391 results about "Molecular geometry" patented technology

The invention discloses a multi-agent cooperation molecular optimization method based on large language model driving. The method comprises the following steps: S1, task initialization and input analysis; s2, constructing an intelligent agent cluster; s3, task decomposition and scheduling execution; s4, performing expert optimization and tool calling; s5, performing evaluation and feedback screening; and S6, multi-round optimization and result output: the scheduling agent adjusts an optimization strategy and redistributes tasks according to a feedback result of the step S5, drives the expert agent to execute a next round of optimization operation, circulates the steps until the evaluation agent judges that an optimization target is met, and outputs a final optimization molecule and related attribute information thereof. And outputting an optimized path and an intermediate result for tracing analysis. According to the method, multi-round optimization and evaluation feedback iteration of a molecular structure are realized by fusing the knowledge reasoning ability of a large language model and an efficient interaction mechanism between intelligent agents.

The invention relates to the technical field of computer-aided drug design, in particular to a drug molecule screening and optimizing method based on artificial intelligence prediction, which comprises the following steps: S1, obtaining a dynamic protein conformation set and molecular multi-dimensional characterization: obtaining a dynamic conformation set of a target protein and a physicochemical property spatial distribution diagram of a binding pocket of the dynamic conformation set, a two-dimensional molecular map topological structure and three-dimensional conformation coordinates of the drug molecules are obtained; s2, multi-modal fusion prediction is carried out; s3, generating interpretable optimization guidance; and S4, automatic iterative optimization: performing batch prediction and screening on the new candidate molecular structure, taking the screened optimal molecule as a new starting point, repeatedly executing the interpretability optimization guidance generation step and the step until an iteration termination condition is met, and outputting a final optimized molecule list. Through the multi-modal fusion deep learning model, the interaction strength of the drug molecules and the target protein can be quickly and accurately predicted, and the screening efficiency of the drug molecules is greatly improved.

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.

A system and method are provided for a machine-learning drive framework for predicting ionic conductivity of solid-state electrolytes. In use, the method and / or system may include receiving, at a machine learning system, two or more molecular structures from at least one structural dataset, where the two or more molecular structures relate to ionic mobility. Additionally, atomic weights are calculated for the two or more molecular structures, and the machine learning system is trained based on the two or more molecular structures, where the training relies on at least one intrinsic atomic feature and the calculated atomic weights for the two or more molecular structures. Further, a bias-correction is applied for the two or more molecular structures to improve the training of the machine learning system. Further, one or more molecular dynamics (MD) simulations are outputted, using the machine learning system, for the two or more molecular structures.

The invention discloses a molecular property prediction method and device based on a graph neural network and a large language model, and the method comprises the steps: obtaining a molecular structure graph according to the SMILES of a molecule, and the molecular structure graph comprises the structural information of atoms and functional groups; generating a description text of each functional group by using the fine-tuned large language model, and encoding the description text by using a text encoder to obtain text information of each functional group; adding text nodes and feature information in the molecular structure diagram, and adding edges between the text nodes and corresponding functional groups to obtain a final edition molecular structure diagram; and inputting the final edition molecular structure diagram into the trained molecular property predictor to obtain a prediction result of the molecular property. According to the method, structural information and text information of atoms and functional groups in molecules are fully considered, and then the information is learned by using the message passing neural network, so that molecular property prediction is more accurate, and the accuracy of molecular property prediction is improved.

A system and method for modeling one or more characteristics of a molecule using a quantum device implemented on quantum hardware is disclosed. Through control, via modulation, of coupling strengths between qubits on the quantum device and a comparison to an experimentally-measured nuclear magnetic resonance spectrum, J-coupling values may be deduced. These J-coupling values may then be used to recover the molecular structure of the given molecule. The control of coupling strengths may take place through an iterative process until convergence to the one or more characteristics is reached. In some embodiments, such a process may be implemented as part of a quantum computing service. In such cases, the quantum hardware may be local to the quantum computing service, or may be outsourced to a quantum hardware provider.

The invention discloses a spectrum and molecular structure comparison matching system based on machine learning, and relates to the field of spectrum intelligent analysis, and the system comprises a model construction module, a training optimization module, a calculation candidate module, and an interpretation visualization module. According to the method, the deep semantic features of the spectrum and the structure are automatically extracted by constructing the multi-modal deep learning model, and the accuracy and robustness of spectrum analysis and molecular structure matching are greatly improved. A shared embedding space learning strategy is adopted, different modes are projected to the same feature space, and unified matching and similarity scoring are achieved. An attention mechanism is introduced, visual mapping between spectrum peaks and molecular structure fragments is achieved, understanding of corresponding chemical groups or atoms behind each spectrum peak is facilitated, and interpretability of a matching result is enhanced.

