21 results about "Molecular modelling" patented technology

The invention relates to the technical field of molecular simulation, and provides a method for constructing a heavy component average molecular structure of thickened oil, which comprises the following steps: inputting parameters, generating a core aromatic ring unit, constructing an inner-layer aliphatic ring unit, constructing an outer-layer aliphatic ring unit, generating a side chain unit, and storing and outputting structural information. A plurality of molecular structures meeting experimental data are generated at a time through a traversal algorithm thought, composition characteristics of a heavy oil complex mixture are met, more available molecular structures are provided for molecular simulation, the accuracy and efficiency of molecular modeling are improved, the molecular structures completely meeting the experimental data can be rapidly generated, and the method is suitable for large-scale popularization and application. And only the mass spectrometer, the elemental analyzer, the nuclear magnetic resonance hydrogen spectrum and the infrared spectrum data need to be input, so that the experiment cost is reduced.

The invention discloses an odor knowledge base construction method and system based on molecular modeling and deep learning, and belongs to the technical field of artificial intelligence and digital odor. According to the method, firstly, smell molecules are subjected to standardization treatment, and smell perception labels of the smell molecules are extracted to construct a smell knowledge base; expressing the odor molecules as a molecular graph structure, introducing molecular position coding to model a three-dimensional geometric configuration, and learning high-dimensional feature expression through a first graph neural network; then selecting odor molecules from the knowledge base and generating mixture candidates based on the similarity so as to form a mixture molecular map; and finally, processing the molecular map of the mixture by using a label prediction model, predicting an olfactory perception label of the mixture, and expanding a result to a knowledge base. According to the method, unified characterization of the single molecule and the mixture is realized, the technical vacancy of mixture olfaction prediction is filled, the dimensionality and expansibility of the knowledge base are improved, and the method can be widely applied to the fields of digital blending, smell recommendation and the like.

The invention discloses a foamed rubber asphalt formula determination method. The method comprises the following steps: S1, constructing a molecular model of a foamed rubber asphalt composite system; s2, performing molecular dynamics simulation on the molecular model to obtain a balanced stable structure configuration; s3, based on the stable structure configuration, under the conditions of different rubber mixing amounts, foaming medium mixing amounts and temperatures, calculating the shear modulus, interface adhesion work and fracture energy of the foam rubber asphalt system; and S4, performing multi-objective optimization by taking the shear modulus, the interface adhesion work and the fracture energy as performance indexes, and determining a combination of the temperature, the foaming medium mixing amount and the rubber mixing amount of the optimal formula of the foamed rubber asphalt. According to the method, the combination of the foaming temperature, the foaming medium mixing amount and the rubber mixing amount is determined through molecular modeling and a multi-objective optimization algorithm, and performance prediction and parameter optimization of the foamed rubber asphalt are achieved.

The invention discloses a construction method and an evaluation method for evaluating and regulating the optical performance of a cellulose acetate film. The construction method comprises the following steps: S1, molecular modeling: carrying out molecular modeling on an acetyl substituted monomer of cellulose acetate; s2, obtaining components alpha xx, alpha yy and alpha zz of polarizability in different coordinate directions corresponding to different wavelengths; s3, constructing a function relationship between the intrinsic birefringence delta n0 (monomer) of a certain monomer of the cellulose acetate monomer and the intrinsic birefringence delta n0i of a certain conformational isomer of the monomer; s4, respectively solving the intrinsic birefringence contributions of 2, 3 and 6 acetyl groups; s5, obtaining substitution degree distribution of 2, 3 and 6 bits; and S6, constructing a CA birefringence function relationship, and solving theoretical birefringence. According to the method, a logical relationship is established between the substitution degree of hydroxyl and acetyl in each monomer conformation, the conformation ratio and the birefringence, a theoretical value that the birefringence of the cellulose acetate is closer to an actual value is obtained, and an effective method is provided for accurate prediction and regulation of optical performance.

The invention relates to a compound having antibacterial and antifungal properties and the chemical structure of Formula X, and to the synthesis method of said compound. Said compound is a piperazine-substituted quinone compound, and the position and orientation of the molecule within DNA and E. coli DNA gyrase B have been determined by means of molecular simulation.

