58 results about "Molecular Fingerprinting" patented technology

The invention belongs to the field of bioinformatics, and relates to a molecular property prediction method based on multi-modal gating and comparative learning, which comprises the technologies of comparative learning, graph neural network, cross-modal alignment, gating attention and the like. Firstly, data standardization and graph construction are carried out, and molecular fingerprint embedding is extracted; secondly, a heterogeneous dual-channel graph coding architecture is adopted, one path captures atom short-range interaction through an attention mechanism, the other path integrates a molecular global structure and long-range dependence, and complementary molecular representation is generated; then, a cross-modal attention mechanism is introduced, bidirectional association of graph and fingerprint features is achieved, and modal weights are adaptively and dynamically distributed through a gating fusion module; and finally, a comparison pre-training strategy is adopted, a molecular graph and fingerprints are utilized to construct a sample pair, and discriminative molecular representation is learned on unlabeled data. According to the method, the accuracy of molecular property prediction is remarkably improved, and an efficient and reliable calculation tool is provided for virtual drug screening and lead compound optimization.

The invention discloses a dynamic optimization system and method for a compound essential oil ratio, and aims to solve the problems that the traditional technology depends on artificial experience, the optimization efficiency is low and the quality tracing is insufficient. According to the method, raw material molecular fingerprint data are collected in real time through a high-precision spectrum sensor array and a micro-fluidic chip, a production environment field domain is constructed in combination with a computational fluid mechanics model, a formula optimization problem is encoded into an Ising model by using a quantum annealing algorithm, and global search is realized through a quantum tunneling effect. The system integrates digital twin bodies, covers molecular dynamics simulation, phase equilibrium prediction and olfactory receptor activation probability simulation, and accurately maps a physical system state; a multi-modal data fusion network and a reinforcement learning framework are adopted to dynamically adjust formula parameters, and an NSGA-III algorithm is combined to optimize efficacy collaboration degree, cost and stability indexes. And the execution control module realizes nanoscale flow regulation through the piezoelectric micro-injection valve array, and realizes non-tampering evidence storage of formula parameters and quality data based on a block chain technology. Dynamic closed-loop optimization of the compound essential oil ratio is achieved, the production efficiency, the product consistency and the supply chain transparency are remarkably improved, and the method is suitable for the field of intelligent manufacturing of high-end essential oil.

The invention relates to the technical field of pharmaceutical knowledge maps, in particular to a pharmaceutical knowledge map construction method and system based on artificial intelligence, and the method comprises the following steps: querying and collecting a molecular structure of a drug and a corresponding target protein sequence through a database, and carrying out the numerical coding of the molecular structure data of the drug, molecular fingerprints and protein structural domain features are extracted, and a drug and protein feature set is formed by combining drug chemical attributes and protein sequence features. According to the invention, through accurate analysis of the molecular structure of the drug and the target protein sequence thereof, the innovative scheme significantly enhances the understanding of the interaction of the drug and the protein, so that researchers can directly extract key features from data and monitor the dynamic change of the drug effect, thereby not only accelerating the development process of the drug, but also improving the development efficiency of the drug. By dynamically tracking the interaction between the side effect of the medicine and the pathological characteristics, the scheme provides powerful data support for personalized medical treatment.

The invention relates to the technical field of molecular property prediction, in particular to a molecular property prediction method based on double-graph collaborative representation and cross-view feature fusion, which comprises the following steps: automatically identifying key functional groups in a molecular graph through a graph attention mechanism (GAT), and constructing a Motif graph to represent a local chemical environment of the molecular graph; the Motif graph can effectively capture local structure characteristics in molecules. The Motif graph and the molecular graph are respectively processed through GAT, information of global features and local features is respectively extracted, and the two features are fused through cross attention to obtain fused molecular graph features, so that more comprehensive molecular feature representation is obtained, and the comprehensiveness of molecular representation and the performance of the model are improved. Molecular fingerprint information is jointly processed by using a bidirectional gating cycle unit (Bi-GRU) and a multi-head attention network, the expression ability of the model to complex molecular fingerprint data is enhanced, and the accuracy of molecular property prediction and the generalization ability of the model are improved.