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 alloy high-temperature and high-pressure environment simulation method under radiation action based on molecular dynamics, which comprises the following steps: establishing a molecular structure framework of an alloy according to a crystal structure and a lattice constant of the alloy, and setting initial parameters to construct an initial simulation model; performing relaxation operation on the molecular structure model in a high-temperature and high-pressure environment; performing irradiation damage simulation on the relaxed model based on molecular dynamics, selecting a central atom as a primary off-site atom, and setting incident energy and incident direction of the primary off-site atom to simulate atom cascade collision; keeping the incident direction unchanged, and adjusting the incident energy of the primary off-site atoms to obtain defect distribution and defect density under different energies; and analyzing the defect evolution law of the alloy in the high-temperature and high-pressure environment according to the simulation result. According to the method, the alloy material is observed from a microscopic atomic scale, so that a powerful support is provided for simulating a defect evolution process in a high-temperature and high-pressure environment and reflecting atomic collision of the alloy in a real radiation environment.

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.

Systems and methods for determining a molecular structure include obtaining mass spectrum data for an unknown parent ion. The mass spectrum data is recorded at each collision energy of a series of collision energies and includes an abundance of one or more substructures of the unknown parent ion. The molecular structure of the unknown parent ion is determined by composing the mass spectrum data into a fragmentation profile and embedding the fragmentation profile into a reduced dimension profile. The reduced dimension profile is mapped into a fragmentation space. One or more proximate molecules in the fragmentation space are identified using the mapping and a candidate for the parent ion is identified using the one or more proximate molecules.

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

The embodiment of the invention provides a drug structure-activity relationship analysis method and device based on a multi-level structure, and the method comprises the steps: carrying out the multi-level molecular structure characterization of a compound, the multi-level molecular structure comprises a clustering structure, an original structure and a fragment structure; calculating the weights of the clustering structure, the original structure and the fragment structure based on the research and development state of the compound; and determining an activity relationship between the compound and the target, and calculating a target association credibility score based on the activity relationship, the evidence supporting the activity relationship and the research and development state weight of the compound.

The invention discloses a method and device for predicting the transmembrane capability of a substance molecule, terminal equipment and a storage medium, and the method comprises the steps: obtaining a target molecular substance, and determining a molecular structure diagram corresponding to the target molecular substance; according to a pre-trained prediction model, determining a permeation free energy prediction value of the target molecular substance; according to the prediction model, multilevel feature extraction is carried out on a molecular structure diagram of sample substance molecules by utilizing a graph neural network model, contribution values of atoms in the sample substance molecules to transmembrane behaviors are evaluated through a gradient weighted class activation mapping method, and permeation free energy prediction values corresponding to the sample substance molecules are output. The graph neural network is trained to obtain the prediction model, and the prediction model is adopted to predict the transmembrane capability of the target molecular substance, so that intelligent screening of the transmembrane capability of a large-scale drug molecular library can be realized, the prediction efficiency and flux are remarkably improved, and intelligent prediction of the transmembrane free energy of substance small molecules is realized.

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 an ultra-low interfacial tension surfactant screening method and system based on AI4S, and relates to the technical field of oil and gas exploitation. The method comprises the following steps: constructing a database covering molecular structures of typical surfactants; constructing a prediction model for predicting the surface tension; generating candidate molecules by using a generative artificial intelligence model comprising a variational auto-encoder and a generative adversarial network; inputting the candidate molecules and the experimental conditions into the trained prediction model to predict the interfacial tension of the prediction model under the target condition, and screening the candidate molecules with the predicted performance reaching the standard; and performing experimental determination on the screened candidate molecules until molecules with target performance are obtained, and feeding back experimental results to a database to form closed-loop optimization of data, prediction and experiments. The research and development efficiency and success rate of the chemical oil-displacing agent can be remarkably improved, the research and development period is shortened, the research and development cost is reduced, and effective technical support is provided for improving the oil and gas recovery efficiency and guaranteeing energy safety.

The invention discloses a biomass pyrolytic reaction network prediction method based on a graph theory and quantum chemistry, which comprises the following steps: analyzing the molecular structure of a biomass initial reactant, abstracting the molecular structure into a directed graph molecular model, marking a potential conversion path after molecular mechanics optimization, generating a unique identifier by using an atom numbering rule and a Hash algorithm, and predicting the biomass pyrolytic reaction network. Functional groups are extracted and packaged into sub-graph modules, chemical bond parameters are obtained in combination with quantum chemistry calculation and are converted into fracture probabilities, basic reaction building blocks are predefined to traverse and mark breakable bonds, fragments are recombined and then de-weighted through Hash, and then reaction activation energy weights are given to side chemical bond fracture reactions; an energy optimal path is screened by means of a priority queue search algorithm, accurate analysis of a chemical bond fracture path and intermediate product conversion in the biomass pyrolysis reaction network is finally achieved, reaction path and product distribution are efficiently predicted, and the calculation efficiency and precision of a complex system are balanced.

