188 results about "Molecular geometry" patented technology

The invention relates to a generative molecule reverse design system based on reinforcement learning, which comprises a data set construction module, a multi-target performance prediction model establishment module, a pre-training module, a reward function construction module and an optimization module, and is characterized in that the data set construction module is used for constructing and screening to obtain a molecular structure performance data set; the multi-target performance prediction model establishment module is used for establishing a multi-target performance prediction model based on the constructed molecular structure performance data set; the pre-training module is used for pre-training a molecular generation model by using the screened molecular structure data; the reward function construction module is used for constructing a layered multi-target reward function; and the optimization module is used for rapidly evaluating key indexes by using a performance prediction model by adopting a reinforcement learning method, and carrying out optimization adjustment on the molecular generation model through a layered multi-target reward function. According to the invention, efficient and systematic reverse design of lithium metal negative electrode interface self-assembly molecules can be realized.

The invention discloses a three-dimensional molecule generation method fusing conditional flow matching and Van der Waals radius constraint, which comprises the following steps: processing a molecule training data set, and extracting a total number of atoms and a training element component histogram; based on the optimal transmission path interpolation, combining the sampling time step and the standard Gaussian noise to construct a noise coordinate and a target condition velocity field; the noise coordinates are input into a continuous flow matching prediction model, node features are extracted through affine transformation modulation, a prediction velocity field is obtained, soft atom type distribution is generated, and the expected Van der Waals radius of each atom type is calculated; calculating flow matching loss through a prediction velocity field and a target condition velocity field, calculating a geometric collision penalty term in combination with an expected Van der Waals radius and a noise coordinate, and constructing a total loss function training model parameter; and defining an ordinary differential equation by using the trained parameters for solving, and outputting a three-dimensional molecular structure file. According to the method, atom space overlapping is inhibited, and the physical rationality and chemical effectiveness of generated molecules are improved.

The invention discloses a multi-target drug molecule generation model construction method and a multi-target drug design method, and relates to computer drug design and bioinformatics. Determining a corresponding two-dimensional molecular map based on the SMILES sequence; the fully-connected pharmacophore diagram and the two-dimensional molecular diagram are used as input of a GatedGCN module, each atomic node in the two-dimensional molecular diagram is connected with all pharmacophore nodes in the fully-connected pharmacophore diagram in message transmission, and information exchange between different nodes is achieved; the masked SMILES sequence serves as the input of an encoder, and the decoder generates an SMILES sequence conforming to pharmacophore characteristics in an autoregression mode; freezing the network parameters of the GatedGCN module and the encoder; and training the multi-target drug molecule generation model through the elite multi-target molecule set, and reversely optimizing and updating decoder network parameters according to an output result. According to the method, the model fully learns the multi-target molecular structure characteristics, and the multi-target drug molecule generation accuracy is improved.

The invention discloses a method and system for determining the reaction activity of material molecules based on quantum computing, and relates to the technical field of material performance analysis based on quantum computing, and the method comprises the following steps: generating a secondary quantization molecular Hamiltonian based on a target material molecular structure; traversing non-zero coefficient excitation terms in the molecular Hamiltonian, and screening an excitation operator conforming to the symmetry of a molecular point group as a target excitation operator; generating symmetric unitary coupling cluster single-double excitation simulation according to the target excitation operator; constructing a quantum circuit based on the configuration; and determining the reaction activity of the target material molecule by using the quantum circuit. According to the method, traditional manual symmetry analysis is avoided, professional knowledge is not needed, and the operation process is simplified. The method is systematized, easy to program and implement, high in universality and capable of being automatically expanded to any molecule, the design cost is remarkably reduced, and the molecular energy level solving efficiency is improved.

This invention relates to a method and apparatus for predicting molecular properties by integrating three-dimensional structure and prior features. The method includes: identifying a set of predicted properties and a set of prior properties; constructing a molecular property prediction model based on the predicted and prior property sets; constructing a model training dataset and training the molecular property prediction model based on the dataset; after model training, receiving first molecular information input by the user, preparing model input data based on the first molecular information and the prior property set, and inputting the three-dimensional molecular structure M and prior characteristic vector X obtained from this data preparation into the molecular property prediction model to predict the corresponding property prediction vector Y, which is then fed back to the current user. This invention can reduce prediction complexity, shorten prediction time, and improve prediction efficiency.