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 field of wastewater treatment and industrial automatic control, and particularly relates to an intelligent dosing control method based on water quality online monitoring. The method comprises the following steps: capturing a water body spectrum fingerprint spectrum by using a full-spectrum scanning module to obtain a characteristic signal sequence; a chemical informatics molecular algorithm is introduced for preprocessing and feature enhancement, and molecular fingerprint information is recognized; a convolutional neural network model is constructed to decouple chemical oxygen demand, ammonia nitrogen, total phosphorus and water toxicity indexes from the spectral signals, and multi-parameter parallel inversion is achieved; and based on the inversion index and the spectral dynamic trend, the dosage is calculated through an intelligent decision-making algorithm, and accurate dosing of the medicament is driven. According to the invention, through fusion of full spectrum scanning and deep learning, high-precision anti-interference water quality perception is realized, potential risks can be identified and preventive intervention can be carried out, and the intelligent level and economical efficiency of dosing control are improved.

The invention provides a pesticide advanced oxidative degradation efficiency prediction method and device based on molecular fingerprints and machine learning and a medium, and the method comprises the following steps: S1, collecting and preprocessing pesticide degradation data; s2, extracting an SMILES format of pesticide molecules, converting the SMILES format into Morgan and counting molecular fingerprints, and combining the Morgan and counting molecular fingerprints with degradation data to form a data set; s3, constructing an extreme gradient lifting model to predict degradation efficiency, and evaluating to obtain an optimal model; s4, identifying key variables through a Shapley value method and a partial dependency graph; and S5, optimizing reaction conditions by using a genetic algorithm-optimal model. According to the model, the advanced oxidative degradation rate is predicted through counting molecular fingerprints and an extreme gradient lifting algorithm, the precision of the model is evaluated by using R2 and RMSE, and feature analysis is performed through a Shapley value and a part of dependency graph. The method is low in cost, simple, convenient and rapid, saves manpower and material resources, is high in accuracy, and has both statistical significance and chemical significance.

The invention discloses a graphene metasurface broadband terahertz molecular fingerprint recognition sensor, which comprises a periodic symmetric graphene double-opening square ring resonator deposited on a silicon dioxide substrate, and a continuous adjustable Quasi-BIC formant is generated by adjusting the Fermi level of graphene. According to the sensor, the sensitivity of 427 GHz / RIU and the quality factor of 15.2 are realized, and the absorption peak intensities of lactose and tyrosine molecules with the thickness of 0.1 mu m are respectively enhanced by 763 times and 548 times. The invention provides a new normal form for ultrasensitive trace biomolecule detection, and has important application value in the fields of biomedical diagnosis, food safety, drug detection and the like.

The application discloses a high-sweetness-amino-acid-content Flammulina velutipes variety and a MNP molecular identification method and application thereof. The Flammulina velutipes variety has excellent properties, the cap is not easy to open, the content of sweet amino acids is high, the diversified market demand is met, the factory annual bottle cultivation is suitable, and the Flammulina velutipes variety has a good application and popularization prospect. The MNP molecular fingerprint of the Flammulina velutipes variety 'Shangyan A111' is comprehensive multiple amplification and sequencing technology, sequence analysis of all marker sites of multiple samples can be performed at one time, compared with ISSR, RAPD, SSR and other molecular markers and mushrooming test, the MNP molecular fingerprint has the advantages of high throughput, multi-target, high sensitivity and high precision. The MNP molecular fingerprint of the Flammulina velutipes variety 'Shangyan A111' has specificity and specificity in identifying the Flammulina velutipes strain 'Shangyan A111'.

