1027results about "Cheminformatics programming languages" 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 relates to a multi-agent-based material performance prediction and synthesis system, and the system comprises a multi-agent data enhancement module which is configured to be used for firstly disassembling a complex problem into a plurality of subtasks, and then constructing a fine tuning data set comprising Sub-CoQ question and answer pairs by starting multi-source parallel retrieval; the multi-expert debate module is configured to be used for simulating decision conflicts of different roles in material engineering and generating a direct preference optimization DPO data set through debate; the training and verification module is configured to be used for training and verifying a large model MatMind in the field of materials by utilizing supervised fine tuning SFT and reinforcement learning RLHF based on the fine tuning data set and the DPO data set; and the material performance prediction and synthesis module is configured to be used for realizing intelligent recommendation of a material performance prediction and synthesis process by importing input parameters into the large model MatMind.

The invention relates to the technical field of data integration, and particularly discloses a perfluoro / polyfluoroalkyl substance multi-source exposure data integration risk assessment system, which is characterized in that environmental exposure data of perfluoro / polyfluoroalkyl substances is acquired through a multi-source environmental sensor array, and a PFAS multi-mode exposure feature database is established; carrying out pollution source isotope fingerprint analysis, and obtaining source contribution rate distribution maps of three pollution sources of industrial emission, surface runoff and atmospheric settlement through a nonlinear source analysis algorithm; constructing a three-dimensional geographic information dynamic migration model according to the source contribution rate distribution map, and generating a multi-medium dynamic migration flux matrix; a composite risk assessment model is established based on the multi-medium dynamic migration flux matrix, probability risk assessment is executed in combination with an ecological toxicity threshold database, and a space gridding risk grade map is output; the method not only fills the blank of the prior art in the aspects of multi-medium dynamic modeling and nonlinear source analysis, but also provides powerful technical support for environmental pollution control and ecological risk prevention and control.

The invention relates to the technical field of ozone precursor traceability, in particular to an ozone precursor collaborative traceability method and system based on a knowledge graph, and the method comprises the following steps: obtaining precursor concentration change, recognizing an abnormal path, extracting path characteristics, carrying out standardized scoring, adjusting graph connection strength, and analyzing sequence offset to obtain collaborative nodes. And screening origin nodes in combination with time sequence meteorology to generate an origin node list. According to the method, pollution response channels are identified through precursor node concentration changes and path connection relations, focusing of key paths is enhanced, paths are scored based on multi-dimensional indexes, connection attributes are dynamically adjusted in combination with monitoring period ozone response intensity, map structure updating is achieved, and concentration response sequence offset is analyzed; according to the method, nodes with co-evolution characteristics are screened, the stable relation identification capability is improved, the path reasonability is evaluated by combining a release time sequence and meteorological conditions, the initial source positioning accuracy is improved, and the response speed and the identification precision of precursor tracing are integrally enhanced.

The invention discloses an environment-adaptive transformer oil sample Raman spectrum detection method and related equipment, and relates to the field of optical sensing systems. The method comprises the following steps: collecting oil sample Raman spectrums and environmental parameters in multiple operation scenes, and constructing a multi-scene spectrum characteristic model and a standard fingerprint database; pre-processing and denoising parameters are adaptively set based on the environmental perception vector, and baseline correction and joint denoising are carried out on the original spectrum; scene discrimination is carried out by fusing the characteristics of peak position, peak height, peak width, integral area and the like, a scene-related component standard spectrum dictionary is generated, and the concentration and confidence of each target component are obtained by adopting constrained spectral line unmixing and quantitative calibration; and driving the fingerprint database and the model to update in combination with quality control indexes such as spectral shape relevancy and residual errors and a drift detection result. The system is composed of a Raman spectrum acquisition module, an environment monitoring module and a data processing module, and can improve the robustness and quantitative precision of Raman detection of transformer oil in a complex environment.

A method accessing a pre-trained specific material database associating each of a plurality of materials with a corresponding material profile, each material profile including one or more parameters including at least one of a transmit frequency and a response frequency; receiving a selection of a target material from a user; identifying first material profile associated with the target material using the pre-trained specific material database; transmitting, via an RF detection device, an RF signal into the target material using the one or more parameters for the target material associated with the first material profile; receiving, via the RF detection device, a response signal from the target material; analyzing the response signal using an AI algorithm to determine whether resonance characteristics of the response signal indicate a presence of the target material; and notifying the user if the presence of the target material is indicated by the resonance characteristics.