The invention discloses a monomolecular structure design and generation method based on a large language model, and relates to the crossing field of monomolecular electronics and artificial intelligence. In order to solve the problems of low single molecule design efficiency and insufficient data, SingMolT5 is obtained through field fine tuning on the basis of MolT5, and generation from a natural language to a molecular SMILES is realized. According to the method, data are collected from literatures in the single molecule field and an existing disclosed molecule library, a fine tuning data set containing 329 high-quality instructions is constructed, and the fine tuning data set comprises three basic data types including a molecular skeleton, an anchoring group and molecules. The model is initialized by a MolT5 check point, and a cross entropy loss function is used in the fine tuning process; reasoning, generating and using a beam search strategy; the follow-up evaluation indexes comprise BLEU, Levenshtein, MACCS, RDK, Morga and effectiveness. The method provides an innovative technical path for'low experience and data dependent type single molecule design 'in the field of single molecule electronics, and is particularly suitable for aided design of a single molecule device core function unit and a single molecule sensing probe molecule, namely, a target molecular structure can be quickly converted through a natural language, the design threshold of researchers is reduced, and the design efficiency is improved. And a traceable technical basis can be provided for screening and iteration of a monomolecular structure based on a high-quality data set and rigorous evaluation logic.

The invention discloses a liquid crystal performance prediction method and device based on molecular dynamics and a liquid crystal theory, and relates to the technical field of liquid crystal performance prediction.The method comprises the steps that the molecular structure of liquid crystal to be simulated is confirmed, and the molecular structure of the liquid crystal is obtained; generating a coarse graining molecular structure and a coarse graining force field parameter through a coarse graining scheme; constructing a nematic phase initial structure of the volume phase system; performing molecular dynamics simulation at a set temperature to obtain molecular dynamics simulation trajectory data, calculating to obtain a molecular director of liquid crystal molecules, and determining an overall director; a second-order-sequence parameter and a fourth-order-sequence parameter are obtained through calculation; constructing a correlation function and fitting to obtain the rotation relaxation time of the system; according to a Nemtsov-Zakharov formula or a Fialkowski formula, the rotary viscosity of the liquid crystal is obtained through calculation; constructing a change relation of the second-order-sequence parameter along with the temperature; and performing fitting analysis through a Maier-Saupe theory or a critical index formula to determine a phase change point. The liquid crystal performance prediction efficiency can be improved, the cost is reduced, and the prediction precision is improved.

The application discloses a bismaleimide resin performance prediction model training method, a performance prediction method, a structure design method and related products, relates to the technical field of bismaleimide resin, and the performance prediction model training method comprises the following steps: taking a molecular structure as input, taking a test condition as a supplementary input feature, taking a dielectric constant as a label, taking a predicted value of the dielectric constant as output, training a supervised learning model to obtain a dielectric constant prediction model; taking the molecular structure as input, taking melting point data as a label, taking a predicted value of the melting point data as output, training the supervised learning model to obtain a melting point prediction model; and taking the molecular structure as input, taking a 5% thermal decomposition temperature as a label, taking a predicted value of the 5% thermal decomposition temperature as output, training the supervised learning model to obtain a thermal decomposition temperature prediction model. The three prediction models are obtained by training the supervised learning model, so that the performance prediction of the bismaleimide resin material can be realized based on the theoretical model.

The invention discloses a method for identifying a molecular structure and constructing a reaction network based on atomic coordinates, and belongs to the technical field of material simulation. According to the method for recognizing the molecular structure based on the atomic coordinates, by obtaining trajectory data, atoms outside a box are converted into atoms inside the box through minimum mirror agreement; grids are divided according to a space truncation radius, potential bonding atoms are accurately screened, and redundancy of distance calculation between every two atoms of the whole system is avoided; atomic clusters are quickly divided in combination with depth-first search, and molecular structure recognition is achieved. The method only depends on basic data such as atomic coordinates and does not depend on a simulation software post-processing module, and the limitation that only a single simulation system is adapted in the prior art is broken through. According to the method for constructing the reaction network, molecular structure description is compared frame by frame, and invalid reactions are offset and filtered through positive and negative reactions to obtain net reactions; counting the net reaction times to determine the reaction probability so as to quantify the tendency of the reaction; and constructing a reaction network by taking the molecular structure as a node and the reaction frequency as an edge weight.