The invention discloses a multi-label smell description prediction method, and relates to the field of compound smell prediction, and the method comprises the steps: obtaining compound identification information, molecular structure descriptors and smell label data, and constructing a multi-label smell data set; generating a molecular structure feature vector through a molecular fingerprint coding technology, and extracting a multi-dimensional descriptor reflecting the physicochemical properties of molecules; compressing the molecular fingerprint features to a low-dimensional space through a dimension reduction algorithm; performing unbalanced data processing on the training set, fusing the dimension-reduced molecular fingerprints with the molecular descriptors to form a joint feature matrix, and configuring a class weight balance mechanism and overfitting suppression parameters by adopting a multi-label classification architecture; independently optimizing a probability threshold for each odor label based on the verification set; and outputting a multi-odor label combination prediction result according to the target molecule identification information. According to the scheme, the multi-odor characteristics of the compound can be accurately depicted, and the combined recognition accuracy of the compound odor is remarkably improved.

The invention provides a molecular inverse synthesis path prediction method and system based on dynamic selective sampling, and the method comprises the steps: carrying out the preprocessing of a target molecule, and obtaining a standardized molecular fingerprint; inputting the molecular fingerprints into a dynamic selective sampling model to obtain a molecular inverse synthesis path; wherein the dynamic selective sampling model comprises an embedding layer, a sampling layer and a dynamic selective sampling layer, wherein the embedding layer is used for dividing molecules into a difference data set and an equilibrium data set based on molecular fingerprint coding and similarity calculation; the selection layer is used for adopting a depth-guided adaptive selection strategy for the difference data set and adopting a dynamic balance group selection strategy for the balance data set so as to select an optimal intermediate molecule; the expansion layer is used for expanding the optimal intermediate molecules; and the planning layer is used for judging whether molecules except the target molecules on the path are commercially available molecules or not. By selecting a depth-guided adaptive selection strategy or a dynamic balance group selection strategy, the path planning efficiency and success rate are improved, local optimum is avoided, and different synthesis scenes are adapted.

The application relates to an intelligent screening system of a mitochondrion effect molecule, a construction method and application thereof, and belongs to the technical field of molecular biology. The construction method of the intelligent screening system of the mitochondrion effect molecule comprises the following steps: 1, establishing a target protein library; 2, obtaining a data set of the mitochondrion effect molecule; 3, adopting Morgan molecular fingerprint to characterize the mitochondrion effect molecule in the data set, carrying out deduplication and decontamination processing, and then carrying out molecular similarity processing to obtain an input set of a model; and 4, taking accuracy and AUC value as evaluation indexes to construct a support vector machine model. The application utilizes the support vector machine model to carry out prediction in a large amount of molecular data set, and gives a molecule with a high probability score which may have an effective effect on mitochondria, the model is helpful for researchers in the field of mitochondria to reduce parameter adjustment time and improve work efficiency.

The present disclosure provides compositions comprising sequence specific endoribonuclease and methods of their use in RNA analysis, RNA synthesis and fingerprinting of RNA molecules. In particular the present disclosure relates to compositions and samples comprising isolated endoribonuclease of Type III toxin-antitoxin systems preferably endoribonucleases of subfamily CptN and subfamily TenpN.

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

The present invention relates to the technical field of molecular identification of plant varieties, and specifically to SSR molecular markers, molecular identification cards, and applications thereof for identifying thin-shelled pecan varieties. The SSR molecular markers for identifying thin-shelled pecan varieties provided by the present invention can achieve the identification of 36 major thin-shelled pecan varieties, have high accuracy and stability of discrimination results, are simple to operate, and require a short time for identification, which is convenient for promotion and application in practice. The present invention also establishes a variety-specific molecular fingerprint and its molecular identification card digital encoding, barcode, and QR code, which achieves effective differentiation of 36 major thin-shelled pecan varieties, providing an effective method for thin-shelled pecan variety identification, seedling purity detection, and variety tracing.

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

This application discloses a method for predicting the quantitative activity of endocrine disruptors, relating to the field of virtual screening of endocrine disruptors. The method includes: acquiring in vitro experimental data of nuclear receptors and removing duplicate data, as well as removing compound sets that do not contain the simplified molecular linear input canonical (SMILES) representation; using a molecular fingerprinting method to extract the primary, secondary, and tertiary structural features of the compounds. For each compound cluster, a quantitative prediction model based on machine learning or quantitative read-across is constructed to predict the quantitative activity value of the compound. Addressing the low efficiency of existing methods for predicting the quantitative activity of endocrine disruptors, this application extracts multi-level structural features of the compounds and constructs corresponding quantitative prediction models for compound structural clusters of different sizes. Through molecular docking and molecular dynamics simulations, the interaction mechanism between endocrine disruptors and nuclear receptors is studied from a structural biology perspective, thus improving efficiency.