The invention belongs to the technical field of composite material design, and discloses a polyimide-based composite material design method and system based on experiment-machine learning collaborative optimization. The method comprises the following steps: S1, experimental database construction and machine learning performance modeling; S2, machine learning model construction and training; S3, model evaluation and performance index output; and S4, intelligent design of the polyimide-based composite material. The multi-objective performance collaborative optimization design and preparation of the polyimide-based ternary carbon heterostructure composite material are carried out by taking experimental data as a main material and machine learning as an auxiliary material. According to the method, an experiment-model-optimization closed-loop iterative design system is constructed based on an experiment-machine learning collaborative optimization method, the method has the advantages of high prediction precision, high optimization efficiency, good multi-target adaptability and the like, the development efficiency of the polyimide-based composite material is remarkably improved, and the development cost of the polyimide-based composite material is reduced. The method is suitable for intelligent design and large-scale application and popularization of the high-performance heat-conducting electromagnetic shielding material.

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 belongs to the technical field of metallurgical industry process intelligent control and prediction, and discloses a self-adaptive mixed intelligent prediction method and system for smelting endpoint parameters of an electric arc furnace. Acquiring smelting process data of the electric arc furnace; constructing a dual-drive prediction system comprising a mechanism model and a data drive model; calculating a decision coefficient of a prediction value and an actual measurement value of the theoretical model, and counting an effective historical data volume; constructing a machine learning prediction model, and selecting a modeling algorithm according to the effective historical data volume; selecting a hybrid prediction strategy based on the decision coefficient and the effective historical data volume; predicting and outputting an end point carbon content predicted value and an end point temperature predicted value according to a hybrid prediction strategy; predictive deviation threshold value judgment and execution control are conducted, and the electric arc power, the oxygen blowing flow, the feeding speed or the cooling water flow are adjusted. Accurate prediction and dynamic optimization control of the electric arc furnace end point parameters are achieved, and the smelting quality, the energy utilization rate and the production stability are remarkably improved.

The invention relates to the technical field of component analysis, in particular to an intermediate infrared spectrometer sensor verification system which comprises the following steps: acquiring spatial distribution information of a target sample through an automatic sampling module, and generating a corresponding first feature group based on the spatial distribution information; adjusting an emission wave band and a modulation strategy corresponding to a mid-infrared light source, converting mid-infrared photons into visible light signals, and generating a corresponding second feature group; performing down-sampling processing on the original spectral data to generate a corresponding third feature group; inputting the sparse spectral coefficient into a pre-trained deep learning reconstruction model, and dynamically correcting the reconstruction process in combination with an environment temperature compensation parameter to generate reconstructed spectral data corresponding to high resolution; and analyzing the reconstructed spectrum data, matching a preset substance spectrum database, extracting a characteristic absorption peak position and an intensity ratio of the target substance, and generating a corresponding final analysis result. According to the invention, the intelligence of the sensor verification system can be improved.

A method accessing a pre-trained specific material database associating each of a plurality of materials with a corresponding material profile, each material profile including one or more parameters including at least one of a transmit frequency and a response frequency; receiving a selection of a target material from a user; identifying first material profile associated with the target material using the pre-trained specific material database; transmitting, via an RF detection device, an RF signal into the target material using the one or more parameters for the target material associated with the first material profile; receiving, via the RF detection device, a response signal from the target material; analyzing the response signal using an AI algorithm to determine whether resonance characteristics of the response signal indicate a presence of the target material; and notifying the user if the presence of the target material is indicated by the resonance characteristics.

The invention relates to a digital intelligent regulation and control preparation method and system for a high-solid-waste low-carbon high-performance grouting material, and solves the problems that a traditional preparation technology lacks an autonomous and controllable digital intelligent regulation and control system and is weak in adaptability to fluctuation of raw materials, and the method comprises the following steps: obtaining XRF chemical components and XRD mineral composition data of industrial solid waste raw materials; constructing a material gene database; based on the database, screening a proportioning scheme by using a performance prediction model, and predicting workability, strength development and shrinkage performance; inputting a prediction result as a fitness function into a multi-objective optimization algorithm, and outputting an optimal material gene combination; a batching scheme is generated based on the optimal combination, and a stirring process is started after technological parameters are preset; collecting data through a real-time monitoring system, and comparing the data with the digital twin model; and based on a comparison result, automatically adjusting material proportioning parameters. The high-solid-waste, low-carbon and high-performance grouting material has the advantages that accurate design and regulation of the high-solid-waste, low-carbon and high-performance grouting material are achieved, material performance is improved, and carbon emission and cost are reduced.