The invention discloses a molecular property prediction and drug design system based on graph comparative learning. The molecular property prediction and drug design system comprises a data preprocessing module, a graph coding module, a comparative learning module, a property prediction module and an interpretability analysis module. According to the method, molecules are modeled into an atom-bond graph structure, a self-supervised contrast learning mechanism is adopted to learn general representation from a large-scale unlabeled molecule library, and a graph neural network with multi-view graph enhancement, a molecule similarity keeping mechanism and substructure perception is combined to obtain a multi-view graph structure; accurate prediction of molecular properties, drug-target interaction prediction and molecular optimization design based on property targets are realized.

The invention relates to deep eutectic solvents (DES) developed through a molecular modeling approach. These solvents serve as environmentally friendly alternatives to high volumes of acids used during the leaching process of bastnaesite mineral. This is the initial step in the lengthy process of mineral processing, and these DES can replace traditional extraction methods. Moreover, the inputs used in creating these solvents have been recognized in the literature for their applications in green chemistry.

The invention provides a low-permeability complex porous medium molecular modeling method which comprises the following steps: performing three-dimensional space expansion on a silicon dioxide crystal based on a selected CLAYFF force field to obtain a superunit cell with a preset size; carrying out simulated annealing on the super-unit cell according to a preset heating parameter and a preset cooling parameter to obtain amorphous silicon dioxide; carrying out three-dimensional space stretching expansion on the amorphous silicon dioxide system according to the total porosity to obtain a porous medium model; obtaining the number of hydroxyl groups needing to be added according to the pore surface area of the porous medium model, and adding the hydroxyl groups to silicon atoms of the porous medium model for surface end capping to obtain a first porous medium model; the second porous medium model is used for carrying out quantity adjustment and / or replacement of hydrophilic hydroxyl groups / hydrophobic methyl groups on the first porous medium model and is adjustable in hydrophilicity and hydrophobicity; and carrying out water molecule input or gas molecule injection on the second porous medium model to obtain a target porous medium molecular model.

The application discloses an MTO reaction process simulation system based on molecular modeling and optimization technology, and relates to the technical field of molecular modeling and digital twinning.The system firstly carries out molecular digitization on raw materials and products in the MTO reaction process, constructs an MTO reaction network, and performs mass balance and heat balance based on reaction path data output by the MTO reaction network, then iteratively optimizes and constructs a reactor model and a regenerator model of an MTO fluidized bed, and finally generates a dynamic simulation scene of MTO catalyst deactivation and regeneration cooperation in a digital twinning environment according to the molecular reaction path data output by the MTO reaction network and the constructed reactor model and regenerator model, and performs regeneration efficiency verification and optimization, so that molecular cascade dynamic simulation and regeneration efficiency precise regulation under the cooperative working condition of the reactor and the regenerator are realized, and the digitalization replication, dynamic optimization and precise prediction of the whole MTO reaction process are realized.

The invention discloses an MTO reaction process simulation system based on a molecular modeling and optimization technology, and relates to the technical field of molecular modeling and digital twinning. The system constructs an MTO reaction network through molecular digitalization of raw materials and products in the MTO reaction process, performs mass balance and heat balance based on reaction path data output by the MTO reaction network, and then iteratively optimizes and constructs a reactor model and a regenerator model of an MTO fluidized bed. Meanwhile, according to molecular reaction path data output by the MTO reaction network and the constructed reactor model and regenerator model, a dynamic simulation scene with MTO catalyst deactivation and regeneration collaboration is finally generated in a digital twin environment, and regeneration efficiency verification optimization is carried out; molecular-level linkage simulation and regeneration efficiency precise regulation and control under the cooperative working condition of the reactor and the regenerator are achieved, and then digital replication, dynamic optimization and precise prediction of the whole MTO reaction process are achieved.

