An AI-optimized high-sensitivity tumor detection reagent development method

Abstract

The application discloses an AI-optimized high-sensitivity tumor detection reagent development method, and relates to the technical field of biological detection reagent development.The specific steps of the method are as follows: firstly, a dynamic database is constructed, multi-dimensional data is acquired, and classification and archiving are completed; then, a space-time graph neural network model containing multiple modules is trained; next, a target antigen sequence is input into the model to screen an optimal epitope combination; according to the determination, a qualified antibody sequence is determined; finally, based on the qualified sequence, a parameter feedback iteration mechanism is constructed by using a closed-loop self-adaptive tuning algorithm, various process configurations are matched, and an antibody preparation process chain is formed; by constructing the dynamic database and the space-time graph neural network model, the application can accurately analyze and predict the tumor antigen antibody binding characteristics, and improve the epitope screening efficiency and the sensitivity of the detection reagent; through the deep cooperation of antibody design and production process, the whole-process configuration is optimized, a whole-process efficient R&D system is formed, and the tumor detection reagent development efficiency and the finished product performance are comprehensively improved.

Description

Technical Field

[0001] This invention relates to the field of biological detection reagent development technology, specifically to an AI-optimized method for developing a high-sensitivity tumor detection reagent. Background Technology

[0002] Early and accurate detection of tumors is a core element in improving the effectiveness of clinical tumor diagnosis and treatment. High-sensitivity tumor detection reagents are key carriers for achieving efficient detection. The specific binding ability of antigens and antibodies directly determines the sensitivity and accuracy of detection reagents. With the continuous advancement of structural biology, bioinformatics, and artificial intelligence technologies, multidimensional biological data related to tumor antigens and antibodies are becoming increasingly abundant. Information such as protein three-dimensional structure, conformational dynamics, microstructural characteristics, molecular binding activity, and biomanufacturing physicochemical parameters has become an important support for the development of high-performance detection reagents. Currently, the demand for clinical tumor detection continues to increase, the types of tumor targets are constantly increasing, and tumor antigens are prone to sequence mutations. Traditional data processing methods cannot achieve standardized integration and dynamic correlation analysis of multi-source data. There is a lack of effective collaboration between molecular design and production processes. The industry urgently needs to build an intelligent and integrated R&D system to overcome existing technological limitations and meet the actual application needs of clinical high-sensitivity tumor detection reagents.

[0003] Traditional tumor diagnostic reagent development suffers from significant technological shortcomings. The overall development process relies on manual experience and single-experiment verification. Multi-source biological data is stored in a scattered manner without integrated utilization, making it impossible to comprehensively analyze the structural, activity, physicochemical, and dynamic conformational characteristics of antigens and antibodies. This results in insufficient accuracy in molecular binding characteristic analysis, making it difficult to improve the sensitivity and specificity of diagnostic reagents. Epitope screening relies on repeated experimental trial and error, which is complex and time-consuming, unable to quickly match multiple mutation forms of tumor antigens, and the stability of screening results is insufficient. Antibody sequence design and preparation processes are independent, lacking a synergistic control mechanism between design and production parameters, making it difficult to guarantee the physicochemical stability and manufacturing suitability of antibodies, and prone to performance failures. Traditional analytical models cannot handle spatiotemporal dynamic characteristics, are easily affected by environmental factors, and produce prediction errors. Furthermore, they are not included in the manufacturability evaluation process, resulting in low overall development efficiency and high costs, which cannot support the efficient development and large-scale application of high-sensitivity tumor diagnostic reagents. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and provide an AI-optimized method for developing high-sensitivity tumor detection reagents. By constructing a dynamic database integrating multi-dimensional biological data, relying on a proprietary algorithm to complete data standardization and weight matching, and training a neural network model with spatiotemporal feature analysis and manufacturability evaluation capabilities, this method can accurately screen optimal epitope combinations, complete antibody sequence design and modification, and compliance screening. Furthermore, by constructing a self-iterable preparation process chain through a closed-loop adaptive algorithm, it achieves intelligent collaboration throughout the entire R&D process. This invention effectively solves the problems of isolated data, disjointed processes, low efficiency, and insufficient detection performance in traditional R&D, significantly improving the sensitivity and specificity of tumor detection reagents, shortening the R&D cycle, reducing production costs, and adapting to the development of multiple tumor targets. It provides reliable technical support for the efficient development and large-scale application of high-sensitivity tumor detection reagents.