The invention provides a multi-platform satellite thermal infrared hyperspectral atmospheric ammonia monitoring method and system, and belongs to the technical field of satellite remote sensing. The method comprises the following steps: collecting thermal infrared hyperspectral data, atmospheric state parameters, earth surface parameters and instrument characteristic parameters of a stationary satellite or a polar orbit satellite; based on a thermal infrared radiation transmission physical mechanism, inputting the preprocessed standardized parameters into a fast radiation transmission forward model to obtain a simulated spectrum consistent with the actually measured data format of a stationary satellite or a polar orbit satellite; constructing a cost function containing observation error constraint and NH3 prior information constraint on the basis of the simulated spectrum and the actually measured spectrum in combination with an optimization estimation theory, solving a minimum value of the cost function by adopting a Levenberg-Marquardt iterative algorithm, and performing inversion to obtain an atmospheric ammonia concentration profile; and performing quality control and column concentration conversion on an inversion result, and verifying the precision by combining multi-source observation data to obtain an atmospheric ammonia concentration data set.

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 a customized generation method of a non-corrosive ionic liquid lubricant, and aims to solve the problem of negative correlation between corrosion and lubricating performance of ionic liquid in application of a metal friction pair. The ionic liquid has the characteristics of low volatility, high thermal stability and the like, but the electrochemical activity of zwitterions of the ionic liquid easily causes metal surface corrosion, and the corrosion and the lubricating property are in a negative correlation relationship. According to the method, through constructing a corrosion-lubrication multi-modal database, combining with the steps of feature engineering, collaborative prediction model training, non-corrosion formula directional generation, molecular dynamics Monte Carlo coupling verification, multi-objective optimization and the like, the whole-process intelligence from molecular structure and performance prediction to formula optimization is realized; and an ionic liquid formula with good lubricating performance and no corrosion is efficiently screened. According to the method, the research and development efficiency of the ionic liquid lubricant is remarkably improved, the research and development cost is reduced, and engineering application of the ionic liquid lubricant in key fields such as spaceflight, military industry and extreme manufacturing is promoted.

The invention provides a full-laser multi-energy-field remanufacturing device and method, and relates to the technical field of laser cladding remanufacturing, and the full-laser multi-energy-field remanufacturing device comprises a laser system, an energy field auxiliary system, a powder conveying system, a monitoring system and an intelligent management and control system; the energy field auxiliary system comprises a magnetic field generation unit and an ultrasonic vibration unit; the monitoring system is used for collecting multi-source sensing data reflecting the state of the molten pool in real time; the intelligent management and control system comprises a machine learning digital twinning module used for acquiring historical process data and receiving and performing analysis and prediction based on multi-source sensing data and the historical process data so as to generate a dynamic cooperative control instruction; and the numerical control execution module is used for receiving the control instruction and performing real-time feedback adjustment on the technological parameters of the laser system and the technological parameters of the magnetic field generation unit, the ultrasonic vibration unit and the powder conveying system according to the control instruction. And real-time and dynamic collaborative feedback regulation and control on a plurality of process units such as laser, a magnetic field, an ultrasonic field and powder feeding are realized.

The invention relates to a digital intelligent regulation and control preparation method of a high-solid-waste low-carbon high-durability concrete connecting material, and solves the problems that an existing UHPC connecting material is difficult to balance high performance and low carbon, the solid waste resource utilization rate is low, a digital intelligent precise regulation and control means is lacked, and the cost is low. The method comprises the following steps: firstly, integrating data to construct a special database for the ultra-high performance concrete, establishing a raw material-process-performance mapping relation through multi-scale simulation, then optimizing target parameters through machine learning, preprocessing the raw materials, and finally obtaining the ultra-high performance concrete after the target parameters are optimized and the raw materials are preprocessed. And verifying the iterative model by using a high-throughput experiment, determining optimal process parameters, importing the optimal process parameters into a system, and executing stirring, forming and curing. The method has the following beneficial effects that high solid waste recycling and low-carbon emission reduction are achieved through digital intelligent regulation and control, high strength and high durability of materials are synchronously guaranteed, and the core application requirements of an assembly type structure are precisely met.

The invention discloses a human metabolism multi-task analysis method based on a large language model, which comprises the following steps of: firstly, constructing a unified multi-task data set containing metabolic reaction prediction, and converting tasks such as compound expert description, enzyme classification, reaction type identification and product prediction into a standardized'question-thinking-answer 'text format; and then a Qwen2.5-7B large language model is adopted as an infrastructure, and supervised training is performed through a training parameter fine tuning technology, so that the model can infer the biochemical reaction process. Experimental results show that the method disclosed by the invention has relatively high efficiency and accuracy in a test with the same standard as those of Deepseekv3 and Qwen 2.5-7B.