This invention provides a system and program that facilitates the process of generating and / or visualizing molecular networks for library molecular structure content. [Solution] In system 500, the molecular network generation system 502 for library molecular structure content includes an evaluation component 512 that performs a comparison between a first molecular fingerprint including first molecular structure data of a first molecular structure and a second molecular fingerprint including second molecular structure data of a second molecular structure, and a visualization component 516 that generates display data that visualizes a representation of the structural similarity score obtained from the first molecular structure, the second molecular structure and the comparison. The representation by the visualization component includes edges corresponding to the structural similarity score that extend between pairs of nodes corresponding to the first molecular structure and the second molecular structure.

The invention discloses a quantum chemical screening method for extracting arbutin, and relates to the technical field of natural product extraction, and the quantum chemical screening method comprises the following steps: constructing an arbutin single-molecule model and a hydrogen bond donor and hydrogen bond receptor single-molecule model by using GaussView software, firstly performing conformation search through MOPAC and molplus programs to obtain a most stable molecular structure, and then extracting the arbutin from the molecular structure; carrying out geometric optimization and single-point energy calculation by utilizing Gaussian; the optimized hydrogen bond donor and hydrogen bond acceptor establish random clusters according to different molar ratios, the random clusters with target molecules are established, a cluster model with an optimal structure is selected for geometric optimization and single-point energy calculation, meanwhile, the binding energy between the eutectic solvent and arbutin is calculated, and IGMH analysis, ESP analysis and AIM analysis are carried out. The invention provides a universal method for green and efficient extraction of active ingredients of natural products.

The invention relates to the technical field of electrical data processing, and provides an anthocyanin stabilizer screening method and system based on fusion of quantum chemistry calculation and machine learning. The method comprises the following steps: constructing a molecular structure data set; performing geometric structure optimization on the anthocyanin molecules and the candidate polyphenol molecules to obtain a ground state optimization structure; performing multi-dimensional molecule descriptor calculation on the candidate polyphenol molecules to obtain a polyphenol molecule descriptor matrix; performing structure optimization, single-point energy calculation and free energy calculation on the anthocyanin polyphenol compound system to obtain a combined free energy value set; integrating the polyphenol molecule descriptor matrix and the combination free energy value set, constructing a training data set, and performing feature screening; performing model training through a machine learning algorithm to obtain a combined free energy prediction model; and predicting the binding free energy of the anthocyanin polyphenol compound through the binding free energy prediction model to obtain candidate molecules of the anthocyanin stabilizer. The screening efficiency of the anthocyanin stabilizer is improved.

The invention relates to a characterization method of a thermal decomposition mechanism of an azo initiator. The characterization method comprises the following steps: (1) carrying out thermal stability analysis on the azo initiator; (2) acquiring microscopic decomposition characteristic data of the azo initiator by using a thermogravimetric-Fourier transform infrared spectroscopy combined technology; (3) constructing a three-dimensional molecular structure model of the azo initiator; (4) calculating the bond dissociation energy of the key covalent bond based on the density functional theory; and (5) the thermal decomposition characteristic temperature, the heat release behavior, the decomposition product and the evolution path of the azo initiator are determined by integrating the experimental data measured by the combination technology and the bond dissociation energy obtained by theoretical calculation, and the complete thermal decomposition mechanism of the azo initiator is clarified. According to the method, the thermal decomposition rule of the azo initiator is disclosed comprehensively and accurately through a technical path of combining experimental analysis and theoretical simulation and verifying macroscopic characteristics and micromechanism, and a key theoretical basis and technical support are provided for preventing and controlling the thermal runaway risk of such substances.

The invention belongs to the technical field of computational chemistry and artificial intelligence crossing, and provides a molecular potential energy surface prediction method based on deep learning. The method comprises the following steps: inputting a structure file of an unoptimized molecule, and reading and predicting energy and force of a current molecular structure by utilizing a machine learning potential model; and then, judging whether the structure is converged or not according to a prediction result by adopting a GeomeTRIC algorithm, and if not, adjusting coordinates to generate a new structure and performing circular prediction until the structure is converged. According to the method, the quantum chemistry and the deep learning technology are combined, the calculation efficiency can be greatly improved, complex inter-atomic interaction in molecules can be fully captured, and the prediction accuracy is ensured; and meanwhile, batch processing and automatic generation of a quantum chemistry software interface are supported, and the method is particularly suitable for high-throughput molecular structure optimization in the fields of drug discovery, material science and chemical product design.

