1526results about "Molecular design" patented technology

A federated distributed computational system enables secure oncological therapy optimization through robotic integration. The system establishes a distributed graph architecture with secure communication channels connecting computational nodes, implementing encryption protocols for cross-institutional data exchange. Each node contains processing capabilities for fluorescence-guided imaging, uncertainty quantification, and expert knowledge integration while maintaining hierarchical knowledge graphs of oncological biomarkers, interventions, and outcomes. The system coordinates domain-specific knowledge through token-space communication and implements an advanced robotic integration system for surgical interventions using spatiotemporal tumor mapping, multi-modal fluorescence imaging, surgical robot coordination, and space-time stabilized mesh management. Key capabilities include wavelength-specific multi-modal fluorescence detection, combined epistemic and aleatoric uncertainty estimation, tensor-based data integration with adaptive dimensionality control, and light cone search for adaptive treatment optimization—all while maintaining strict privacy controls.

A federated distributed computational system enables secure oncological therapy optimization through multi-expert integration and advanced uncertainty quantification. The system implements a multi-expert integration framework that coordinates domain-specific knowledge through token-space communication for precision oncological treatment, while maintaining secure cross-institutional data exchange. The architecture coordinates multi-scale spatiotemporal synchronization across computational nodes, with each node containing local processing capabilities for fluorescence-guided imaging, uncertainty quantification, and expert knowledge integration. Through a distributed graph architecture, the system enables advanced fluorescence imaging with wavelength-specific targeting, multi-level uncertainty estimation combining epistemic and aleatoric approaches, and multi-scale tensor-based integration with adaptive dimensionality control. The system implements light cone search and planning for adaptive treatment strategy optimization, enabling medical institutions and research organizations to collaborate on complex oncological therapy projects while maintaining strict data privacy controls.

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 discloses a drug resistance prediction method and system based on comparative learning and multi-modal fusion, and the method comprises the steps: firstly generating a molecular map and a molecular fingerprint based on the SMILES of a target drug, and extracting the molecular features of the drug through a comparative learning model constructed through combining a map attention network and a map convolution network; and then, acquiring protein expression, gene expression and metabolic expression data from the target tissue cells, extracting modal features through a deep convolutional network, a Transform encoder and a multi-dimensional attention network, and realizing adaptive fusion of the multi-modal features through a heterogeneous interactive attention mechanism. And finally, jointly inputting the fused multi-modal features and drug molecular features into a multi-layer sensor to realize high-precision prediction of the drug resistance of cells to drugs. By introducing a contrast learning and multi-modal feature fusion mechanism, the characterization capability and prediction precision of the model are effectively improved, and efficient and reliable support can be provided for drug screening and clinical decision making.

The invention discloses an unknown pollutant non-target identification method and system based on mass spectrum and a generative model, and is applied to the technical field of environmental monitoring and analytical chemistry. The method comprises the following steps: collecting mass spectrum data of a water body sample to be detected; preprocessing the original mass spectrum data, and outputting standardized features; inputting the standardized features into a pre-trained generative model to generate a plurality of candidate molecular formulas; using chemical and physical constraints to eliminate candidate molecular formulas which do not conform to rules; executing a rule-driven algorithm to obtain candidate pollutant molecular structures; carrying out comprehensive scoring and sorting on candidate pollutant molecular structures through chemical prior and environmental prior; and semi-quantitative or relatively quantitative concentration determination is carried out. According to the method, a rule-driven expert system and a data-driven generation model are combined, unknown pollutants which do not exist in a standard library can be effectively recognized and analyzed, and full-process automatic processing from original mass spectrum data to pollutant structures and concentrations is achieved.

A system is disclosed for encoding a data string of a first modality into a hierarchical tokenized representation for processing by a text-based deep neural network (DNN) trained on a second modality. The data string comprises multiple units, each having one or more attributes. Each attribute is represented in the tokenized string as a sequence of hierarchical tokens, with a first hierarchical token encoding one or more most significant bits and a subsequent hierarchical token encoding one or more less significant bits. The DNN processes the data string bidirectionally, across the sequence of units and within the token hierarchy, to select tokens that capture attribute information. The selected hierarchical tokens output by the DNN from a representation of the original data string that preserves attribute detail while enabling cross-modal processing using models trained on text.