[0005] To solve the above-mentioned technical problems, this invention provides the following technical solution: a method for developing an AI-optimized high-sensitivity tumor detection reagent, the specific steps of which are as follows: S1. Construct a dynamic database: Obtain the three-dimensional structure, protein conformational evolution trajectory, microstructure, binding activity, and biomanufacturing physicochemical parameters of tumor antigen-antibody complexes; use a dynamic feature coupling algorithm to calculate standardized calibration coefficients and association matching weights; complete classification and archiving; and form a dynamic database. The dynamic database contains three-dimensional structural data of complexes formed by natural antibodies, humanized antibodies, chimeric antibodies, and tumor antigens corresponding to various tumor targets; protein conformational evolution trajectory data under different temperature, pH, ionic strength, and osmotic pressure conditions; microstructural data such as hydrogen bond arrangement, hydrophobic region area, surface charge distribution, disulfide bond sites, and glycosylation sites; binding activity data such as molecular binding constant and molecular dissociation constant; biomanufacturing physicochemical parameters such as protein solubility, structural thermal response values, and charge distribution values; and standardized calibration coefficients and correlation matching weights calculated by the dynamic feature coupling algorithm. All data are classified and archived according to tumor type, antigen subtype, and data category. S2, Training the model: Construct a spatiotemporal graph neural network model that includes graph convolutional layers, temporal learning layers, causal denoising layers and manufacturability evaluation modules. Use the spatiotemporal co-evaluation algorithm to calculate the model prediction error and parameter gradients. Use the full sample of the dynamic database from step S1 to iteratively train the model and calibrate the parameters. S3, Screening the optimal epitope combination: Input the wild-type and mutant sequences of the target tumor antigen into the model trained in step S2, use the spatiotemporal co-evaluation algorithm to calculate the epitope identification score, generate an epitope mutant library, perform multi-level screening on the library, and determine the optimal epitope combination based on the score; When generating the epitope mutant library, the epitope identification score calculated by the model is used to first identify the epitope regions on the antigen surface that meet the screening criteria, determine the amino acid sequence length of the epitope region, and then modify the epitope amino acid sequence through four methods: conserved amino acid substitution, non-conserved substitution, amino acid fragment insertion, and amino acid fragment deletion. Among them, the key amino acid sites in the core region of the epitope are selected for amino acid substitution, and the length of the inserted and deleted amino acid fragments is controlled within 1-3 amino acids. Epitope mutants of different modification types are generated in batches. All generated mutants are classified and organized according to mutation type and amino acid modification sites to form an epitope mutant library containing multiple mutation forms. S4, Determine qualified antibody sequences: Based on the optimal epitope combination in step S3, the spatiotemporal collaborative evaluation algorithm is used to calculate the binding free energy score and physicochemical performance prediction value after antibody variable region design and humanization modification, complete the dynamic binding mode deduction, conduct physicochemical performance testing and compliance screening, and determine qualified antibody sequences. S5, Forming the process chain: Based on the qualified antibody sequence in step S4, a closed-loop adaptive optimization algorithm is used to calculate the feedback correction amount between antibody design parameters and production parameters, construct a parameter feedback iteration mechanism, reverse correct the design parameters, match the codon optimization scheme, cell fermentation regulation parameters and chromatography purification process configuration, and form the antibody preparation process chain.

[0006] Furthermore, in S1, a dynamic database is constructed, in which the collected three-dimensional structures of tumor antigen-antibody complexes cover the structures of natural antibodies, humanized antibodies, and chimeric antibody combinations corresponding to various tumor targets; protein conformational evolution trajectories are collected, including continuous structural change data under different temperature ranges, acid-base ranges, ion concentration ranges, and osmotic pressure ranges; microstructure includes hydrogen bond arrangement values, hydrophobic region area, surface charge distribution values, solvent-accessible region range, disulfide bond arrangement sites, and glycosylation distribution sites; binding activity includes molecular binding constant and molecular dissociation constant; and biomanufacturing physicochemical parameters include protein solubility values, structural thermal response values, and charge distribution values.

[0007] Furthermore, in S1, the mathematical expression for the dynamic feature coupling algorithm in the dynamic database is: in, represents the comprehensive feature vector of the complex, and t represents the conformational evolution time parameter. Represents a vector of microenvironment parameters. Represents the dynamic weight values ​​of structural features. Represents the microstructure eigenvector. This represents the weighting of the influence of the microenvironment. Represents the combination of active feature vectors, This represents a fixed weight value for manufacturing characteristics. Represents the physicochemical characteristic vector of biological manufacturing. This represents the corrected value for multi-source data.

[0008] Furthermore, in S2, the training model, specifically the spatiotemporal graph neural network model, includes a graph convolutional layer comprising a residue node encoding unit and an atomic interaction edge weight calculation unit. It uses amino acid residues of tumor antigens and antibodies as operational nodes and atomic interactions between residues as associated edges to complete the encoding and computation of node features and edge weights. The temporal learning layer comprises a temporal feature sequence receiving unit and an attention weight allocation unit, used to receive protein conformational feature sequences corresponding to different time nodes and assign corresponding attention weights to key conformational features. The causal denoising layer comprises an environmental deviation extraction unit and a denoising computation unit, used to collect feature deviation data generated by temperature and pH fluctuations and perform denoising computation. The manufacturability evaluation module comprises a physicochemical index input unit and a multi-dimensional numerical calculation unit, used to incorporate values ​​related to protein aggregation, protein hydrolysis tolerance, charge distribution, and structural thermal stability and complete comprehensive calculations. Each layer and module is connected sequentially in the order of feature input, computation processing, and result output to form a complete spatiotemporal graph neural network model.

[0009] Furthermore, in S2, the mathematical expression of the spatiotemporal co-evaluation algorithm in the training model is: in, This represents the overall evaluation score. This represents the combined feature weight values. This represents the result of the spatiotemporal graph feature extraction operation. This represents the weighting value of the physicochemical evaluation. Represents a multi-dimensional physicochemical feature vector. This represents the quality correction value. This represents the environmental noise reduction coefficient value. This represents the sequence-specific correction value.

[0010] Furthermore, in step S2, during the training of the model, when iteratively training the model and calibrating the parameters using the full sample data from the dynamic database in step S1, all sample data in the dynamic database are first retrieved and divided into training sample set and validation sample set according to data category. Iterative training is carried out sequentially in a preset fixed batch. In each batch of training, the complex feature data, conformational evolution data, and physicochemical parameter data from the training sample set are first input into the spatiotemporal graph neural network model for calculation. Then, the model's calculation output results are compared with the actual sample values. Combined with the model prediction error and parameter gradient calculated by the spatiotemporal co-evaluation algorithm, the weight parameters of the model graph convolutional layer, temporal learning layer, causal denoising layer, and the calculation parameters of the manufacturability evaluation module are initially adjusted. After completing a single batch of training, the validation sample set data is input into the model for calculation verification. Based on the verification results, the relevant parameters of the model are fine-tuned again. The above batch training, parameter adjustment, and verification process is repeated until the model calculation error drops to a preset threshold, thus completing the model training and parameter calibration.

