms as hydrogen bond acceptors are strong electron-withdrawing groups, capable of interacting with water molecules to form intermolecular hydrogen bonds. The oxygen atom acts as an electron donor, and the hydrogen atoms in water act as proton donors, disrupting the original hydrogen bonds and tetrahedral structure of water molecules. By blocking the formation of the hydrogen bond network of water molecules, this effectively inhibits water-induced corrosion side reactions and hydrogen evolution behavior.

The invention relates to a contrast agent for nuclear spin imaging, comprising a polarizable agent (tracer) having a nuclear spin, tracer molecules being polarizable by polarizing the nuclear spins thereof, and comprising a retarding agent (retarder), a molecular interaction of retarder molecules with tracer molecules resulting in an extension of the T1 relaxation time of the tracer. The invention further relates to a method for producing a hyperpolarized contrast agent, to a retarder for use in a contrast agent for nuclear spin imaging, to a contrast agent for use in an in vivo and / or in vitro diagnostic method, and to the use of a retarder in a contrast agent for nuclear spin imaging in order to extend the T1 relaxation time of the tracer.

The invention discloses an ellagic acid composite solid dispersion and a preparation method thereof. The preparation method comprises the following steps: S1, respectively preparing a fucoidin aqueous solution and an ellagic acid ethanol suspension; s2, mixing the two solutions to obtain a mixed solution; s3, adding the modified polysorbate 20 and the chitosan-CPP compound, and performing ultrasonic stirring; s4, ball milling; and S5, carrying out reduced pressure rotary evaporation to remove the solvent, drying, crushing, and sieving with a 100-mesh sieve to obtain the product. Ternary synergistic compounding is formed through the fucoidin-modified polysorbate 20-chitosan-CPP compound, a multi-dimensional molecular interaction network is constructed, and the effect of remarkably improving the water solubility and dispersion stability of ellagic acid is achieved; wherein xylooligosaccharide branched chains of the modified polysorbate 20, hydroxyl groups of the fucoidin and phosphate groups of the chitosan-CPP compound form a multi-point hydrogen bond cross-linked structure through hydrogen bonds, and the hydrophobic association effect and lattice stacking among ellagic acid molecules are broken through the steric hindrance effect.

The invention relates to the technical field of bioinformatics and functional food development, and discloses a plant-source alpha-amylase inhibitory peptide high-throughput screening method and application thereof.The method comprises the steps that firstly, white kidney beans are selected and subjected to wall breaking and degreasing treatment, and efficient extraction is conducted; a three-stage gradient process is adopted for enzymolysis, and a series ultrafiltration membrane system is adopted for refining white kidney bean protein; separating white kidney bean protein, performing high-precision mass spectrum identification, screening peptide fragments, and constructing a candidate database; constructing a peptide-amylase molecular interaction screening network; collecting alpha-amylase inhibitory peptide data to carry out data enhancement, training a PAMIS-Net model, and carrying out model performance optimization; inputting the optimized PAMIS-Net model into the database in batches to intelligently screen high-activity candidate peptide fragments, and establishing a comprehensive scoring model for experimental verification; according to the method, the problems of low screening efficiency and poor prediction precision of the active peptide caused by difficulty in integration of multi-dimensional information such as sequences, structures and physicochemical properties in the prior art are solved.

A multi-modal surface plasmon resonance detector for measuring molecular interactions is provided herein. The multi-modal surface plasmon resonance detector includes a detector frame manufactured by metal additive manufacturing, the detector frame comprising integrated conformal cooling passages. The multi-modal surface plasmon resonance detector includes a prism providing a plurality of optical faces for a plurality of optical paths. The multi-modal surface plasmon resonance detector includes a first detection path configured for surface plasmon resonance detection. The multi-modal surface plasmon resonance detector includes a second detection path configured for surface plasmon resonance imaging. The multi-modal surface plasmon resonance detector includes a sensor chip interface configured to receive a sensor chip. The multi-modal surface plasmon resonance detector includes a liquid cooling system configured to circulate liquid through the integrated conformal cooling passages.

Disclosed is a method for classifying observations from an analytical sensor system (20) by interacting (100) a first set of analyte sample solutions with a ligand (3), obtaining (101) a response data set, extracting (102) at least one interaction parameter from the response data, and, for each analyte sample solution, providing the interaction parameter to a trained machine learning algorithm. The trained machine learning algorithm classifies (104) each analyte sample solution into at least one quality classification group indicative of an interaction between the analyte sample solution and the ligand (3) based on the interaction parameter. The machine learning algorithm is trained (200) using the set of interaction parameters extracted from response data obtained from the interaction between a second set of analyte sample solutions and the at least one ligand (3) and the at least one quality classification group indicative of an interaction between the analyte sample solution and the ligand (3).

This application discloses a method, apparatus, device, and medium for determining the force field parameters of fluorinated compounds in some embodiments. In this method, multi-orientation scanning captures anisotropic features that can comprehensively cover molecular interactions, adaptive Boltzmann weighting factors can automatically focus on thermodynamically probable regions, and effective temperature fitting can achieve wide temperature range adaptation, thereby improving the accuracy of determining the force field parameters of fluorinated compounds.

