773results about "In silico combinatorial chemistry" patented technology

The invention relates to the technical field of drug research and development, and discloses a drug-like molecule screening system and method based on deep learning, and the system comprises a physical and chemical characteristic analysis module which is used for calculating molecular descriptors of compounds and generating a visual characteristic distribution chart; and the drug-like molecule screening module integrates a Lipinski rule, a Veber rule, a Ghose rule and a Muegge rule, and supports a user to customize a screening rule. Compound characteristics are comprehensively analyzed through the physical and chemical characteristic analysis module, flexible screening is achieved through the drug-like molecule screening module, toxicity risks are accurately recognized through the toxicity assessment module, the affinity assessment module is combined to assess the binding capacity in multiple ways, and the synthesis feasibility assessment module plans an efficient and low-cost synthesis path. The dynamic feedback optimization module continuously improves the system performance; all the modules work cooperatively, the problems of scattered tools, insufficient precision, low planning efficiency, optimization deficiency and the like in the prior art are effectively solved, the medicine research and development efficiency is greatly improved, and the research and development risk and cost are reduced.

The invention relates to a Bayesian optimization-based ruthenium, tin and indium doped lithium-rich manganese-based electrocatalyst screening method, electronic equipment and a storage medium, and the method comprises the following steps: generating a uniform ruthenium, tin and indium doped lithium-rich manganese-based material data set based on Dirac distribution, and generating a doped structure through element replacement; calculating hydroxyl adsorption energy and extracting descriptors to construct a data set to train a machine learning model; performing cell expansion operation on the initial electrocatalyst structure, predicting hydroxyl adsorption energy of each adsorption site, and mapping adsorption energy deviation into current density characterization component activity in combination with Boltzmann distribution; selecting an initial component based on the obtained component activity information, and constructing an agent model by utilizing Bayesian optimization; and determining a to-be-sampled component through an expectation improvement strategy, sampling and updating the agent model, and outputting an optimal doping proportion. Compared with the prior art, the method has the advantages that the ruthenium, tin and indium doped lithium-rich manganese-based electrocatalyst with excellent catalytic performance can be efficiently screened out, and the experiment cost is reduced.

The invention belongs to the technical field of drug screening, and provides a multi-target drug screening and optimizing method based on machine learning, which comprises the following steps: S1, carrying out feature screening on compound molecular descriptors by utilizing a machine learning algorithm, and determining the molecular descriptor which has the most significant influence on biological activity; s2, constructing a quantitative structure-activity relation model, and predicting the biological activity of the compound; s3, based on a multi-objective optimization algorithm, combining absorption, distribution, metabolism, excretion and toxicity properties of the compound; according to the method, feature screening is carried out on compound molecule descriptors by utilizing a machine learning algorithm, a QSAR model is constructed to predict the biological activity, ADMET property classification prediction is combined, the biological activity, ADMET properties and pharmacokinetic characteristics of drugs are comprehensively considered, and the accuracy and accuracy of drug activity prediction are improved. The method can efficiently and accurately screen and optimize drug candidate compounds with potential drug effects and good pharmacokinetic characteristics, so that the success rate of drug research and development is increased, and the research and development cost and time are reduced.

The invention discloses a multi-target blending optimization method for refined oil of a refining enterprise, and particularly relates to the technical field of target optimization. Constructing an original physical property feature set by identifying non-linear response sensitive variables in the blending component proportion; respectively constructing an oil product performance prediction model and a blending component interaction influence model based on the original physical property feature set; utilizing an oil product performance prediction model to analyze the influence of non-linear coupling of simultaneous change of a plurality of blending component proportions on the change trend of key performance indexes of the refined oil product; analyzing the influence of the proportion change of a single or individual blending component on the stability of the key performance index of the product oil by utilizing the blending component interaction influence model; and according to the key performance change trend characteristics and the fluctuation sensitivity indexes, constructing a constraint equation, generating a blending solution set, performing target performance achievement degree evaluation and global optimal solution screening, obtaining an optimal blending scheme, improving the blending prediction precision, and realizing stable optimization control of the product oil quality.