[0011] Furthermore, in S3, the input mutant sequences in the screening of the optimal epitope combination include missense mutation sequences, nonsense mutation sequences, frameshift mutation sequences, and fragment deletion mutation sequences; the epitope identification score is calculated based on a comprehensive calculation of sequence feature values ​​and spatial structure feature values; the epitope mutant library is constructed through amino acid site-directed substitution, amino acid fragment insertion, and amino acid fragment deletion; the multi-level screening is carried out in sequence around the structural feature values, molecular binding values, and physicochemical feature values ​​to make hierarchical judgments.

[0012] Furthermore, in S4, the qualified antibody sequence is determined to contain both heavy chain and light chain variable regions; humanization modification involves adjusting the base and amino acid matching of the variable region framework sequence; calculations are performed based on the molecular binding interface forces using free energy scores; physicochemical performance predictions cover aggregation values, hydrolysis sensitivity values, charge shift values, and heat tolerance values; dynamic binding mode deduction covers the spatial conformational changes throughout the molecular docking process; physicochemical performance testing includes solution environment tolerance testing, temperature gradient tolerance testing, and protease contact testing; and compliance screening sets judgment thresholds based on bioproduct quality control standards.

[0013] Furthermore, in step S5, the mathematical expression for the closed-loop adaptive optimization algorithm in the process chain is: in, The value represents the parameter correction. This represents the value of the feedback control coefficient. This represents the value specified by the quality standard. This represents the overall evaluation score. Representative and A vector of parameter correlation coefficients of the same dimension. This represents the weighted value of the physicochemical deviation. This represents the deviation vector of physicochemical indicators. Represents the initial parameter set. This represents the corrected set of parameters.

[0014] Furthermore, in S5, the feedback correction amount in the process chain is jointly calculated based on the deviation of antibody physicochemical values ​​and the deviation of production parameter ranges; the parameter feedback iteration mechanism runs in a loop according to the order of data output, numerical comparison, and parameter adjustment; the designed parameter correction range includes epitope screening judgment values ​​and antibody sequence modification values; the codon optimization scheme is matched and set according to the codon usage preferences of mammalian expression systems; the cell fermentation regulation parameters include culture environment osmotic pressure, feeding sequence, culture environment temperature, and culture environment pH; the chromatography purification process configuration includes affinity chromatography unit, ion exchange chromatography unit, hydrophobic chromatography unit, and ultrafiltration concentration unit.

[0015] Compared with existing technologies, this AI-optimized method for developing high-sensitivity tumor detection reagents has the following advantages: I. This invention constructs a dynamic database integrating multi-dimensional biological data, uses a dynamic feature coupling algorithm to standardize and weight the data, builds a spatiotemporal graph neural network model with multiple modules and completes iterative training, achieving accurate analysis and prediction of tumor antigen-antibody binding characteristics. This approach breaks through the limitations of isolated data and one-sided feature extraction in traditional R&D, comprehensively capturing the structure, activity, and physicochemical characteristics of antigens and antibodies, improving the accuracy of molecular binding characteristic assessment. Based on the trained model, epitope combination screening can be carried out, quickly identifying high-quality epitopes that are compatible with target tumor antigens. This avoids the drawbacks of traditional epitope screening relying on manual experience and having a lengthy cycle, significantly improving epitope screening efficiency. At the same time, it ensures the specificity of epitope-antibody binding, enhances the recognition sensitivity of detection reagents, and avoids the problems of weak detection signals and insufficient specificity from the core design stage, laying a solid core foundation for the development of high-sensitivity tumor detection reagents.

[0016] II. This invention completes the design, modification, and compliance screening of antibody sequences based on optimal epitope combinations. Combined with a closed-loop adaptive optimization algorithm, it constructs a parameter-iterative antibody preparation process chain, achieving deep synergy between antibody design and production processes. This model solves the problems of design-production disconnect and poor process adaptability in traditional R&D, improving the physicochemical stability and biomanufacturing adaptability of antibody sequences, ensuring stable antibody performance in different application scenarios. Through closed-loop feedback correction of process parameters, it continuously optimizes the entire process configuration, including codon optimization, cell fermentation, and chromatographic purification, improving preparation efficiency and finished product quality pass rate, reducing resource consumption in R&D and production. Overall, it forms a highly efficient R&D system covering the entire process from data modeling and epitope screening to antibody design and process preparation, comprehensively improving the R&D efficiency and finished product performance of tumor detection reagents, and facilitating the rapid large-scale R&D and application of high-sensitivity tumor detection reagents.

[0017] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0019] Figure 1 Flowchart of a method for developing high-sensitivity tumor detection reagents optimized for AI; Figure 2 A schematic diagram illustrating data transmission between steps in the development method of AI-optimized high-sensitivity tumor detection reagents. Figure 3 A flowchart illustrating the process of selecting the optimal epitope combination. Detailed Implementation

[0020] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0021] Reference Figure 1One embodiment of the present invention proposes an AI-optimized method for developing high-sensitivity tumor detection reagents. It adopts a full-process AI collaborative mechanism that integrates multi-source biological data dynamically, models spatiotemporal feature neural networks, intelligently screens antigen epitopes, precisely optimizes antibody sequences, and fine-tunes the closed-loop design and production process. This mechanism can fully cover the spatial characteristics and temporal variation patterns of tumor antigen-antibody interactions, effectively eliminate detection interference caused by environmental factors, and simultaneously improve antibody binding activity and industrial production adaptability, ultimately achieving efficient and stable development of high-sensitivity tumor detection reagents.