The application discloses a molecular property prediction method based on spectral position coding and bionic side inhibition gating, and relates to the field of olfactory perception. The method comprises the following steps: constructing a molecular structured input comprising atomic types, a Coulomb matrix and graph Laplacian eigenvector; fusing atomic chemical attributes and spectral domain topological positions through a feature extraction module to generate initial atomic features; using a multi-scale aggregation module, hierarchical neighborhood aggregation is carried out based on preset scale constraints to extract multi-level structure features covering chemical bonds, functional groups and molecular skeletons; with the help of a global interaction module of bionic side inhibition gating, local features are dynamically modulated by a global query signal to realize adaptive denoising and semantic sharpening; finally, through mask pooling and a decoupled multi-label prediction head, the scores of each odor attribute of the molecule are output. The application significantly improves the accuracy, structure perception ability and cross-task generalization performance of molecular property prediction.

The challenge is to reduce the computational cost when performing distributed learning with parallel data generation. [Solution] An information processing device that performs machine learning on a machine learning model that extracts molecular structure features using different molecules as multiple training data obtains the number of atoms contained in each of the multiple training data. If there is a first training data with a number of atoms greater than or equal to a threshold among the obtained number of atoms in each of the multiple training data, the first training data is replaced with another number of atoms that is less than the threshold, based on the other training data among the multiple training data.

The application relates to a molecule generation method and device, a molecule design method and device, and an electronic device. The molecule generation method comprises the following steps: obtaining a protein molecule comprising a protein pocket conformation and at least one starting fragment conformation matched with the protein pocket conformation; setting a current starting fragment conformation in the at least one starting fragment conformation to a current matching position of the protein pocket conformation, and determining a growth direction based on a starting growth site of the current starting fragment conformation; obtaining a growth fragment conformation, wherein the growth fragment conformation comprises a connection site and a first growth site; connecting the starting growth site and the connection site in the growth direction to obtain a grown starting fragment conformation; taking the grown starting fragment conformation as the current starting fragment conformation; and repeating the last two steps until a stop growth condition is met. The application can make the generated molecule structure matched with the protein pocket.

The invention discloses a chemical molecule file format conversion method and system based on artificial intelligence, and relates to the technical field of chemical informatics. Comprising the steps of obtaining a to-be-processed chemical molecule file; performing format cleaning on the to-be-processed chemical molecule file to obtain an initial chemical molecule file; performing format conversion on the initial chemical molecule file to obtain an intermediate chemical molecule file; the molecular structure of the intermediate chemical molecule file is converted into an SMILES expression, and the SMILES expression is obtained; rendering functional groups of the complete molecular structure to obtain a visual molecular map; and summarizing the original file name, the normalized file name, the SMILES expression and the visual molecular graph into a structured spreadsheet to obtain a format conversion result of the chemical molecular file to be processed. According to the method, the batch processing requirements of multi-format and multi-language chemical molecule files can be effectively met, and the accuracy and efficiency of chemical molecule file format conversion are remarkably improved.

The application relates to the technical field of electric digital data processing, and discloses a molecular design constraint condition generation method, system, device and medium based on an interpretable artificial intelligence, which comprises the following steps: acquiring molecular structure data marked with active / inactive labels, and extracting molecular property features and / or molecular fingerprint structure features; training a molecular activity prediction model by using a machine learning algorithm; applying the model to a to-be-tested molecule, analyzing a prediction result by using interpretability analysis, and generating a molecular design constraint condition. The application can convert the prediction result of the artificial intelligence model into specific and executable molecular design guidance, and breaks through the limitation that a traditional model can only output "whether active" but cannot explain "why active" and "how to design". The application can be flexibly adapted to various molecular feature input modes, can provide the most comprehensive design prompt, significantly reduces the dependence on expert experience, and improves the efficiency and success rate of molecular design.