The application discloses a kind of metal organic framework UiO-66 modified terahertz super surface detection lung cancer marker 2-pentanone method.The method is based on a kind of terahertz multi-resonance super surface sensor, and its unit structure is composed of two S-shaped open resonant ring and an antenna, produces three independent resonant valleys.To improve detection selectivity and structural stability, the application is functionalized to super surface using three-layer modification strategy: APTES is introduced in sequence to form stable interface layer, spin-coated amino-rich PEI layer is provided with additional adsorption sites to interact with ketone molecules, and finally spin-coated metal organic framework UiO-66 is used as sensing layer to realize effective pre-enrichment of 2-pentanone.During detection, three resonant valleys show linear red shift with the increase of 2-pentanone concentration, and the lowest test concentration is 20 ppm.The application uses multi-resonance characteristics combined with multi-dimensional response mode to realize rapid, high-sensitivity and high-selectivity non-contact detection of lung cancer marker 2-pentanone.

The invention discloses umami peptide from euphausia superba and application of the umami peptide, and belongs to the technical field of bioactive peptide. The number of the umami peptides from the euphausia superba is five, and the amino acid sequences of the umami peptides from the euphausia superba are shown as SEQ ID NO.1 The umami peptide can be used as or used for preparing a flavor enhancer. According to the method, the antarctic krill protein is taken as a raw material, virtual hydrolysis is carried out, joint screening is carried out by adopting an umami peptide prediction model, and through physicochemical property prediction, five potential umami peptides which are non-toxic and good in water solubility are finally obtained. Molecular interaction analysis shows that the umami peptide can be stably combined with a T1R3 key site, and the interaction mainly comprises non-covalent acting forces such as hydrogen bonds and hydrophobic interaction. The invention constructs a screening and mechanism analysis strategy of the antarctic krill umami peptide based on combination of virtual enzymolysis, multi-model prediction and molecular docking, and provides a theoretical basis for directional development of natural umami peptide and high-valued utilization of antarctic krill marine protein resources.

The application relates to a preparation method of co-assembled nanoparticles, and belongs to the field of nanomaterials. A new type of nanoparticle (MUR NPs) is developed by taking hydrophobic core ursolic acid and hydrophilic shell matrine as a delivery system, aiming at improving the water solubility and bioavailability of resveratrol. And the NSMs compound with good biological activity but unable to be used for drug delivery can be applied. Scanning electron microscopy (SEM), x-ray diffraction (XRD) and Fourier transform infrared (FTIR) spectrophotometry are used to characterize the properties and structure of the molecular interaction in the MUR NPs. In addition, the MUR NPs have good physicochemical stability, have sustained gastrointestinal digestion release characteristics, and improve in-vitro antioxidant and bioavailability. The study may contribute to the development of RES oral delivery systems and the application of hydrophobic active molecules in the field of functional foods.

This invention relates to methods, components, and systems for determining characteristic properties of molecular interactions. The method includes providing a liquid sample comprising particles capable of being in equilibrium and non-equilibrium states. The particles comprise a marker in at least one of their equilibrium and non-equilibrium states. The method further includes subjecting the sample to a conditional transition including a temperature jump and / or a pressure jump, bringing the particles to a non-equilibrium state; reading out the marker as a function of time during at least a portion of the relaxation time of the particles; and determining the characteristic properties of the molecular interactions.

The invention discloses a drug interaction prediction method and device for an ADME process and a medium, and relates to the technical field of medicine. The method is applied to a drug interaction prediction model obtained by training a TRACE model, and comprises the processes of enhanced molecular map construction, feature extraction and prediction. According to the method, external chemical knowledge (chemical element knowledge graph) is introduced in a molecular graph construction process, so that the modeling capability of the model on a molecular complex structure and a long-range dependency relationship is improved, and the adaptability to a traditional Chinese medicine complex component system is remarkably enhanced; the TRACE model adopted by the invention can automatically identify high-risk substructures closely related to the ADME process, and a scientific and efficient solution is provided for safety evaluation and druggability prediction of drug combination of traditional Chinese medicine components and chemical drugs thereof. The risk of molecular interaction in the new drug research and development process and the traditional Chinese medicine modernization process can be effectively reduced, and the improvement of the drug safety management level is promoted.

The invention discloses a chemical evolution prediction method based on a spectroscopy inversion neural network algorithm, and relates to the technical field of inversion prediction. According to the method, a chemical and material database is firstly constructed, so that data support is provided for subsequent prediction; the molecular descriptor fusing the spectral characteristics, the microstructure and the physical property associated information is generated, and the defects that a traditional molecular descriptor is single in information and insufficient in representativeness are overcome; the molecular descriptor is used as input, a spectrum structure-effect relationship prediction model with common feature extraction and multi-branch special prediction capabilities is constructed, and spectrum-structure, structure-effect and spectrum-effect associated synchronous precise learning is realized; the compatibility and reliability of the input data and the model are ensured by carrying out noise reduction and standardization preprocessing on the target spectroscopic data subsequently, and finally, chemical structure change parameters, molecular interaction rules and physical property evolution trend data are directly output through model inversion, so that the defects that a traditional method needs multi-step splitting and experimental verification is tedious are avoided.