[0022] The method described in this embodiment specifically includes: S1, constructing a dynamic database: acquiring the three-dimensional structure, protein conformational evolution trajectory, microstructure, binding activity, and biomanufacturing physicochemical parameters of tumor antigen-antibody complexes; calculating standardized calibration coefficients and association matching weights using a dynamic feature coupling algorithm; completing classification and archiving to form a dynamic database; S2, training a model: constructing a spatiotemporal graph neural network model containing graph convolutional layers, temporal learning layers, causal denoising layers, and a manufacturability evaluation module; calculating model prediction errors and parameter gradients using a spatiotemporal co-evaluation algorithm; iteratively training the model and calibrating parameters using the full sample data from the dynamic database in step S1; S3, selecting the optimal epitope combination: inputting wild-type and mutant sequences of the target tumor antigen into the model trained in step S2; and using a spatiotemporal co-evaluation algorithm... The process involves: S4, determining qualified antibody sequences: Based on the optimal epitope combination from step S3, a spatiotemporal collaborative evaluation algorithm is used to calculate the binding free energy score and physicochemical performance prediction values ​​after antibody variable region design and humanization modification. Dynamic binding mode deduction is completed, and physicochemical performance testing and compliance screening are performed to determine qualified antibody sequences. S5, forming the process chain: Based on the qualified antibody sequences from step S4, a closed-loop adaptive optimization algorithm is used to calculate the feedback correction amount between antibody design parameters and production parameters. A parameter feedback iteration mechanism is constructed to reverse-correct design parameters, match codon optimization schemes, cell fermentation regulation parameters, and chromatography purification process configurations to form the antibody preparation process chain.

[0023] Specifically, the key to the technical implementation of this invention lies in building a dynamic database covering multi-dimensional biological information, relying on a customized spatiotemporal graph neural network model to complete the intelligent optimization of antigen epitopes and antibody sequences, and then achieving collaborative matching between R&D design and industrial production through a closed-loop adaptive tuning algorithm. Throughout the process, AI algorithms complete data calibration, feature extraction, error removal, and parameter correction, ultimately obtaining antibodies for tumor detection reagents that meet the requirements of high-sensitivity detection and are suitable for large-scale production. Figure 2 As shown.

[0024] S1, Building a dynamic database: Optionally, the constructed dynamic database collects three-dimensional structures of tumor antigen-antibody complexes covering natural antibodies, humanized antibodies, and chimeric antibody combinations corresponding to various tumor targets; it collects continuous structural change data of protein conformation evolution trajectories under different temperature ranges, acid-base ranges, ion concentration ranges, and osmotic pressure ranges; the microstructure includes hydrogen bond arrangement values, hydrophobic region area, surface charge distribution values, solvent-accessible region range, disulfide bond arrangement sites, and glycosylation distribution sites; the binding activity includes molecular binding constant and molecular dissociation constant; and the biomanufacturing physicochemical parameters include protein solubility values, structural thermal response values, and charge distribution values.

[0025] Specifically, the construction of a dynamic database requires the comprehensive collection and standardization of multi-source data. First, it involves collecting the three-dimensional structural data of complexes formed by natural antibodies, humanized antibodies, and chimeric antibodies corresponding to tumor targets binding with tumor antigens. Then, it involves collecting continuous trajectory data of protein conformation changes over time under different temperatures, pH values, ionic strengths, and osmotic pressures. Simultaneously, it includes microstructural data such as hydrogen bond arrangement, hydrophobic region area, surface charge distribution, solvent-accessible regions, disulfide bond sites, and glycosylation sites; binding activity data such as molecular binding constants and molecular dissociation constants; and bio-manufacturing physicochemical parameters such as protein solubility, structural thermal response, and charge distribution. Subsequently, a dynamic feature coupling algorithm is used to calculate all the above data to obtain standardized calibration coefficients and association matching weights. Finally, the entire dataset is classified and archived according to tumor type, antigen subtype, and data category to form a dynamic database that can be directly used for model training.

[0026] The mathematical expression for the dynamic feature coupling algorithm is: in, represents the comprehensive feature vector of the complex, and t represents the conformational evolution time parameter. Represents a vector of microenvironment parameters. Represents the dynamic weight values ​​of structural features. Represents the microstructure eigenvector. This represents the weighting of the influence of the microenvironment. Represents the combination of active feature vectors, This represents a fixed weight value for manufacturing characteristics. Represents the physicochemical characteristic vector of biological manufacturing. This represents the corrected value for multi-source data.

[0027] For example, when constructing a dynamic database corresponding to a target tumor, the three-dimensional structural data of complexes formed by the binding of natural antibodies, humanized antibodies, and chimeric antibodies to tumor antigens are first collected to achieve full coverage of the structure of multiple types of antibody-antigen complexes. Then, different environmental conditions such as temperature, pH, ionic strength, and osmotic pressure are set, and continuous trajectory data of protein conformational evolution under each condition are collected. Simultaneously, microstructural data such as hydrogen bond arrangement, hydrophobic region area, surface charge distribution, solvent-accessible region range, disulfide bond arrangement sites, and glycosylation distribution sites are detected and recorded, along with binding activity data such as molecular binding constant and molecular dissociation constant, and biomanufacturing physicochemical parameters such as protein solubility, structural thermal response, and charge distribution. All the collected data are then substituted into a dynamic feature coupling algorithm to calculate the dynamic weight of structural features, the weight of microenvironmental influence, and the fixed weight of manufacturing features, resulting in a comprehensive feature vector of the complex, standardized calibration coefficients, and association matching weights. Finally, all data are classified and archived according to tumor type, target antigen subtype, and data category to complete the construction of the dynamic database, which can directly provide standardized sample data for subsequent model training.