This invention discloses a novel molecular fingerprinting algorithm for assisting in the design of MOFs for acid gas separation, comprising the following steps: S1, using drawing software to draw 17 possible ortho, meta, and para positions of benzene rings, five-membered rings, and six-membered rings, obtaining their .mol ​​files; S2, converting the .mol ​​files to .smart format and writing a separate .py file for the new fingerprint; S3, calling the original fingerprint in the newly written .py file, adding the two results to obtain a new 184-bit fingerprint; S4, using the new fingerprint to test multiple CoRE-MOFs using various machine learning methods. This invention can determine whether the N-bond in a five-membered or six-membered ring of a macromolecule is ortho, meta, or para, which is beneficial for identifying differences in molecular structure between molecules with different performance characteristics, thereby accelerating the screening of high-performance materials, effectively saving time and costs, and shortening the development cycle.

The invention provides an organic compound and application thereof, the organic compound has a structure as shown in a formula I. Through the design of a molecular structure, especially through the design and optimization combination of three groups on arylamine, the organic compound has appropriate spatial configuration, excellent hole injection and transmission performance, excellent carrier migration / transmission capability, and excellent electron mobility. The comprehensive photoelectric property is excellent. The organic compound is used for the organic electroluminescent device, is especially suitable for being used as a main body material, and can regulate and control carrier transport balance in the device and improve the recombination capability of excitons, so that the working voltage of the device is reduced, the efficiency of the device is improved, the service life of the device is remarkably prolonged, and the comprehensive photoelectric property of the device is optimized.

This invention discloses an atomic-level construction method for a fixed-axis molecular rotor and its array on a semiconductor substrate. Metal atoms and phthalocyanine molecules are deposited on the semiconductor substrate surface using an ultra-high vacuum thermal evaporation method. The phthalocyanine molecules are laterally manipulated using a scanning tunneling microscope (STM) to place them onto the surface-adsorbed metal atoms. The metal atoms are then longitudinally manipulated using the STM to place them onto the phthalocyanine molecules, resulting in a metal atom-phthalocyanine molecule-metal atom fixed-axis molecular rotor composite structure. The rotation and control of the molecular rotor are achieved by applying a gate voltage to regulate the local potential. This method achieves atomic-level precise anchoring of the molecular rotor composite structure and realizes the rotor array arrangement. The rotation speed is controlled by regulating the local potential through the gate voltage of pre-embedded electrodes. By precisely designing the molecular rotor array using individual atoms and molecules as basic units, the problem of the inability to reposition and arrange molecular structures is solved.

This invention provides a novel material for preparing X-ray structural analysis samples of organic compounds. The X-ray structural analysis sample forming material of this disclosure comprises a metal complex crystal. The metal complex crystal comprises a metal ion and a ligand coordinated to the metal ion, the metal complex crystal having a three-dimensional network structure with regularly ordered pores, the ligand comprising a carboxylic acid ligand (c) and a pyridine ligand (p), the carboxylic acid ligand (c) and the pyridine ligand (p) being a combination of the following [I] or [II]: [I] a compound represented by formula (c1) and a compound represented by formula (p1); [II] a compound represented by formula (c2) and a compound represented by formula (p2).

In various examples, methods for generating accurate three-dimensional molecular structures using a generative artificial intelligence model include receiving a request to generate a molecular structure, the request including an initial structure, atom type information, and bond type information for the molecular structure; generating a fused input feature based on embedding representations of the initial structure, the atom type information, and the bond type information; generating an intermediate predicted molecular structure based on a transformer layer of a generative artificial intelligence model and the fused input feature; refining, using a graph neural network-based layer in the generative artificial intelligence model, the intermediate predicted molecular structure into the molecular structure; and outputting the molecular structure.

The invention belongs to the field of Po-210 aerosol characteristic calculation, and particularly relates to a Po-210 aerosol particle characteristic and decay kinetics calculation method. The method comprises the following steps: S1, determining an optimal theoretical method; s2, constructing an initial molecular structure of one or more candidate Po species to be researched, and completing construction of the initial molecular structure; s3, geometric structure optimization and frequency calculation are carried out; s4, a Lennard-JonesLJ potential parameter is calculated, and the Lennard-JonesLJ potential parameter is calculated; s5, determining thermodynamic properties of the candidate chemical species based on the LJ potential parameters obtained in the step 4; and S6, calculating to obtain the characteristics of the Po-210 aerosol. Based on quantum chemistry, molecular dynamics and a Monte Carlo method, particle nucleation, collision and combination mechanisms and decay dynamics of Po-210 in a gas space are disclosed, key mechanism support is provided for predicting environment evolution behaviors and designing source foot control strategies, and a feasible calculation normal form is provided for predicting cross-scale behaviors of highly toxic radioactive substances.