The invention discloses a drug-drug interaction prediction method based on a drug flow sub-graph, and relates to the technical field of bioinformatics, and the method comprises the steps: data preparation: collecting a reference data set including drug-drug interaction, and introducing an external knowledge graph for adaptability preprocessing; constructing a model: constructing a drug-drug interaction prediction model, and training by using the preprocessed reference data set; and effect prediction: inputting a target drug into the drug-drug interaction prediction model, and outputting a drug-drug interaction prediction result through the drug-drug interaction prediction model. The structure and semantic information of the drug flow sub-graph are fully utilized, accurate prediction of drug interaction is realized, and the prediction efficiency is improved. And the interpretability of the drug-drug interaction prediction model is improved.

A federated distributed computational system enables secure drug discovery and resistance tracking through hybrid simulation capabilities. The system implements a hybrid simulation orchestrator that coordinates molecular dynamics simulations with machine learning models for drug discovery analysis, while maintaining secure cross-institutional data exchange. The architecture coordinates multi-scale spatiotemporal synchronization across computational nodes, with each node containing local processing capabilities for molecular dynamics simulation and resistance pattern detection. Through a distributed graph architecture, the system enables real-world clinical data integration, resistance evolution tracking, and multi-scale tensor-based analysis with adaptive dimensionality control. The system implements real-time drug response prediction through multi-modal data analysis, enabling pharmaceutical companies and research institutions to collaborate on complex drug discovery projects while maintaining strict data privacy controls.

The invention discloses a reaction site prediction method and device based on chemical and physical prior driving, and the method comprises the steps: extracting set features through the multi-modal input of a fusion molecular map, an SMILES sequence and a three-dimensional conformation; generating atomic embedding by using a message passing neural network, and calculating a mixed feature fusing a topological path and a three-dimensional distance; combining the key type weight to construct a graph position code of chemical environment correction; injecting the mixed distance and the charge difference into a Transform attention mechanism, and explicitly modeling an inter-atomic long-range electron effect; a model is jointly trained through double tasks of comparative learning and mask prediction, the comparative learning adopts a directional negative sample to enhance generalization, and mask prediction synchronously recovers an atom type and a charge transfer matrix; and finally, injecting quantum chemistry priori constraint attention weights such as a Fuzzy well function, outputting an atomic-scale reaction activity probability, generating a thermodynamic diagram, and realizing high-precision and interpretable active site labeling. According to the method, the drug design and reaction mechanism analysis efficiency can be remarkably 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.

The invention provides a drug-target interaction prediction method and device based on a dynamic heterogeneous double-flow graph neural network, and belongs to the field of drug research and development. The method solves the problems of low prediction accuracy and weak generalization ability caused by insufficient graph structure construction and feature expression in the prior art, and comprises the following steps: constructing a graph structure according to feature data of drugs and targets, and dynamically adjusting weights of the graph structure and edges according to data change to obtain a dynamic heterogeneous graph; the double-flow graph neural network is utilized to process feature information of the medicine and the target spot at the same time, and a complex mode of medicine-target spot interaction is effectively captured; in combination with long-range dependency modeling and random walk feature learning, on the basis of heterogeneous graph convolutional network learning, processing a remote dependency relationship between a drug and a target spot, and capturing multi-hop information in an isomorphic network by using random walk to further optimize interaction prediction; designing a decoder based on matrix completion; training and optimizing the model; the method is applied to drug-target interaction prediction.

The invention provides a whole-process intelligent management system for stem cell culture medium development, and belongs to the technical field of intelligent management. According to the method, artificial intelligence, big data analysis and knowledge graph technologies are fused, and the whole process from demand analysis, formula intelligent design and virtual simulation, small-scale culture verification and iterative optimization, pilot scale-up process optimization to quality compliance and tracing is covered. All the modules are coordinated through the central intelligent decision engine, the research and development efficiency of the culture medium can be remarkably improved, the development cost can be greatly reduced, and batch consistency and quality stability of the culture medium are ensured by reducing trial and error and optimizing in real time. Meanwhile, a full-chain electronic tracing function is provided, the requirements of laws and regulations for compliance are comprehensively met, and the problems that traditional research and development are low in efficiency, high in cost, unstable in batch and the like are effectively solved.

The invention discloses an intelligent catalyst design method and system based on multi-objective optimization and deep learning, and aims to solve the problems of limitation of single-objective optimization, high computing resource consumption and the like in traditional catalyst design. According to the method, graph data are constructed by fusing atomic-scale and macroscopic features, multi-target prediction is carried out by utilizing a multi-task graph neural network (GNN), and collaborative optimization of adsorption energy and stability loss is realized by combining an NSGA-II multi-target optimization algorithm and a dynamic weight adjustment strategy of Bayesian optimization. A closed loop is verified through active learning and virtual experiments, candidate materials are dynamically selected, the model is updated, and the efficiency and precision of material design are remarkably improved. The method is suitable for rapid discovery and optimization of the high-performance catalyst, and has a wide application prospect.