The invention discloses an information acquisition method and device of an alloy material, electronic equipment and a medium. The method comprises the following steps: acquiring an information query instruction of the alloy material; analyzing the information query instruction through a specified large model to determine a current information query task; the information query task comprises at least one of a general information query task, an alloy performance prediction task and an alloy reverse design task; according to the information query task, calling a target model from a pre-constructed alloy field model library; and executing the information query task through the target model to obtain a target query result. Therefore, the general understanding, reasoning and interaction capabilities of the specified large model and the professional prediction precision and reverse optimization capability of the alloy field model are effectively combined, so that the efficiency, accuracy and intelligent level of alloy material design and discovery are improved, and the rich knowledge requirements of users on alloy material design are met.

The invention belongs to the technical field of biological information, and relates to an acetylcholin esterase inhibitor prediction method based on Stacking ensemble learning and molecular feature fusion, which comprises the steps of data collection and preparation, data annotation and optimization, feature extraction and analysis, construction of a Stacking model, result verification and feedback and construction of a prediction platform. The molecular fingerprints and the property descriptors are used as features, and an acetylcholin esterase inhibitor classifier is successfully constructed by adopting a Stacking algorithm. According to the method, the problems that the efficiency of finding the acetylcholin esterase inhibitor by a traditional experimental method is low, and a common quantitative structure-function relationship method is high in complexity and poor in generalization ability can be solved, the new drug finding speed is increased, experimental candidates are accurately positioned, and resource waste is reduced.

The invention belongs to the technical field of traditional Chinese medicine information processing, and provides a traditional Chinese medicine disease prescription data base construction method and system.The method comprises the steps that five types of data including ancient literatures, modern medical records, prescription data, traditional Chinese medicine resources and medical record manuscript images are accessed through an extensible adapter frame; a traditional Chinese medicine term service platform is constructed by using multi-source term resources, and standardized data is generated from the analyzed data; the method comprises the following steps: extracting diseases, syndromes, prescriptions and traditional Chinese medicine entities by adopting a rule engine and a domain self-adaptive entity recognition model, on the basis of a public traditional Chinese medicine standard knowledge graph, aggregating neighbor node information through GAT, and introducing a contrast learning optimization entity relationship classifier to obtain an enhanced entity vector and classify a semantic relationship; constructing a knowledge graph based on the extracted semantic relationship; and carrying out data quality and algorithm performance monitoring and tracing on data access analysis, standardization processing, knowledge extraction and graph construction. Uniform access and standardized integration of multi-source heterogeneous traditional Chinese medicine data are realized.

The invention belongs to the cross technical field of artificial intelligence and environmental protection engineering, particularly relates to a denitration catalyst microwave regeneration optimization method based on machine learning, and aims to solve the problems of inaccurate energy matching, large structural damage and low efficiency of a traditional regeneration method. The method comprises the following steps: constructing a catalyst multi-dimensional feature database, establishing a microwave regeneration multi-physics field coupling simulation model, and training an integrated learning prediction model to associate input features with a regeneration effect; in the regeneration process, infrared thermal imaging, microwave reflection and gas sensing equipment are integrated, temperature field, energy absorption and tail gas data are collected in real time, and microwave power, frequency and atmosphere parameters are dynamically regulated and controlled. Accurate energy release and closed-loop intelligent regulation and control are achieved, the activity recovery rate after regeneration exceeds 92%, the specific surface area retention rate exceeds 85%, the regeneration period is shortened to one third of that of a traditional method, energy consumption is reduced by 40%, full-life-cycle intelligent management of the catalyst is supported, and economical efficiency and sustainability of environmental protection equipment are remarkably improved.

The invention belongs to the field of artificial intelligence, particularly relates to a coal-fired power plant denitration catalyst full-life-cycle intelligent management system, and aims to solve the problems of information splitting, state evaluation lagging, inaccurate life prediction, subjective regeneration decision and the like. The system comprises catalyst identity identification, multi-source data fusion acquisition, operation state real-time evaluation, life attenuation dynamic prediction, regeneration decision intelligent optimization, disposal path closed-loop management and a full-period digital twin platform. The whole process tracking from delivery to disposal, health quantitative evaluation, residual life accurate prediction, regeneration opportunity and process intelligent decision and compliance disposal closed loop are realized, and the operation economy and environmental protection reliability of the denitration system are improved.