The invention relates to a cyano-stilbene mercury ion fluorescent probe compound and a preparation method thereof. The molecular structure of the compound is shown in the specification.

The application discloses a traditional Chinese medicine small molecule Gibbs free energy prediction method and system based on deep learning. The application receives a traditional Chinese medicine small molecule in the form of a SMILES string or a molecular structure file, and after structure verification and standardization processing, the traditional Chinese medicine small molecule is converted into a molecular graph representation. The molecular graph is input into a deep learning model. The model captures the molecular structure characteristics and interaction relationship through a graph representation learning mechanism, refines the representation through multi-layer updating, and finally outputs the predicted Gibbs free energy value of the traditional Chinese medicine small molecule through a pooling operation. The test set determination coefficient R2 is greater than or equal to 0.99. The application improves the calculation efficiency and prediction accuracy while maintaining accuracy. In addition, the model of the application supports general equipment deployment, and has strong deployment flexibility.

The invention provides a molecular structure determination method and device, electronic equipment and a storage medium, and belongs to the technical field of molecular optimization. The electronic device can input a two-dimensional molecular graph and a three-dimensional molecular graph of an initial molecule into a graph encoder to obtain molecular structure characteristics of the initial molecule, input a target molecule text into a text encoder to obtain text characteristics, and input the molecular structure characteristics and the text characteristics into a diffusion model. And adjusting the molecular structure features based on the text features through a diffusion model to obtain the molecular structure of the target molecule. According to the method, the two-dimensional molecular diagram and the three-dimensional molecular diagram can be processed, when the molecular structure is optimized, the two-dimensional structure of the molecule is considered, the three-dimensional structure of the molecule is combined, the molecule is optimized based on the two-dimensional structure and the three-dimensional structure of the molecule, the utilized molecular structure information is more complete, and the molecular structure optimization efficiency is improved. Therefore, the accuracy of the predicted molecular structure of the target molecule can be improved.

The present application relates to the technical field of electric data processing, and provides a screening method and system for anthocyanin stabilizer based on quantum chemistry calculation and machine learning fusion. The method comprises the following steps: constructing a molecular structure dataset; performing geometric structure optimization on anthocyanin molecules and candidate polyphenol molecules to obtain ground state optimized structures; performing multidimensional molecular descriptor calculation on the candidate polyphenol molecules to obtain a polyphenol molecule descriptor matrix; performing structure optimization, single-point energy calculation and free energy calculation on the anthocyanin polyphenol complex system to obtain a binding free energy numerical set; integrating the polyphenol molecule descriptor matrix and the binding free energy numerical set, constructing a training dataset and performing feature screening; performing model training through a machine learning algorithm to obtain a binding free energy prediction model; and predicting the binding free energy of the anthocyanin polyphenol complex through the binding free energy prediction model to obtain candidate molecules for anthocyanin stabilizer. The present application improves the screening efficiency of anthocyanin stabilizer.

The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing a contrastive molecular-phenomic embedding model that learns joint latent space embeddings between molecular structures and phenomic images to generate molecular-phenomic embeddings that represent molecular impacts on cellular functions. Indeed, the disclosed systems can utilize phenomic image embeddings generated from a pretrained phenomic image encoder model and corresponding molecular structural embeddings with a contrastive molecular-phenomic embedding model to learn a joint latent space between molecular structures and phenomic images utilizing a modified rank-n-contrast loss with a learnable temperature parameter. In addition, the disclosed systems can utilize molecular structures and / or phenomic images with the contrastive molecular-phenomic embedding model to generate molecular-phenomic embeddings that enable a variety of molecular inferences (e.g., similar molecule determinations, similar phenomic image determinations, phenotypic impact determinations from particular molecules, molecular activity classifications, and / or inactive region filtering).