The invention discloses a generative odor real-time synthesis method and system based on cross-modal potential space mapping, and belongs to the technical field of artificial intelligence and olfaction calculation. The method comprises the following steps: acquiring a multi-modal input stream of a current scene, and extracting an emotion semantic feature vector by using a deep neural network; mapping the semantic features into target odor chemical feature vectors by using nonlinear projection through a pre-constructed vision-smell joint embedding space; constructing a convex optimization model based on olfactory perception, and calculating a basic liquid optimal mixing proportionality coefficient matrix capable of fitting the target vector; the matrix is converted into a micro-fluidic driving signal, and the target smell is synthesized in situ in the micro-fluidic chip. The invention further discloses a self-adaptive cleaning logic and olfactory fatigue compensation mechanism based on scene mutation detection. The method solves the problems that in the prior art, label matching is dependent, new smell cannot be synthesized, and dynamic transition is lacked, and olfactory replicating and real-time generation of abstract semantic scenes are achieved.

Embodiments relate to system and methods involving use of a technique for managing a database for producing a material composition having a thermodynamic phase. The technique can include: receiving a binary phase diagram for each material to be used as a component of a high-entropy alloy (HEA); using one or more active learning machine learning techniques for generating a feature, the feature including: a primary feature that is representative of a probability that an HEA will exhibit a solid solution phase and / or an intermetallic phase, and a physics-based feature that is representative of a factor related to formation of a desired intermetallic HEA phase; encoding the primary feature and the physics-based feature; generating an output representation of a HEA alloy composition and phase of a predicted materials composition; and selecting a HEA composition and phase that will meet a material design criterion.

The invention discloses an antibacterial cyclic peptide screening system and method based on a multi-modal cross attention mechanism, and belongs to the field of antibacterial cyclic peptide screening, the antibacterial cyclic peptide screening system comprises the antibacterial cyclic peptide screening system of the multi-modal cross attention mechanism, the antibacterial cyclic peptide screening system of the multi-modal cross attention mechanism comprises a multi-modal cross attention screening model and a multi-modal cross attention screening model, the method is used for screening antibacterial cyclic peptides; data of the data input module comprises a one-dimensional sequence, a two-dimensional structure and a three-dimensional structure, data sources in the data input module comprise but are not limited to APD3, DRAMP and a Un i Prot public database, and the data input module is used for conveying data of the antibacterial cyclic peptide; through cooperative use of the devices, the cross attention fusion module is arranged, and weights of different modals are dynamically allocated, so that a key active region of the antibacterial cyclic peptide is more obvious, and a multi-modal cross attention screening model can more accurately identify related characteristics of the antibacterial activity of the antibacterial cyclic peptide; the screening of the antibacterial cyclic peptide is more accurate.

The invention relates to the field of artificial intelligence assisted drug discovery, in particular to a protein large language model-based molecule generation and optimization method, which comprises the following steps of: acquiring amino acid sequence information and three-dimensional structure information of a target protein pocket; encoding the amino acid sequence of the protein pocket by using a protein encoder constructed based on a protein large language model to obtain a protein pocket feature vector; using a context encoder module to encode the context information according to a preset molecule generation mode (de novo generation or optimization based on a seed compound) to obtain a latent vector; and fusing the protein pocket feature vector with the latent vector. According to the method, accurate representation of the protein pocket is realized by utilizing the protein large language model, and a generation-screening-optimization iterative drug design strategy is developed by supporting a unified framework of two generation modes, so that the targeting specificity of generated molecules and the overall efficiency of drug design are improved.

The invention discloses a bidirectional reversible conversion method and system between a peptide molecule SMILES and a sequence expression. The core innovation lies in that a new sequence description syntax is defined to retain information of a polypeptide special bond and specific modification of amino acid; a main chain atom index and adjacency traversal topology identification algorithm is adopted, and end group and topology integrated detection and coding are carried out; a residue recognition algorithm for main chain cutting and template library matching is compatible with any standard or non-standard amino acid residues, an extensible end group library / monomer template library and an automatic increment mechanism, and automatic recognition and sequence annotation of S-S disulfide bonds; the invention relates to a high-fidelity assembly algorithm of HELM anchor points and topology aware cyclic peptide processing. The method solves the problems of incapability of supporting a complex polypeptide topological structure, poor reversibility, insufficient expansibility of a monomer library and the like in the prior art, can be widely applied to scenes of quantitative structure-activity relationship model construction, large-scale polypeptide data cleaning and the like, and has remarkable practicability and innovativeness.