The present disclosure provides compositions comprising sequence-specific endoribonucleases and methods for their use in RNA analysis, RNA synthesis, and fingerprinting of RNA molecules. In particular, the present disclosure relates to compositions and samples comprising ToxN endoribonucleases that recognize and cleave single-stranded RNA, as well as optimal conditions for obtaining cleavage.

This invention provides SSR molecular markers for Hibiscus rosa-sinus and their applications, belonging to the field of molecular marker technology. The invention comprises 15 molecular markers, with corresponding primer sequences shown in SEQ ID NO. 1~30. Testing revealed that this series of markers exhibits a 100% polymorphism rate, excellent polymorphism information content, marker index, and resolution, and strong genetic stability. The molecular marker primers of this invention can efficiently distinguish Hibiscus rosa-sinus germplasm materials, clearly revealing the population's genetic structure and phylogenetic relationships. They are suitable for Hibiscus rosa-sinus germplasm resource identification, genetic diversity analysis, molecular fingerprinting construction, and assisted breeding, possessing the characteristics of high specificity, high resolution, and wide applicability.

The invention belongs to the technical field of cyclopeptide drug screening, and particularly relates to a cyclopeptide drug auxiliary screening method based on multichannel feature fusion and a machine learning model. Comprising the following steps: collecting a plurality of cyclopeptide samples having a binding effect on the receptor protein as candidate samples, and additionally collecting a plurality of cyclopeptide samples having a target activity effect on the receptor protein as reference samples; respectively acquiring molecular fingerprints, two-dimensional structure information, physicochemical properties and ADMET properties of the reference sample and the candidate sample, and serially connecting and fusing to form a four-channel feature data set; and processing the candidate samples based on the reference samples by adopting a diclustering method or a machine learning method, and screening the candidate samples. According to the method, cyclic peptides are classified from multiple aspects of sequence-structure-biochemistry-toxicology, so that the problem of relatively low screening precision caused by single characteristics is avoided; corresponding auxiliary effects can be provided for new drug research and development and traditional drug effect optimization.

The invention relates to the cross technical field of artificial intelligence and bioinformatics, in particular to a multi-modal enzyme kinetic parameter prediction method of an adaptive protein language model. Aiming at the problems that an existing processing method is lack of enzymatic reaction dynamic mechanism modeling and insufficient in three-dimensional structure utilization, the invention provides the following technical scheme: step 1, acquiring and preprocessing multi-modal data; 2, protein language model and molecular fingerprint feature extraction; 3, carrying out substrate recognition feature fusion based on cross attention; step 4, extracting conformation adaptive features based on the hybrid expert network; 5, correcting enzyme-substrate distribution alignment characteristics; step 6, kinetic parameter regression prediction; step 7, constructing a multi-objective loss function; and step 8, model training and parameter optimization. Through an enzyme reaction bridging adapter and an enzyme-substrate distribution alignment technology, high-precision prediction of enzyme kinetic parameters is realized, and cross-data-set accuracy and robustness are remarkably improved.

The application provides a lake dissolved organic matter quantitative tracing method based on molecular fingerprint and application thereof, and relates to the technical field of environmental analysis and water ecological management. The method comprises the following steps: collecting end member samples such as lake water and sediments, surface soil, plant litter, chemical fertilizer, livestock and poultry manure, tail water of a sewage treatment plant, tail water of aquaculture, etc., and enriching DOM through solid phase extraction; then obtaining molecular formula data through mass spectrometry; screening stubborn inert molecules that satisfy H / C < 1.5, conversion times of 0 and exist only in a single end member, and verifying the ecological conservation thereof through -2 < betaNTI < 2 and -0.95 < RCBray < 0.95; adopting SHAP algorithm to optimize a high-contribution molecular feature subset, and inputting the high-contribution molecular feature subset into a MixSIAR model to output the contribution proportion of each end member and the 95% confidence interval. The method guarantees the reliability of the tracer from the mechanism, breaks through the bottleneck of spectral overlap, and realizes accurate and reliable quantitative disassembly of endogenous and exogenous DOM.