[0028] S2, Training the model: Optionally, in the training model, the graph convolutional layer of the spatiotemporal graph neural network model includes a residue node encoding unit and an atomic interaction edge weight calculation unit, using amino acid residues of tumor antigens and antibodies as operation nodes and atomic interaction relationships between residues as associated edges to complete the encoding operation of node features and edge weights; the temporal learning layer includes a temporal feature sequence receiving unit and an attention weight allocation unit, used to receive protein conformation feature sequences corresponding to different time nodes and assign corresponding attention weights to key conformation features; the causal denoising layer includes an environmental deviation extraction unit and a denoising operation unit, used to collect feature deviation data generated by temperature and pH fluctuations and perform denoising operations; the manufacturability evaluation module includes a physicochemical index input unit and a multi-dimensional numerical calculation unit, used to incorporate relevant values ​​of protein aggregation, protein hydrolysis tolerance, charge distribution, and structural thermal stability and complete comprehensive calculations; each layer and module is connected sequentially in the order of feature input, operation processing, and result output to form a complete spatiotemporal graph neural network model.

[0029] Specifically, the spatiotemporal graph neural network model consists of a graph convolutional layer, a temporal learning layer, a causal denoising layer, and a manufacturability evaluation module connected sequentially. The graph convolutional layer uses the amino acid residues of the antigen and antibody as computational nodes and atomic interactions as relational edges to complete the encoding operation of spatial structural features. The temporal learning layer receives protein conformational feature sequences at different time points, assigns attention weights to key conformational features, and captures the temporal change patterns. The causal denoising layer extracts feature deviations caused by temperature and pH fluctuations and performs denoising processing to remove environmental interference. The manufacturability evaluation module incorporates relevant values ​​of protein aggregation, hydrolysis tolerance, charge distribution, and thermal stability to complete the comprehensive calculation of production adaptability. The four-layer structure and module work together to complete the spatiotemporal feature extraction, error removal, and manufacturability assessment of the antigen-antibody complex.

[0030] During training, a spatiotemporal co-evaluation algorithm is used to calculate the model prediction error and parameter gradients. The mathematical expression of this algorithm is: in, This represents the overall evaluation score. This represents the combined feature weight values. This represents the result of the spatiotemporal graph feature extraction operation. This represents the weighting value of the physicochemical evaluation. Represents a multi-dimensional physicochemical feature vector. This represents the quality correction value. This represents the environmental noise reduction coefficient value. This represents the sequence-specific correction value.

[0031] Optionally, when iteratively training the model and calibrating parameters using the full sample data of the dynamic database, all sample data in the dynamic database are first retrieved and divided into training sample set and validation sample set according to data category. Iterative training is carried out in a predetermined fixed batch. In each batch of training, the complex feature data, conformational evolution data, and physicochemical parameter data in the training sample set are first input into the spatiotemporal graph neural network model for calculation. Then, the model's calculation output results are compared with the actual sample values. Combined with the model prediction error and parameter gradient calculated by the spatiotemporal co-evaluation algorithm, the weight parameters of the model graph convolutional layer, temporal learning layer, causal denoising layer, and the calculation parameters of the manufacturability evaluation module are initially adjusted. After completing a single batch of training, the validation sample set data is input into the model for calculation verification. Based on the verification results, the relevant parameters of the model are fine-tuned again. The above batch training, parameter adjustment, and verification process is repeated until the model calculation error drops to a predetermined threshold, thus completing the model training and parameter calibration.

[0032] Specifically, model training requires first dividing the samples, separating the entire dynamic database into training and validation sets according to categories, and then conducting iterative training in fixed batches. For each batch, the complex characteristics, conformational evolution, and physicochemical parameters of the training samples are input into the model for computation. The model output values ​​are compared with the actual sample values, and the prediction error and parameter gradient are calculated using a spatiotemporal co-evaluation algorithm. Initial adjustments are made to the weights of each layer and the computational parameters of each module. After a single batch of training, the model computation results are verified using the validation set, and the parameters are fine-tuned based on the verification error. This training, parameter tuning, and verification process is repeated until the model computation error reaches a preset standard, completing model training and parameter calibration.

[0033] For example, when training a spatiotemporal graph neural network model, all sample data in the dynamic database are first retrieved, and the samples are divided into training sample sets and validation sample sets according to data categories. Fixed batches are set for iterative training. Complex feature data, conformational evolution data, and physicochemical parameter data from the training sample sets are sequentially input into the spatiotemporal graph neural network model. The model completes calculations and outputs results through graph convolutional layers, temporal learning layers, causal denoising layers, and a manufacturability evaluation module. The model output results are compared with the actual sample values. The model prediction error and parameter gradient are calculated using a spatiotemporal co-evaluation algorithm. Based on the calculation results, the weight parameters of the graph convolutional layers, temporal learning layers, and causal denoising layers, as well as the calculation parameters of the manufacturability evaluation module, are initially adjusted. After a single batch of training is completed, validation sample set data is input into the model for computational verification. Based on the error values ​​obtained from the verification, the relevant model parameters are fine-tuned again. This process of batch training, initial parameter adjustment, verification, and secondary parameter fine-tuning is repeated iteratively until the model's computational error drops to a preset threshold, ultimately completing the full training and calibration of all parameters of the model.

[0034] S3, Filtering the optimal epitope combination: Optionally, in the selection of the optimal epitope combination, the input mutant sequences include missense sequences, nonsense sequences, frameshift sequences, and fragment deletion sequences; the epitope identification score is calculated based on a comprehensive calculation of sequence feature values ​​and spatial structure feature values; the epitope mutant library is constructed through amino acid conserved substitution, non-conserved substitution, amino acid fragment insertion, and amino acid fragment deletion; multi-level screening sequentially performs hierarchical judgment based on structural feature values, molecular binding values, and physicochemical feature values, such as... Figure 3 As shown.