A drug design method based on an autoregressive model is provided, which relates to the field of drug design technologies. The method includes: applying a sub-word tokenization algorithm to biological text processing, training protein and ligand information in data sets to obtain a protein tokenizer and a ligand tokenizer, and constructing a tokenizer of the autoregressive model; processing and transforming original data in the data sets into a text form, and encoding by the tokenizer to construct a training data set for the autoregressive model; training the autoregressive model by the training data set, so that the autoregressive model can understand SMILES representations of ligands and learn an interaction mode between proteins and ligands; generating predicted ligands by using the trained autoregressive model, and post-processing through a chemical information tool to acquire candidate ligands with specific chemical structures; and evaluating and optimizing the candidate ligands to determine target candidate molecules.

A method is disclosed for using an artificial intelligence engine to generate candidate drug compounds, wherein the method comprises: generating candidate drug compounds comprising sequences via a creator module of the artificial intelligence engine. The method includes generating, via a descriptor module, a respective description for each of the candidate drug compounds at nodes in a knowledge graph, wherein the knowledge graph comprises a multi-dimensional representation of the candidate drug compounds and the respective description comprises drug compound structural information, drug compound activity information, and drug compound semantic information. The method includes determining a shape of the multi-dimensional representation of the candidate drug compounds; determining, based on the shape, a slice configured to be obtained from the representation; determining, using a decoder, which dimensions are included in the slice; and based on the dimensions, determining an effectiveness of a biomedical feature of the slice.

The invention relates to the technical field of drug research and development, in particular to a target AKT1 drug molecule discovery and delivery integrated system and method based on deep learning. Aiming at the problems of molecular design, optimization and delivery link separation and low research and development efficiency of drugs in the prior art, the system constructs a multi-module collaborative framework, and comprises a target analysis module for analyzing a target structure and formulating a generation strategy; the molecule generation and optimization module is used for generating and optimizing candidate molecules in combination with the generation model and reinforcement learning; the delivery scheme design module is used for matching a delivery carrier based on molecular physicochemical properties; and a verification module that predicts and evaluates the molecule-deliverer combination using molecular docking and ADMET. An evaluation result of the verification module is fed back to the molecule generation and optimization module to form a closed-loop optimization mechanism, so that an automatic process from target analysis to output of candidate drug molecules and matched delivery schemes thereof is realized. Compared with the prior art, the efficiency and success rate of early drug discovery can be improved.

The invention relates to a modeling method in the field of molecular dynamics, in particular to an automatic polymer full-atom molecular dynamics modeling method, and the polymer modeling process is divided into three core steps: firstly, converting SMILES of a repetitive unit into SMILES of a polymer; secondly, constructing a single-chain structure; and finally, constructing a multi-chain amorphous system. The link of cross-linking reaction needs to be additionally added to the cross-linked polymer. According to the invention, through the built-in DREIDING force field parameters, an input file required by the Lammps molecular dynamics can be directly operated and generated.

The invention belongs to the field of liquid crystal polyarylester synthesis, and particularly relates to a liquid crystal polyarylester synthesis catalyst screening method, which comprises: (a) respectively carrying out molecular modeling and structure optimization on a candidate catalyst and a reactant in a liquid crystal polyarylester synthesis reaction; (b) carrying out electronic structure analysis through the optimized structure, determining the number of active sites of the candidate catalyst, and preliminarily predicting the activity of the catalyst; (c) respectively constructing a micro-scale catalyst-reactant interaction model and a mesoscale catalyst-reactant interaction model by the catalyst structure and the reaction active site, and calculating interaction energy under the two scales; and (d) based on the interaction energy result of the multi-scale simulation, screening out a candidate catalyst with the interaction energy absolute value greater than or equal to a set threshold value as a high-activity catalyst. According to the screening method, the experimental screening steps of the liquid crystal polyarylester synthesis catalyst are simplified, and the raw material cost and the time cost are greatly saved.