The training method of the molecular characterization information generation model comprises the following steps: acquiring first molecular information of a target molecule; performing diffusion processing to obtain second molecular information; determining real trajectory information of the target molecule; taking the second molecular information as input of a target neural network model, and outputting molecular characterization information; according to the molecular characterization information, track information is really simulated; determining a first loss value according to the difference between the real trajectory information and the simulated trajectory information; determining first comparison information and second comparison information according to the molecular characterization information; performing comparative learning by taking the first comparison information as a positive sample and the second comparison information as a negative sample, and determining a second loss value; and performing iterative training according to the first loss value and the second loss value to obtain a molecular characterization information generation model. In the training process, the comparison change of the molecular motion trail and the molecular structures at different moments is considered, so that the molecular characterization information has rich molecular information and can be used for various downstream tasks.

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.

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.

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.

A method for a structural analysis of a sample molecule using a mass spectrometer includes: a process for obtaining a standard analysis condition for each of setting items for performing a molecular-related ion measurement, where the setting items include an ion amount setting item, a mass-to-charge-ratio range setting item, and a signal intensity setting item; a process for performing a product ion measurement under an altered analysis condition, to acquire mass spectrum data of the product ion measurement, where the altered analysis condition is prepared by changing at least one of analysis conditions of the ion amount setting item, the mass-to-charge-ratio range setting item and the signal intensity setting item in the standard analysis condition; a process for extracting peaks corresponding to the fragment ions; and a process for determining at least a portion of the structure of the sample molecule based on mass information of the extracted peaks.

The invention relates to the technical field of artificial intelligence and drug design, and provides a three-dimensional drug molecule generation method and system based on a Bayesian flow network, and the method comprises the following steps: inputting real initial molecule data into a drug molecule generation model combining the Bayesian flow network and a diffusion model; wherein noise molecular features are generated according to prior statistical knowledge; in combination with the noise molecular characteristics and random Gaussian noise obtained from a noise schedule, introducing noise into current molecular data to generate noisy molecular data; meanwhile, updating data distribution in the prior statistical knowledge based on Bayesian inference; denoising the noisy molecule data to generate predicted drug molecules; and repeating the steps until the molecular structure converges or reaches a preset step number T to obtain a drug molecule generation result.

A method and apparatus for the characterization of matter is described. Light from or modulated by the matter is analyzed by means of the spectral correlation. The spectral correlation can report on the composition, static and temporally dynamic characteristics of the matter. The amplitude and temporal characteristics of the spectral correlation are measurement features that report on properties of matter, or changes in local molecular forces, chemical composition, molecular structure, shape, size, charging state, mass, and more. The advantage of spectral correlation of scattered light lies in the increased time-resolution and minimized noise in the spectroscopic characterization of matter, in particular in the characterization of fluctuations. In one embodiment, an apparatus may combine an optical system to illuminate the matter, collect scattered photons, direct these photons into an optical interferometer, and the time-resolved detection of the photons. By performing temporal intensity correlation of the light after the interferometer the spectral correlation is obtained.

The invention discloses a method for predicting the irradiation chemical damage degree of a molecular crystal, and the method comprises the steps: carrying out the data preparation, building a training data set, carrying out the preprocessing of the data, constructing a prediction model of the irradiation chemical damage of the molecular crystal through XGBoost after the preprocessing, and carrying out the prediction of the irradiation chemical damage degree of the molecular crystal through the XGBoost. And calculating the contribution degree of each feature to model prediction according to XGBoost, and calculating a feature interaction effect on the contribution of each feature by using an SHAP method to obtain irradiation sensitive molecular group data. The method has the advantages that limitation of single energy scale of a traditional empirical model is broken through, key damage sensitive factors such as molecular groups and bond dissociation energy can be revealed through cross-scale feature fusion and model interpretability analysis, irradiation damage prediction is promoted to be transformed from an empirical formula to an intelligent calculation normal form by the technology, and the method is suitable for large-scale popularization and application. And an interdisciplinary solution is provided for life evaluation of space electronic devices, nuclear reactor material design, energetic molecular structure optimization and the like.

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.

A method for predicting a molecular structure includes: preparing a learning data set including first learning data including an eigenvector value and a quantum mechanics calculation value for a monoatomic and molecular structural model, a bulk structural model, a slab structural model, and a nanoparticle structural model of a material including a plurality of elements; learning an artificial neural network using the learning data set to obtain a potential value; and predicting a molecular structure of another material by using the potential value.

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.

This application provides a method and apparatus for training a molecular multiconformation prediction model. The method includes: acquiring training data, which includes molecular structure representation samples and molecular conformation samples; processing the molecular structure representation samples and molecular conformation samples using the molecular multiconformation prediction model to be trained to obtain a training output; constructing a loss function value based on the training output and the molecular conformation samples; and, if the loss function value or the number of training rounds is within a preset range, using the trained molecular multiconformation prediction model as the molecular multiconformation prediction model. This application can achieve fast and accurate prediction of multiple three-dimensional conformations of molecules without analyzing their potential energy surfaces, reducing the computational resources and time consumed in resolving multiple three-dimensional conformations of molecules.