[0035] Specifically, the optimal epitope combination screening process first requires inputting the wild-type sequence of the target tumor antigen and four types of mutant sequences—missense variants, nonsense variants, frameshift mutations, and fragment deletion variants—into the trained model. The model calculates an epitope identification score by integrating sequence features and spatial structural features through a spatiotemporal co-evaluation algorithm. Based on the score, epitope regions on the antigen surface that meet the screening criteria are identified. After determining the amino acid sequence length of the region, the epitope sequence is modified using four methods: conserved amino acid substitution, non-conserved substitution, fragment insertion, and fragment deletion. Substitution targets key amino acid sites of the epitope, while insertion and deletion fragments are controlled to a length of 1-3 amino acids. Mutants are generated in batches and classified to form an epitope mutant library. Finally, multi-level screening is carried out in the order of structural feature values, molecular binding values, and physicochemical feature values, and the optimal epitope combination is determined based on the final score.

[0036] For example, when screening for the optimal epitope combination of a target tumor antigen, the wild-type sequence of the target tumor antigen, as well as four types of mutant sequences—missense variants, nonsense variants, frameshift mutations, and deletion variants—are first input into a trained and calibrated spatiotemporal graph neural network model. The model uses a spatiotemporal co-evaluation algorithm to calculate the identification score for each epitope by combining sequence feature values ​​and spatial structure feature values. Based on the epitope identification score, epitope regions on the antigen surface that meet the screening criteria are identified, and the amino acid sequence length of these regions is determined. Key amino acid sites in the core region of the epitope are selected, and conserved and non-conserved amino acid substitution operations are performed. Simultaneously, the epitope... The sequences underwent amino acid insertion and deletion operations, with the length of the inserted and deleted amino acid fragments strictly controlled to be between 1 and 3 amino acids. Different types of epitope mutants were generated in batches using the above four modification methods. All mutants were classified and organized according to mutation type and amino acid modification site to form an epitope mutant library containing multiple mutation forms. Subsequently, the library underwent multi-level screening. The first level was based on structural feature values, the second level on molecular binding values, and the third level on physicochemical feature values. After three levels of screening, the remaining mutants were ranked according to their epitope identification scores, and the epitope combination with the highest score was determined as the optimal epitope combination.

[0037] S4, Determine the qualified antibody sequence: Optionally, in the determination of qualified antibody sequences, the antibody variable region includes heavy chain variable region sequences and light chain variable region sequences; humanization modification involves base and amino acid matching adjustments to the variable region framework sequence; calculations are performed based on the molecular binding interface forces using free energy scores; physicochemical performance prediction values ​​cover aggregation values, hydrolysis sensitivity values, charge shift values, and heat tolerance values; dynamic binding mode deduction covers the spatial conformational changes throughout the molecular docking process; physicochemical performance testing includes solution environment tolerance testing, temperature gradient tolerance testing, and protease contact testing; compliance screening sets judgment thresholds based on biopharmaceutical quality control standards.

[0038] Specifically, the determination of qualified antibody sequences requires the design of antibody heavy chain variable region and light chain variable region sequences based on the optimal epitope combination, and the humanization modification of the variable region framework sequence by matching bases and amino acids. A spatiotemporal collaborative evaluation algorithm is used, based on numerical calculations of molecular binding interface forces combined with free energy scores, to simultaneously predict physicochemical properties such as aggregation values, hydrolysis sensitivity values, charge shift values, and heat tolerance values. The spatial conformation dynamics of the entire antibody-antigen molecule docking process are then simulated, followed by three physicochemical property tests: solution environment tolerance, temperature gradient tolerance, and protease contact. Finally, a judgment threshold is set according to the biopharmaceutical quality control standards to complete compliance screening and ultimately determine the qualified antibody sequences that meet the requirements.

[0039] For example, when determining a qualified antibody sequence, the optimal epitope combination obtained in the previous step is used as the design basis to complete the design of the heavy chain variable region sequence and the light chain variable region sequence of the antibody. For the variable region framework sequence of the antibody, base and amino acid matching adjustments are carried out to complete the humanization modification. The modified antibody sequence is input into the model, and through the spatiotemporal collaborative evaluation algorithm, based on the numerical calculation of molecular binding interface interaction forces and the free energy score, the predicted values ​​of physicochemical properties such as aggregation value, hydrolysis sensitivity value, charge shift value, and heat tolerance value are obtained. The entire molecular docking process between the antibody and the antigen is dynamically simulated, fully covering the spatial conformational changes of the entire molecular docking process. Subsequently, physicochemical performance testing is carried out, including solution environment tolerance test, temperature gradient tolerance test, and protease contact test, to confirm that the antibody physicochemical performance meets the standards. Finally, according to the biopharmaceutical quality control standards, a compliance judgment threshold is set, and the antibody sequences are screened for compliance. Sequences that do not meet the standards are eliminated, and finally, antibody sequences that meet all the standards are determined to be qualified antibody sequences.

[0040] S5, forming the process chain: Optionally, in the formation process chain, the feedback correction amount is jointly calculated based on the deviation of antibody physicochemical values ​​and the deviation of production parameter ranges; the parameter feedback iteration mechanism runs in a loop according to the order of data output, numerical comparison, and parameter adjustment; the design parameter correction range includes epitope screening judgment values ​​and antibody sequence modification values; the codon optimization scheme is matched and set according to the codon usage preferences of mammalian expression systems; the cell fermentation regulation parameters include culture environment osmotic pressure, feeding sequence, culture environment temperature, and culture environment pH; the chromatography purification process configuration includes affinity chromatography unit, ion exchange chromatography unit, hydrophobic chromatography unit, and ultrafiltration concentration unit.