Provided are a new drug candidate discovery system and a computer program implementing a new drug candidate discovery platform. A new drug candidate discovery system may include an automatic data preprocessing module configured to receive a target protein information from a user through a web interface, and perform preprocessing on a protein structure file obtained based on the target protein information; a simulation setting module configured to predict an Enzymatically Active Pocket for Docking Calculation (EAPDC) from the protein structure file using an artificial intelligence language model, and determine a docking calculation site; and a docking simulation module configured to perform a docking simulation for the docking calculation site.

The invention provides a drug relocation method and a drug relocation system. The method comprises the following steps: predicting an expression profile of a drug after cell line disturbance according to a chemical structure of the drug, dose information of the drug and an undisturbed expression profile. And calculating the differential expression profile of the gene in the cell line based on the expression profiles before and after the cell line is disturbed by the drug. And for each drug, according to the differential expression profiles of the genes, calculating an average value of the differential expression profiles of the genes after the drugs disturb different cell lines, and sorting the genes based on the average value to obtain a sorting list of the differential expression profiles of the drugs. And according to the gene characteristics of the target disease and the sorting list, calculating the enrichment score of each drug on the target disease, and according to the enrichment score, evaluating the potential efficacy of the drug on the target disease. The drug relocation method provided by the invention can be used for giving disease specific gene characteristics for drug library screening.

The invention relates to the technical field of mineral processing data processing, in particular to a preparation method of a high-calcium fluorite and barite paragenic ore combined inhibitor, which comprises the following steps: collecting multi-source process mineralogy data output by an X-ray fluorescence spectrometer, an automatic mineral analyzer and the like, converting the multi-source process mineralogy data into a structured format, and constructing a process mineralogy database; based on historical data of a database, a random forest algorithm is adopted to train a correlation model, the correlation rule of mineral characteristics such as calcite content, fluorite particle size distribution and barite monomer dissociation degree and the inhibitor ratio is learned, and parameters of the optimization model are fed back through a flotation test. The combined inhibitor solves the problem of selective separation caused by similar surface properties through a synergistic effect of covering active sites of fluorite calcium by modified water glass, regulating active sites of barite barium by citric acid and assisting in enhancing inhibition by modified starch, and solves the problem of low efficiency of multi-source data integration through structured data processing and model closed-loop optimization; the flotation separation efficiency and the concentrate grade are improved.

The invention relates to the technical field of knowledge maps, in particular to a multi-task hepatitis B drug screening method and system based on knowledge map assistance, and the method comprises the following steps: based on hepatitis B virus genotype sequence data recorded along with time, patient drug use records and drug sensitivity. According to the method, virus genotype sequence data, patient drug use records, drug sensitivity and other multi-dimensional dynamic time sequence data are integrated, interaction events between entities are marked through timestamps, and virus variation tracks, drug curative effect changes and other dynamic characteristics are embedded into atlas node attributes; and the knowledge representation can reflect the time dependence in a real scene. A hyperedge connection multi-entity set is defined based on biological pathway annotation information and protein interaction data, the limitation that only a binary relation is supported in a traditional knowledge graph is expanded, a drug combination and multi-target synergistic effect mechanism is explicitly modeled, and misjudgment of the combination effect caused by simplification of the interaction relation is avoided.

The invention discloses a drug molecule optimization design method based on deep reinforcement learning and skeleton constraint, and belongs to the technical field of drug molecule optimization design, and the method comprises the steps: firstly building a molecule generation model based on deep learning, and training the molecule generation model in combination with data of an open source database; carrying out transfer learning on the pre-trained molecular generation model by utilizing a target attribute related compound database; on this basis, a strategy gradient algorithm is constructed, reinforcement learning based on target attributes is carried out in combination with a scoring model result, and skeleton constraints are added in a reinforcement learning sampling process to generate molecules with specific skeleton structures; and finally, comprehensively evaluating and screening the generated molecules by utilizing a multi-dimensional evaluation index, thereby identifying the patent medicine molecules with further research and development potential, rapidly obtaining molecular structures meeting target requirements, and greatly improving the efficiency of optimal design of the medicine molecules.