The invention provides a semiconductor epitaxial growth parameter inversion method, a semiconductor epitaxial growth parameter inversion system, a semiconductor epitaxial growth parameter detection device and a related product, and relates to the technical field of semiconductor detection. The gas phase transmittance of a detection light path is further obtained, quantitative compensation is carried out on the actually measured reflection spectrum in the semiconductor epitaxial growth process, the compensated target reflection spectrum is closer to the real optical response of a semiconductor epitaxial film, and therefore spectral shape distortion and system errors caused by gas phase absorption superposition are effectively weakened. And the accuracy and the stability of the inverted growth parameters are obviously improved. Furthermore, the quantitative compensation value can be adaptively updated according to the fluctuation of the working condition, the inversion deviation introduced by the drift of the working condition is reduced, the consistency and comparability of the process are improved, and more reliable end point control and parameter closed-loop optimization can be conveniently realized.

The invention discloses an intelligent adding control platform for a water treatment process, and relates to the technical field of water treatment, and the intelligent adding control platform comprises a data processing module which is used for obtaining historical water treatment data of water treatment plants in different regions to obtain historical medicament use data; the feedforward control module is used for acquiring sewage data in the plant area to obtain basic dosing data; the prediction and supplement module is used for predicting a water quality change trend according to the basic dosing data to obtain a dosing effect, and performing medication adjustment to obtain supplementary dosing data; the dynamic adjustment module is used for obtaining a target quantification set of the plant area and carrying out weight distribution to obtain a dosing strategy; and the trust adjustment module is used for collecting the sensor state and sensor output data of each sensor to obtain the confidence coefficient of sewage treatment, and modifying the dosing strategy according to the confidence coefficient to obtain a target dosing strategy. The method has the effect of improving the accuracy and intelligence of automatic dosing on medicament control.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for predicting toxicity of a molecule. In one aspect, a method comprises: obtaining data identifying an input molecule; generating data defining a toxicity analysis tree for the input molecule; and processing the toxicity analysis tree to generate a respective toxicity score for each of a plurality of molecule fragments in the input molecule that characterizes an impact of the molecule fragment on a toxicity of the input molecule.

The embodiment of the invention provides a construction method of an initial structure of a molecular system, a construction method of a molecular system structure, a computer program product, a computer system and a computer readable storage medium. The construction method of the initial structure of the molecular system comprises the following steps: setting a molecular object, obtaining a basic construction unit of the initial structure of the molecular system to be constructed, and obtaining molecular parameters corresponding to the molecular object; obtaining atomic charges according to a predetermined atomic charge calculation method; according to the SMILES character string of the basic construction unit, obtaining an atomic type identifier; carrying out three-dimensional stacking; obtaining a Dresing force field parameter of the stacked molecular system, and obtaining an LAMMPS format file of the structure of the stacked molecular system and the Dresing force field parameter of the stacked molecular system; and carrying out a relaxation process on the stacked molecular system to obtain an initial structure of the molecular system. By adopting the construction method to construct the initial structure of the molecular system, the construction accuracy and efficiency are relatively high.

The invention provides an agricultural land heavy metal pollutant pollution tracing method and system, and relates to the technical field of data processing method.The agricultural land heavy metal pollutant pollution tracing method comprises the steps that multi-source heterogeneous environment data of an agricultural land to be traced is acquired; constructing a heterogeneous graph; inputting node features of each node in the heterogeneous graph and edge indexes and edge attributes of each connection edge into a plurality of trained graph attention network models to obtain edge contribution vectors; polymerizing the plurality of edge contribution vectors to obtain the final contribution of each heavy metal pollutant; and heavy metal pollutant pollution tracing is carried out. According to the scheme, the heterogeneous graph containing the receptor nodes and the pollution source nodes is constructed, the multi-source heterogeneous environment data is mapped into the node features and the edge attributes, explicit structured expression of the pollution source-receptor relation is achieved, the model can learn the complex nonlinear mapping relation of pollution contributions end to end, and the method has the advantages of being high in practicability and easy to popularize. And the spatial accuracy and the chemical rationality of the traceability result can be improved.

The embodiment of the invention discloses a drug molecule discovery method and device, a medium and equipment, and the method comprises the steps: extracting an entity of an input text, recognizing the intention of the input text, and scheduling one or more processes in drug molecule discovery processes related to the entity according to an intention recognition result, so that a user only needs to give the input text, and the user experience is improved. The subsequent operation of the drug molecule discovery process can automatically schedule one or more processes in the drug molecule discovery processes related to the entity in the input text according to the intention result of the input text, so that a user is prevented from manually importing and exporting data between different processes, and the processing efficiency of drug molecule discovery is improved.