A clinical pathologic data-free and computer-aided prediction method for genetic variation pathogenicity and an inheritance pattern thereof includes a molecular dynamics (MD) simulation step, a computing step, a determination step, and a predicted result production step. The MD simulation step is to perform molecular modeling and computational simulations on channel protein configurations, where the channel protein configurations include a wild-type channel or mutated channels. The computing step is to compute the number of ions passing through the wild-type channel and the number of ions passing through the mutated channel, respectively to obtain a first ion number and a second ion number within a predetermined period. The determination step is to determine the class of each mutated channel according to the first ion number and the second ion number. The predicted result production step is to produce a predicted result according to the classes of the mutated channels.

The application discloses a kind of based on molecular modeling and deep learning's smell knowledge base construction method and system, belong to artificial intelligence and digital smell field.In the method of the application, first, the odor molecule is standardized, and its olfactory perception label is extracted to construct the smell knowledge base;Then the odor molecule is represented as a molecular graph structure, and the molecular position coding is introduced to model the three-dimensional geometric configuration, and the first graph neural network is used to learn its high-dimensional feature representation;Then the odor molecule is selected from the knowledge base and the mixture candidate is generated based on the similarity, so as to form the mixture molecular graph;Finally, the label prediction model is used to process the mixture molecular graph, predict the olfactory perception label of the mixture, and extend the result to the knowledge base.The method realizes the unified representation of single molecule and mixture, fills the technical gap of mixture olfactory prediction, improves the dimension and expandability of the knowledge base, and can be widely used in digital perfumery and smell recommendation fields.

The invention relates to a graph enhancement method and system based on core subgraph retention and structure compression and a storage medium, and relates to the technical field of graph contrast learning. According to the method, a graph augmentation strategy combining core subgraph extraction and information compression is provided for solving the problems that in existing graph contrast learning, random augmentation is prone to damaging a semantic structure, and redundant information is introduced. The method comprises the following steps: encoding an input graph by using a flexible connection GNN, and identifying and retaining a core sub-graph with a stable structure and dense semantics through adaptive frequency spectrum filtering; and an enhanced view is generated on the basis of the core subgraph, semantic consistency is kept by using comparison consistency loss, and compression loss based on an information bottleneck principle is introduced to remove redundant information. According to the method, noise can be effectively suppressed while the semantic core of the graph is reserved, the discrimination and generalization performance of representation are improved, and the method is suitable for various graph representation learning tasks such as molecular modeling, social network analysis and recommendation systems.

Disclosed is a method of generating new drug candidate information using virtual molecular modeling of a protein pocket structure, including identifying hit compounds having selective ligand characteristics only for a target protein, which is a cause of pathogenesis present in cells, from existing compounds using virtual molecular modeling of a protein pocket structure and artificial intelligence, generating information on novel compounds having ligand characteristics similar to the hit compounds for the target protein, and determining, among the novel compounds, novel compounds having predicted binding affinity for the target protein and in vitro properties and in vivo properties that meet preset criteria as new drug candidates, thus generating new drug candidate information, thereby enabling effective development of new drugs with relatively low R&D costs and short development time, the method including a hit compound search step (S100), an analogue / lead compound search step (S200), and a new drug search step (S300).

This invention provides a molecular simulation method for quickly calculating the equilibrium density of a first molecular model, which is modeled as an all-atom model, and at least one of the terminal distance and radius of inertia. [Solution] The molecular simulation method includes an equilibrium state calculation step which includes a step S11 of inputting a plurality of first molecular models that model the first molecule as an all-atom model, a step S12 of inputting a second molecular model that model the second molecule as an all-atom model, a step S13 of inputting a mixed system model that includes a plurality of first molecular models and second molecular models, and a step S14 of calculating the equilibrium state of the mixed system model, and a density estimation step which calculates at least one of the terminal distance and radius of inertia of the first molecular models in the equilibrium state mixed system model, and estimates the equilibrium density of a system that includes only the first molecular model based on the equilibrium density of the second molecule, the mass of the first molecular model, the mass of the second molecular model, and the volume of the mixed system model in the equilibrium state.

