7 results about "Molecular Fingerprint" patented technology

The compound molecular fingerprint prediction belongs to the technical field of mass spectrum data analysis, and from the perspective of fully mining the implicit information of mass spectrum data, neutral loss information is added behind corresponding ion peaks when a vector representation of mass spectrum data is constructed, natural language processing technology is adopted to learn the relationship between peaks and between peaks and neutral losses in the mass spectrum, a multi-dimensional spectrum vector is constructed, and molecular fingerprints are predicted. Due to the addition of neutral loss of corresponding ion peaks in the spectrum vector, more abundant structural information is contained, and the accuracy of molecular fingerprint prediction can be effectively improved.

The application provides a cell-targeting drug primary screening model training method, a drug primary screening method and equipment. The method comprises the following steps: obtaining molecular fingerprint feature data corresponding to each compound sample with label information to form a corresponding original data set, performing data screening on the original data set by using a distance measurement method, and training a regression model based on a graph neural network to learn the structure topology of each compound sample. The model is trained as a cell-targeting drug primary screening model for predicting whether a compound has potential cell-targeting drug activity. The application can effectively improve the comprehensiveness of molecular representation during the training process of the cell-targeting drug primary screening model, effectively improve the generalization ability of the cell-targeting drug primary screening model obtained by training, effectively improve the applicability of the cell-targeting drug primary screening model obtained by training, and improve the accuracy and reliability of the prediction result of the potential cell-targeting drug activity predicted by the model.

The application discloses a molecular odor prediction method based on cross-modal attention fusion, relates to the fields of chemical informatics and deep learning, and comprises the following steps: acquiring a molecular dataset, extracting basic molecular property features, molecular fingerprint features and SMILES sequence features for each molecule in parallel, performing molecular odor prediction model training, wherein the molecular odor prediction model comprises a modal encoder, a cross-modal attention fusion module and a classification head connected in sequence, inputting the corresponding basic molecular property features, molecular fingerprint features and SMILES sequence features of a to-be-predicted molecule into the trained molecular odor prediction model to obtain prediction probabilities of corresponding odor categories, and determining an odor category prediction result of the to-be-predicted molecule based on the prediction probabilities. Through multi-modal feature fusion, an interpretable cross-modal attention and a gating mechanism, the method realizes more reasonable representation modeling of molecular odor, and effectively improves the accuracy of odor label prediction.

This invention discloses a high-throughput experimental sequence adaptive planning method based on Bayesian optimization, relating to the field of chemical experiment automation technology. The method includes: digitally encoding multiple chemical variables involved in the experiment to construct a multi-dimensional chemical space to be searched; constructing a probabilistic prediction surrogate model based on known experimental samples; pre-setting the acquisition cost of each variable and establishing a cost function; combining the expected improvement amount and the cost function, calculating the next batch of candidate experimental points through a target optimization algorithm, prioritizing the experimental points with the largest unit cost gain; converting the candidate coordinates into instructions and sending them to the automated experimental platform, and updating the surrogate model with the returned experimental data in real time, repeating the process until convergence to the global optimum or reaching the cost ceiling. This invention, by introducing a cost-sensitive acquisition function and a heterogeneous molecular fingerprint fusion strategy, achieves synergistic optimization of experimental results and experimental costs, significantly improving the efficiency and resource utilization of high-throughput experiments.

The application discloses a terahertz communication transmission method, system and device based on molecular fingerprints and a medium, and relates to the technical field of wireless communication. The method comprises the following steps: constructing a mapping relationship between the absorption spectrum feature vector of each spatial point medium molecule, the spatial point coordinate and the temperature and humidity information, and storing the mapping relationship in a fingerprint feature library; the transmitting end calls the absorption spectrum feature vector matched with the target spatial point, calculates the equivalent frequency offset of the corresponding signal working frequency band, and quantitatively encodes the equivalent frequency offset to generate a fingerprint key sequence; the to-be-sent data stream and the fingerprint key sequence are modulated respectively, a double-channel signal is obtained, and the double-channel signal is radiated to the receiving end; the receiving end calls the absorption spectrum feature vector matched with the receiving end from the fingerprint feature library according to the position point of the receiving end, and calculates the actual equivalent frequency offset; the fingerprint key signal is demodulated and decoded by using the actual equivalent frequency offset, the data signal is channel-decoded by using the fingerprint key sequence, and the original data stream is obtained.

The application discloses antibacterial application of repaglinide, relates to the technical field of medicines, and comprises prediction of antibacterial activity of a compound and evaluation of structural novelty of the compound, and specifically comprises the following steps: step 1, collecting and collating high-throughput data of antibacterial activity to form antibacterial activity benchmark data; step 2, generating Daylight molecular fingerprint features of benchmark compounds by using Pybel and PyDPI and constructing an activity prediction model; step 3, predicting and evaluating antibacterial activity and structural novelty of a to-be-tested compound by using the model and fmcsR; and step 4, experimentally verifying repaglinide with high potential. The application can provide important ideas and guidance for research and development of novel antibacterial drugs, and more importantly, provides a novel antibacterial active compound repaglinide with low toxicity to cope with the increasingly serious crisis of bacterial drug resistance.