This invention belongs to the field of bioinformatics and relates to a molecular property prediction method based on multimodal gating and contrastive learning, including techniques such as contrastive learning, graph neural networks, cross-modal alignment, and gated attention. First, data standardization and graph construction are performed, and molecular fingerprint embeddings are extracted. Second, a heterogeneous dual-channel graph encoding architecture is adopted, with one channel capturing short-range atomic interactions through an attention mechanism, and the other integrating the global molecular structure and long-range dependencies to generate complementary molecular representations. Subsequently, a cross-modal attention mechanism is introduced to achieve bidirectional association between graph and fingerprint features, and modal weights are adaptively and dynamically allocated via a gated fusion module. Finally, a contrastive pre-training strategy is employed to construct sample pairs using the molecular graph and fingerprint, learning discriminative molecular representations on unlabeled data. This method significantly improves the accuracy of molecular property prediction, providing an efficient and reliable computational tool for virtual drug screening and lead compound optimization.

The invention relates to the field of artificial intelligence, in particular to a chemical reaction yield optimization method, system and device and a medium, and the method comprises the steps: obtaining a training sample and a yield label of a target chemical reaction; generating a molecular fingerprint of each training sample as an initial representation, and determining substructures contained in the training samples; the representation mapping model to be trained is trained with the yield label as supervision, the feature similarity after mapping is made to be consistent with the yield similarity, and a representation mapping model is obtained; mapping the initial representation through a representation mapping model to obtain a reaction implicit representation; inputting the reaction implicit representation into a yield prediction model to obtain a predicted yield; calculating the contribution degree of each substructure according to the influence of the corresponding codes of the covering substructures on the prediction yield; and optimizing the target chemical reaction according to the contribution degree. According to the present invention, the specific substructure capable of promoting or inhibiting the yield can be accurately positioned, and the favorable substructure is specifically introduced or the adverse substructure is avoided so as to improve the target chemical reaction yield.

The invention discloses a screening method for anti-pancreatic beta cell apoptosis compounds based on a classification model, and relates to the technical field of molecular feature recognition and calculation screening. Comprising the following steps: collecting structure and activity data of anti-pancreatic beta cell apoptosis compounds, dividing active compounds and inactive compounds, and constructing a training set and a test set; a classification model is constructed based on a machine learning algorithm and multiple molecular fingerprint types, a cross validation method is adopted to evaluate model performance for a training set, a test set is used to verify model prediction capability, an optimal classification model is screened based on optimized statistical parameters, and optimized molecular fingerprint features are identified as feature substructures; screening the natural product library by adopting a screening strategy of combining a plurality of feature substructures to obtain a candidate compound library, performing activity prediction on the candidate compound library, and determining candidate compounds; and determining the anti-pancreatic beta cell apoptosis activity to obtain the target compound. And the potential anti-pancreatic beta cell apoptosis compound can be quickly found in the natural product.

The invention discloses a method for predicting harm of a new pollutant PFAS to human health based on a large language model, and aims to solve the problems of single structural feature expression, lack of toxicological semantic fusion, insufficient multi-organ toxicity prediction capability and the like in the prior art. According to the method, PFAS multi-dimensional structure features are extracted through combination of a graph neural network and molecular fingerprints, toxicology literature semantic evidence is mined based on a large language model, structures and semantic evidence vectors are fused by adopting a Transform multi-task framework, a multi-label classification model covering six organ systems including the liver, the kidney and the nerves is constructed, the toxicity probability of each organ is output, and the toxicity probability of each organ is calculated. Five risk levels are divided, and interpretable results are provided in combination with structure fragments and literature evidence. According to the method, the accuracy and stability of toxicity prediction are improved, and PFAS substitute screening and health risk management and control are assisted.