The application provides a molecular framework structure construction method and device based on machine learning reasoning, relates to the technical field of molecular construction, and comprises the following steps: modeling a to-be-connected molecule and a new molecule to obtain a first molecule definition model and a second molecule definition model; constructing a first 3D molecule model of the to-be-connected molecule and a second 3D molecule model of the new molecule according to the first molecule definition model and the second molecule definition model; extracting a connectable point with the same coordinate as a connected point and connecting the connected point to obtain an initial molecular structure according to the first 3D molecule model and the second 3D molecule model; deducing based on the initial molecular structure by using inheritance and transformation rules to obtain a deduction result; constructing a training data set according to the deduction result, so that a molecular connection model is trained; and inputting a to-be-spliced graph structure and a quantity into the molecular connection model to obtain a target molecular structure, so that the molecular framework structure construction of splicing periodic structures with high efficiency and high accuracy is realized.

The invention aims to provide a method for screening a zinc-iron alloy corrosion-resistant complexing agent based on a density functional theory aiming at the problems that the existing corrosion-resistant coating design depends on a large number of experiments, the trial and error cost is high and the mechanism is not clear. According to the method, the computational chemistry and wave function analysis technology is utilized, structural modeling, energy decomposition and weak interaction analysis of the metal-complexing agent complex system are completed on a computer, quantitative evaluation is conducted on the coordination stability and electronic structure characteristics of different metal-complexing agent systems under the condition that actual electroplating or corrosion experiments are not needed, and the method has the advantages of being high in repeatability and good in repeatability. According to the method, a plating layer system with relatively high corrosion-resistant potential is predicted and screened, the problems of low efficiency, long period and lack of molecular scale mechanism support in traditional process optimization are solved, and a corrosion-resistant system theoretical design and complexing agent screening method which is based on molecular modeling, good in universality, high in efficiency and low in cost is established.

This invention provides a method for generating information on new drug candidate substances using the virtualization of protein pocket structures. [Solution] The generation method includes the steps of: identifying existing compounds that have selective ligand properties only for target proteins, which are disease-causing proteins present in cells, through virtual molecular modeling of protein pocket structures using artificial intelligence, and generating structural morphological information of the identified existing compounds as active substance information (S100); generating similar substance information for novel compounds that have ligand properties similar to the active substance for the target protein, identifying similar substances among the similar substances that are predicted to bind to the target protein with a binding strength of a predetermined value or higher, and generating the identified similar substance information as leading substance information (S200); and evaluating the physical properties of the leading substances and generating leading substances whose physical properties conform to predetermined criteria as new drug candidate substance information (S300).

The application discloses a kind of molecular modeling method and modeling system based on electron density, wherein the method comprises: obtaining atomic coordinate information in target molecule;Based on atomic coordinate information, electron density grid point information of target molecule is obtained at preset resolution;From electron density grid point information, target electron density value greater than preset electron density value is extracted, and 3-dimensional matrix formed by target electron density value is constructed as the molecular model of target molecule.The technical scheme provided by the application solves the problem that the features contained in the traditional molecular representation model are insufficient.

The disclosure relates generally to methods and systems for dynamic drug design of a pharmacological target. Conventional techniques in the drug design that use in-silico, in-vitro, and in-vivo approaches are not explicitly mentioned workflow related details for identifying novel molecules. The methods and systems of the present disclosure make the drug design dynamically by integrating the in-silico, in-vitro and in-vivo approaches through the dynamic generative artificial intelligence (GenAI) and artificial intelligence (AI) technologies. The integration of in-silico (molecular modeling and AI), in-vitro and in-vivo approaches helps in designing the novel optimized lead molecules. Optimization and prediction of ADMET based on QM based descriptors help in filtering the molecules.

This application provides a method, apparatus, device, medium, and product for generating the molecular composition of complex materials based on macroscopic properties, applicable to the fields of computer-aided chemistry or molecular modeling. The method includes: acquiring measured macroscopic property data and measured molecular composition data of complex materials; constructing a mechanistic model of the complex materials based on the measured macroscopic property data and measured molecular composition data; generating a virtual dataset containing multiple sets of macroscopic property data and molecular composition data based on the mechanistic model; training an initial diffusion model based on the virtual dataset to obtain a target diffusion model; and inputting the macroscopic property data to be processed into the target diffusion model to obtain the target molecular composition data corresponding to the macroscopic property data to be processed. This achieves the technical effect of improving the efficiency of generating molecular composition information of complex materials.