8 results about "Molecular Fingerprinting" patented technology

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.

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 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 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 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.

This invention discloses a method for predicting the function of bioactive peptides based on multi-view, multimodal representation learning. The method includes: extracting amino acid sequence information of peptides using multi-scale dilated convolutional CNN and bidirectional LSTM; extracting structural and functional features of peptide molecules using an ESM-2 model; processing molecular fingerprint information using convolutional CNN and Mamba structures; extracting topological information of the peptide molecular graph using traditional convolutional CNN, and processing node features using graph convolutional neural networks. All these multi-view features are ultimately fused into an aggregated feature representation, which is then passed through a fully connected layer and a sigmoid function is applied for multi-label classification. By concatenating and fusing the extracted features, a comprehensive peptide molecule feature representation is formed to predict various bioactive properties of the peptide. This multi-view, multimodal feature integration method not only enhances the model's predictive ability but also improves its flexibility and accuracy when processing complex biological data.

The present application relates to the technical field of pollutant screening. Disclosed are a method and system for identifying organic pollutants in soil on the basis of a combination of AI and high-throughput screening. The method comprises: extracting substance peaks from high-resolution mass spectrometry data of a soil sample; constructing an organic pollutant mass spectrometry database, extracting spectrum features and structural features of compounds from the organic pollutant mass spectrometry database, constructing a molecular fingerprint prediction model, and establishing a spectrum-to-structure mapping relationship; on the basis of the extracted substance peaks, constructing spectrum vectors to predict molecular fingerprints, and acquiring candidate chemical structures by searching the organic pollutant mass spectrometry database; and scoring the candidate chemical structures by means of the predicted molecular fingerprints, selecting chemical structures that meet a preset standard, constructing an identification basis on the basis of the selected chemical structures, and acquiring an organic pollutant identification result for the soil sample. In the present application, non-targeted intelligent analysis is performed by means of the synergistic integration of artificial intelligence and mass spectrometry analysis, thereby realizing the rapid and accurate identification of organic pollutants in soil.

The present application relates to the field of artificial intelligence, in particular to a chemical reaction yield optimization method, system, device and medium, comprising: obtaining training samples and yield labels of a target chemical reaction; generating molecular fingerprints of each training sample as initial representation, and determining substructures contained in the training samples; training a to-be-trained representation mapping model supervised by the yield labels, so that the similarity of the mapped features is consistent with the yield similarity, and obtaining a representation mapping model; mapping the initial representation to a reaction hidden representation through the representation mapping model; inputting the reaction hidden representation into a yield prediction model to obtain a predicted yield; calculating the contribution degree of each substructure by masking the influence of the corresponding code of the substructure on the predicted yield; and optimizing the target chemical reaction according to the contribution degree. The present application can accurately locate specific substructures that promote or inhibit the yield, and introduce beneficial substructures or avoid adverse substructures, thereby improving the yield of the target chemical reaction.