[0041] Specifically, the formation of the process chain requires a qualified antibody sequence as the foundation. A closed-loop adaptive optimization algorithm is used to jointly calculate the deviation of antibody physicochemical values ​​and the deviation of production parameter ranges to obtain the feedback correction amount of design parameters and production parameters. A parameter feedback iterative mechanism is constructed to cycle through data output, numerical comparison, and parameter adjustment to reverse correct design parameters such as epitope screening judgment values ​​and antibody sequence modification values. Based on the codon usage preference matching of mammalian expression systems, a codon optimization scheme is set, and control parameters such as osmotic pressure, feeding sequence, temperature, and pH value of cell fermentation are set. Purification processes such as affinity chromatography, ion exchange chromatography, hydrophobic chromatography, and ultrafiltration concentration are configured to finally form a complete antibody preparation process chain.

[0042] The mathematical expression for the closed-loop adaptive tuning algorithm is: in, The value represents the parameter correction. This represents the value of the feedback control coefficient. This represents the value specified by the quality standard. This represents the overall evaluation score. Representative and A vector of parameter correlation coefficients of the same dimension. This represents the weighted value of the physicochemical deviation. This represents the deviation vector of physicochemical indicators. Represents the initial parameter set. This represents the corrected set of parameters.

[0043] For example, when forming the antibody preparation process chain, firstly, based on a qualified antibody sequence, the quality standard limit values, comprehensive evaluation score values, parameter correlation coefficient values, physicochemical deviation weight values, and physicochemical index deviation vector values ​​are substituted into a closed-loop adaptive optimization algorithm to calculate parameter correction values. Based on these parameter correction values, a parameter feedback iteration mechanism is constructed. This mechanism cyclically operates according to the order of data output, numerical comparison, and parameter adjustment, reversibly correcting design parameters such as epitope screening judgment values ​​and antibody sequence modification values. According to the codon usage preferences of mammalian expression systems, a codon optimization scheme for the antibody sequence is matched and set. Cell fermentation control parameters are set, including culture environment osmotic pressure, feed addition timing, culture environment temperature, and culture environment pH. Chromatographic purification processes are configured, sequentially setting up affinity chromatography units, ion exchange chromatography units, hydrophobic chromatography units, and ultrafiltration concentration units. Through the parameter feedback iteration mechanism, the design and production parameters are continuously optimized, ultimately forming a complete and stable antibody preparation process chain.

[0044] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for developing an AI-optimized, highly sensitive tumor detection reagent, characterized in that, The specific steps of this method are as follows: S1. Construct a dynamic database: Obtain the three-dimensional structure, protein conformational evolution trajectory, microstructure, binding activity, and biomanufacturing physicochemical parameters of tumor antigen-antibody complexes; use a dynamic feature coupling algorithm to calculate standardized calibration coefficients and association matching weights; complete classification and archiving; and form a dynamic database. S2, Training the model: Construct a spatiotemporal graph neural network model that includes graph convolutional layers, temporal learning layers, causal denoising layers and manufacturability evaluation modules. Use the spatiotemporal co-evaluation algorithm to calculate the model prediction error and parameter gradients. Use the full sample of the dynamic database from step S1 to iteratively train the model and calibrate the parameters. S3, Screening the optimal epitope combination: Input the wild-type and mutant sequences of the target tumor antigen into the model trained in step S2, use the spatiotemporal co-evaluation algorithm to calculate the epitope identification score, generate an epitope mutant library, perform multi-level screening on the library, and determine the optimal epitope combination based on the score; S4, Determine qualified antibody sequences: Based on the optimal epitope combination in step S3, the spatiotemporal collaborative evaluation algorithm is used to calculate the binding free energy score and physicochemical performance prediction value after antibody variable region design and humanization modification, complete the dynamic binding mode deduction, conduct physicochemical performance testing and compliance screening, and determine qualified antibody sequences. S5, Forming the process chain: Based on the qualified antibody sequence in step S4, a closed-loop adaptive optimization algorithm is used to calculate the feedback correction amount between antibody design parameters and production parameters, construct a parameter feedback iteration mechanism, reverse correct the design parameters, match the codon optimization scheme, cell fermentation regulation parameters and chromatography purification process configuration, and form the antibody preparation process chain.

2. The method for developing an AI-optimized high-sensitivity tumor detection reagent according to claim 1, characterized in that, S1 involves constructing a dynamic database that collects three-dimensional structures of tumor antigen-antibody complexes covering various tumor targets, including natural antibodies, humanized antibodies, and chimeric antibody combinations. Protein conformational evolution trajectories are collected based on continuous structural changes across different temperature, acid-base, ion concentration, and osmotic pressure ranges. Microstructure data includes hydrogen bond arrangement values, hydrophobic region area, surface charge distribution values, solvent-accessible region range, disulfide bond arrangement sites, and glycosylation distribution sites. Binding activity includes molecular binding constants and molecular dissociation constants. Biochemical parameters include protein solubility values, structural thermal response values, and charge distribution values.

3. The method for developing an AI-optimized high-sensitivity tumor detection reagent according to claim 1, characterized in that, In S1, the mathematical expression for the dynamic feature coupling algorithm in the dynamic database is: in, represents the comprehensive feature vector of the complex, and t represents the conformational evolution time parameter. Represents a vector of microenvironment parameters. Represents the dynamic weight values ​​of structural features. Represents the microstructure eigenvector. This represents the weighting of the microenvironment's influence. Represents the combination of active feature vectors, This represents a fixed weight value for manufacturing characteristics. Represents the physicochemical characteristic vector of biological manufacturing. This represents the corrected value for multi-source data.