The invention provides a method for predicting a transcription response to disturbance of a novel drug, a virtual screening method based on the prediction method, and a virtual screening system based on the above methods. Wherein the prediction model comprises a disturbance adapter, a disturbance encoder and a disturbance decoder. The disturbance adapter is used for encoding the input chemical structures of one or more novel drugs and the dose information of the novel drugs into disturbance embedding; the disturbance encoder is used for mapping the influence of the disturbance of the novel medicine on the input undisturbed transcriptional spectrum to an interpretable potential space to obtain a potential representation of an undisturbed state; and a perturbation decoder for predicting a distribution of a transcriptional response of the novel drug perturbation based on the potential representation and perturbation embedding. And performing virtual screening according to the predicted transcription response of the novel drug, calculating the enrichment score of the candidate compound to the disease-sensitive drug, and recommending the compound according to the enrichment score. The flexibility and expansibility of the prediction model do not need priori knowledge or annotation.

Embodiments described herein relate to systems and methods for mapping molecules into interfaces. An example interface includes a visual interface having a map visualization. Example molecules include proteins, or any protein-like molecules or fragments thereof such as antibodies, antigens, proteins, lectins, receptors. Embodiments described herein relate to systems and methods for mapping molecules into interfaces by processing data using dimensionality reduction to generation representations of molecule maps (e.g. map visualizations).

The invention discloses an antibacterial cyclic peptide screening system and method based on a multi-modal cross attention mechanism, and belongs to the field of antibacterial cyclic peptide screening, the antibacterial cyclic peptide screening system comprises the antibacterial cyclic peptide screening system of the multi-modal cross attention mechanism, the antibacterial cyclic peptide screening system of the multi-modal cross attention mechanism comprises a multi-modal cross attention screening model and a multi-modal cross attention screening model, the method is used for screening antibacterial cyclic peptides; data of the data input module comprises a one-dimensional sequence, a two-dimensional structure and a three-dimensional structure, data sources in the data input module comprise but are not limited to APD3, DRAMP and a Un i Prot public database, and the data input module is used for conveying data of the antibacterial cyclic peptide; through cooperative use of the devices, the cross attention fusion module is arranged, and weights of different modals are dynamically allocated, so that a key active region of the antibacterial cyclic peptide is more obvious, and a multi-modal cross attention screening model can more accurately identify related characteristics of the antibacterial activity of the antibacterial cyclic peptide; the screening of the antibacterial cyclic peptide is more accurate.

Retention time drift caused by fluctuations in physical factors such as temperature ramping rate and carrier gas flow rate is ubiquitous in chromatographic measurements. Proper peak identification and alignment across different chromatograms is critical prior to any subsequent analysis. This work introduces a peak identification method called retention time trajectory (RTT) matching, which uses chromatographic retention times as the only input and identifies peaks associated with any subset of a predefined set of target compounds. RTT matching is also capable of reporting interferents. An RTT is a 2-dimensional (2D) curve formed uniquely by the retention times of the chromatographic peaks. The RTTs obtained from the chromatogram of a test sample and of pre-characterized library are matched and statistically compared. The best matched pair implies identification. Unlike most existing peak alignment methods, no mathematical warping or transformations are involved.