The invention discloses a circular RNA drug sensitivity correlation identification method based on integrated multi-instance learning. The circular RNA drug sensitivity correlation identification method comprises the following steps: collecting experimental verification circular RNA and drug sensitivity correlation data; establishing a feature representation model based on a heterogeneous network and vertexes; embedding a heterogeneous graph node into the model, and extracting deep feature representation of the node; designing a meta-path instance embedding projector, and generating a plurality of meta-path instances; a circular RNA and drug sensitivity association predictor is constructed and completed; constructing an integrated heterogeneous graph network deep learning model; and outputting the meta-path instance of the circular RNA and drug pair and the attention coefficient, and carrying out interpretable analysis. According to the method, integrated learning and a deep learning model are combined, so that the reliability of the model is improved; according to the invention, interpretable analysis is carried out by utilizing the meta-path and the attention coefficient, the potential action mechanism of the circular RNA associated with the drug sensitivity can be explained, and the guidance of medical research is facilitated.

Multiple molecular designs may be generated by computations. One or more attribute calculation models may be applied to determine a plurality of attributes for each molecular design. Each attribute calculation model may be trained to approximate a probability distribution of possible values for the corresponding attribute. A cumulative distribution function indicator corresponding to an expected multivariate rank for each molecular design may be determined based on an output of the attribute calculation model. The multivariate rank of a molecular design may quantify the probability that none of its attributes may be improved without degrading at least one other attribute. One or more molecular designs may be selected as candidates for wet laboratory assessment based on the cumulative distribution function indicator for each molecular design. The molecular design selected for wet laboratory assessment may exhibit incrementally better properties than designs in previous design iterations.

The invention discloses a microemulsion interfacial tension efficient prediction method and system based on active learning and molecular dynamics. According to the method, 217 molecular descriptors corresponding to each molecular structure are calculated by adopting an RDKit software package, and the descriptors are used for representing molecular structure characteristics and serve as input variables of a machine learning model, so that key structure information including molecular branching degree, polarity and the like is transmitted. For an oil-water-surfactant ternary interface system, the oil-water interfacial tension in the presence of a surfactant is simulated and calculated through molecular dynamics, and an IFT value is set as a model prediction target. An active learning mechanism is introduced, and iterative sample labeling in the molecular dynamics simulation process is guided; and integrating the obtained IFT data with the molecular descriptor features, constructing a machine learning data set, and training a random forest model. According to the method, the problem of screening a high-performance surfactant layer by a middle-phase microemulsion system can be solved, and the ultra-low oil-water interfacial tension can be rapidly and efficiently screened.

The invention discloses application of an AI-driven target screening technology in anti-obesity treatment of a drug delivery system, and belongs to the field of cross fusion of biological medicine and artificial intelligence. The screening method provided by the invention comprises the following steps: selecting a signal channel target and active small molecules related to lipid metabolism; processing the signal channel target and the active small molecules through the data set to obtain an input matrix; and inputting the input matrix into a deep learning model to obtain the interaction intensity between the active small molecules and the signal path target. According to the method, an artificial intelligence technology is introduced, a deep learning model is constructed to efficiently predict the affinity between a drug and a protein target, the problems that a traditional target screening method depends on experience and is low in efficiency are solved, and target expression is subjected to experimental verification through a molecular biology method, so that the target screening efficiency is improved. A verification closed loop from calculation prediction to molecular demonstration is realized, and the biological credibility of a target screening result is proved.

The present invention relates to a method for calculating an electronic structure of a material by using quantum computing. Particularly, the method of the present invention for calculating an electronic structure of a material performed linking a quantum computer and a classical computer, may comprise the steps of: fragmenting a target molecule into fragments of a plurality of monomers having a predetermined positional relation; performing a first VQE routine for performing a Hamiltonian matrix calculation for the plurality of monomers on the basis of a calculation of electron density for the plurality of monomers, inputting modified electron density, and repeating the first VQE routine performing the Hamiltonian matrix calculation until the modified electron density according to the Hamiltonian matrix by the result of the first VQE routine converges; and performing a second VQE routine for performing a Hamiltonian matrix calculation on one or more dimers consisting of two monomer pairs of the plurality of monomers.

Systems and methods for characterizing an interaction between a compound and a polymer include obtaining a plurality of sets of atomic coordinates. Each set of atomic coordinates comprises the compound bound to the polymer in a corresponding pose in a plurality of poses. Each respective set of atomic coordinates, or an encoding thereof, is sequentially inputted into a neural network, to obtain a corresponding initial embedding as output, thereby obtaining a plurality of initial embeddings. Each initial embedding corresponds to a set of atomic coordinates in the plurality of sets of atomic coordinates. An attention mechanism is applied to the plurality of initial embeddings, in concatenated form, to obtain an attention embedding. A pooling function is applied to the attention embedding to derive a pooled embedding. The pooled embedding is inputted into a model to obtain an interaction score of the interaction between the compound and the polymer.