4. The method for developing an AI-optimized high-sensitivity tumor detection reagent according to claim 1, characterized in that, In the S2 training model, the spatiotemporal graph neural network model includes a graph convolutional layer containing a residue node encoding unit and an atomic interaction edge weight calculation unit. It uses the amino acid residues of tumor antigens and antibodies as operation nodes and the atomic interaction relationships between residues as associated edges to complete the encoding operation of node features and edge weights. The temporal learning layer includes a temporal feature sequence receiving unit and an attention weight allocation unit, which are used to receive protein conformation feature sequences corresponding to different time nodes and assign corresponding attention weights to key conformation features. The causal noise reduction layer includes an environmental deviation extraction unit and a noise reduction calculation unit, which are used to collect characteristic deviation data generated by temperature and pH fluctuations and perform noise reduction calculations; the manufacturability evaluation module includes a physicochemical index input unit and a multi-dimensional numerical calculation unit, which are used to incorporate relevant values ​​of protein aggregation, protein hydrolysis tolerance, charge distribution, and structural thermal stability and complete comprehensive calculations; each layer and module is connected in sequence according to the order of feature input, calculation processing, and result output to form a complete spatiotemporal graph neural network model.

5. The method for developing an AI-optimized high-sensitivity tumor detection reagent according to claim 1, characterized in that, In S2, the mathematical expression of the spatiotemporal co-evaluation algorithm in the training model is: in, This represents the overall evaluation score. This represents the combined feature weight values. This represents the result of the spatiotemporal graph feature extraction operation. This represents the weighting value of the physicochemical evaluation. Represents a multi-dimensional physicochemical feature vector. This represents the quality correction value. This represents the environmental noise reduction coefficient value. This represents the sequence-specific correction value.

6. The method for developing an AI-optimized high-sensitivity tumor detection reagent according to claim 1, characterized in that, In step S2, during model training, when iteratively training the model and calibrating parameters using the full sample data from the dynamic database in step S1, all sample data in the dynamic database are first retrieved and divided into training sample set and validation sample set according to data category. Iterative training is carried out sequentially in a preset fixed batch. For each batch of training, the complex feature data, conformational evolution data, and physicochemical parameter data from the training sample set are first input into the spatiotemporal graph neural network model for computation. Then, the model's computational output results are compared with the actual sample values. Combined with the model prediction error and parameter gradient calculated by the spatiotemporal co-evaluation algorithm, the weight parameters of the model graph convolutional layer, temporal learning layer, causal denoising layer, and the computational parameters of the manufacturability evaluation module are initially adjusted. After completing a single batch of training, the validation sample set data is input into the model for computational verification. Based on the verification results, the relevant parameters of the model are fine-tuned again. The above batch training, parameter adjustment, and verification process is repeated until the model's computational error drops to a preset threshold, thus completing model training and parameter calibration.

7. The method for developing an AI-optimized high-sensitivity tumor detection reagent according to claim 1, characterized in that, In step S3, the input mutant sequences for screening the optimal epitope combination include missense mutation sequences, nonsense mutation sequences, frameshift mutation sequences, and fragment deletion mutation sequences. The epitope identification score is calculated based on a combination of sequence feature values ​​and spatial structure feature values. The epitope mutant library is constructed through amino acid site-directed substitution, amino acid fragment insertion, and amino acid fragment deletion. The multi-level screening is carried out in sequence based on structural feature values, molecular binding values, and physicochemical feature values.

8. The method for developing an AI-optimized high-sensitivity tumor detection reagent according to claim 1, characterized in that, In step S4, the qualified antibody sequence is determined to contain both heavy chain and light chain variable regions within its variable region; humanization modification involves adjusting the base and amino acid matching of the variable region framework sequence; calculations are performed based on the molecular binding interface forces using a free energy score; physicochemical performance predictions cover aggregation, hydrolysis sensitivity, charge shift, and heat tolerance values; dynamic binding mode deduction covers the spatial conformational changes throughout the molecular docking process; physicochemical performance testing includes solution environment tolerance testing, temperature gradient tolerance testing, and protease contact testing; and compliance screening sets judgment thresholds based on biopharmaceutical quality control standards.

9. The method for developing an AI-optimized high-sensitivity tumor detection reagent according to claim 1, characterized in that, In step S5, the mathematical expression for the closed-loop adaptive optimization algorithm in the process chain is: in, The value represents the parameter correction. This represents the value of the feedback control coefficient. This represents the value specified by the quality standard. This represents the overall evaluation score. Representative and A vector of parameter correlation coefficients of the same dimension. This represents the weighted value of the physicochemical deviation. This represents the deviation vector of physicochemical indicators. Represents the initial parameter set. This represents the corrected set of parameters.

10. The method for developing an AI-optimized high-sensitivity tumor detection reagent according to claim 1, characterized in that, In S5, the feedback correction amount in the process chain is jointly calculated based on the deviation of antibody physicochemical values ​​and the deviation of production parameter ranges; the parameter feedback iteration mechanism runs in a loop according to the order of data output, numerical comparison, and parameter adjustment; the designed parameter correction range includes epitope screening judgment values ​​and antibody sequence modification values; the codon optimization scheme is matched and set according to the codon usage preferences of mammalian expression systems; the cell fermentation regulation parameters include culture environment osmotic pressure, feeding sequence, culture environment temperature, and culture environment pH; the chromatography purification process configuration includes affinity chromatography unit, ion exchange chromatography unit, hydrophobic chromatography unit, and ultrafiltration concentration unit.

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