The present invention relates to a multimodal non-invasive in vivo system and related method for the characterization of the tissue microenvironment. The system comprises: a multispectral imaging (MSI) camera for superficial spectral screening; a multispectral optoacoustic tomography (MSOT) module for dynamic vascular and perfusion analysis; a Mueller Matrix Polarimetry (MMP) module for extracellular matrix anisotropy assessment; a Raman spectroscopy module for molecular fingerprinting of fibroblast-associated proteins; an optical coherence tomography (OCT) module for morphological and stratigraphic evaluation; an optional Elastic Scattering Spectroscopy (ESS) module for subcellular scattering biomarkers; and a diachronic analysis module for longitudinal monitoring. Data are integrated by an artificial intelligence processing unit to generate quantitative biomarkers of vascularization, extracellular matrix features, immune activity (Langerhans cells) and fibroblast phenotype, distinguishing physiological myofibroblasts from CAF. The invention enables non-nvasive, biopsy-free evaluation of scars, melanocytic lesions, tumor stroma, wound healing and surgical site monitoring.

The invention provides a high-throughput molecule screening and molecule intelligent construction platform and a data processing method thereof, and relates to the technical field of chemical informatics and drug research and development, the high-throughput molecule screening and molecule intelligent construction platform comprises a multi-molecule batch operation module, a single-molecule editing module and a modular task combination engine, 34 core functions of molecular data cleaning, multi-dimensional screening, molecular fingerprint similarity analysis, similarity map screening based on MACCS fingerprints, structure replacement, molecular disassembly, atomic contribution analysis and the like are supported; according to the data processing method, a unique SMILES code is generated through standardized cleaning, in combination with screening conditions such as user-defined functional groups and molecular weights, a similarity matrix is calculated by utilizing topology / MACCS fingerprints, and a reference molecular structure is matched through a similarity map, so that high-precision screening is realized. The problems of limited application scene, poor database compatibility and complex operation in the prior art are solved, and the molecular screening efficiency and precision are remarkably improved.

The invention belongs to the field of vegetable molecular technology breeding, and particularly relates to a method for analyzing germplasm genetic diversity and constructing a fingerprint spectrum of balsam pear based on a simple sequence repeat (SSR) marker. By screening high-polymorphism SSR core markers, genetic diversity analysis is carried out on 26 germplasm parts of the balsam pear, and an SSR molecular fingerprint spectrum is constructed. Core SSR markers (MC0569594, MC0450530, MC0687314 and MC10146038) are used for carrying out PCR (Polymerase Chain Reaction) amplification and banding pattern analysis, the polymorphism information content (PIC) is calculated, and the germplasm of the balsam pear is clustered into three main groups through clustering analysis. By combining a fingerprint database, efficient identification and information storage of germplasm of the balsam pear are realized. The method is easy and convenient to operate and high in accuracy, and important technical support is provided for protection, utilization and breeding of germplasm resources of the balsam pear. By means of the method, the variety of the large-top bitter gourds on the market can be rapidly identified, materials with excellent characters can be effectively screened out in the breeding process, the breeding efficiency is improved, and the method has wide application prospects.

This invention discloses a dual-objective solvent screening paradigm based on machine learning, belonging to the fields of chemical engineering and machine learning technology. The paradigm includes the following steps: constructing a dual-objective solvent screening paradigm with solubility and flammability as screening objectives; collecting and organizing solubility data and flammability risk data; constructing differentiated feature engineering for solubility prediction and flammability prediction respectively; constructing a multi-dimensional hybrid feature set integrating molecular fingerprints, physicochemical descriptors, and temperature for solubility prediction, and using multi-dimensional molecular descriptors for flammability prediction; optimizing the model using different hyperparameter optimization methods for different prediction tasks; and employing a Pareto ranking mechanism to output a Pareto-optimal solvent set with the objectives of maximizing solubility and minimizing risk, thus achieving dual-objective collaborative screening. This method achieves accurate prediction of solubility and flammability, possesses strong adaptability and generalization ability, and provides an effective tool for high-throughput solvent screening in crystallization process design.