The invention discloses a screening method of fentanyl substances, and belongs to the technical field of detection and identification of fentanyl substances. Comprising the following steps: S1, acquiring m types of fentanyl and fentanyl substance samples, and cracking to obtain characteristic warning fragments; s2, establishing a feature warning fragment database; s3, the warning ion database is imported into TraceFinder software, after a sample to be screened is split, fragments and characteristic warning fragments are deduced through the TraceFinder software according to the fragmentation law and the characteristic theory of each type of fentanyl, and whether the fragments and the characteristic warning fragments are fentanyl substances or not is judged. According to the method, n characteristic warning fragments of fentanyl are obtained after high-resolution mass spectrum pyrolysis characteristics of m known typical fentanyl compounds are obtained, and the characteristic warning fragments are used for constructing a database, so that fentanyl substances can be quickly screened and identified.

The invention provides a method for screening activated carbon to catalyze chlorine oxidation to remove manganese, and belongs to the technical field of water treatment, catalysis and computational chemistry. The method for screening the activated carbon for catalytic manganese removal comprises the following steps: acquiring different physicochemical characteristic indexes of different activated carbon, wherein the physicochemical characteristic indexes are obtained by performing physicochemical characterization on the activated carbon; manganese removal efficiency indexes of different activated carbon are obtained, wherein the manganese removal efficiency indexes are obtained through tests of activated carbon catalytic oxychlorination manganese removal; performing correlation analysis on different physicochemical characteristic indexes and manganese removal efficiency indexes of different activated carbon, and determining a decisive index of activated carbon catalytic oxychlorination manganese removal according to a Pearson's correlation coefficient; performing linear regression fitting on at least one index in the decisive indexes and a manganese removal efficiency index to obtain a fitting coefficient; and screening out target activated carbon suitable for catalytic manganese removal according to the fitting coefficient, and predicting the catalytic oxychlorination manganese removal efficiency of the target activated carbon.

The invention relates to the field of pesticide composition screening, and discloses a stabilizer screening process for improving the low-temperature stability of a chlorantraniliprole and lufenuron suspending agent, which comprises the following steps: a) analyzing the low-temperature behavior of chlorantraniliprole and lufenuron based on molecular simulation, constructing a candidate stabilizer molecule library, and preliminarily screening candidate stabilizers; b) applying low-temperature stress to the chlorantraniliprole and lufenuron suspending agent sample containing the candidate stabilizer by using a microfluidic platform, and performing in-situ dynamic characterization to obtain dynamic data related to the low-temperature stability of the sample; and c) processing the dynamic data, and establishing an evaluation index representing the low-temperature stability performance of the candidate stabilizer. According to the invention, a cognitive bridge from a micromechanism to a mesoscopic phenomenon is constructed by integrating molecular simulation pre-screening and microfluidic high-throughput dynamic characterization. The problems of long research and development cycle and resource consumption caused by a large number of trial and error and experience judgment in a traditional screening method are effectively solved.

The invention provides an improved solidification scheme design method and system for a roadbed soil body, and relates to the field of soil body solidification, and the method comprises the steps: obtaining soil body solidification data, carrying out the expansion of the soil body solidification data through deep learning, and obtaining a solidification agent information database; screening in a curing agent information database according to the soil parameters to obtain a basic formula and corresponding strength data; constructing an initial relation equation based on the basic formula; coding level design of influence factors is carried out through central composite design, corresponding true values are obtained, and fitting is carried out on the initial relation equation to obtain an early strength equation and a late strength equation; and solving an optimal intersection of the solutions of the early strength equation and the late strength equation to obtain a target formula, and obtaining an improved curing scheme according to the target formula. According to the method, the optimal ratio of the raw materials is automatically calculated and output through deep learning and central composite design, so that intelligent soil curing agent formula design is realized.

Systems and methods for identifying compounds in a combinatorial synthesis library (CSL) having specific molecular properties is provided. The CSL is accessed using an auto¬ encoder comprising an encoder and a decoder. The encoder maps compounds in the CSL into latent codes in a learned latent space. The decoder retrieves molecular structures of compounds using such codes. A policy selects a batch of latent codes, from which a plurality of compounds is identified using the decoder. Molecular properties of these compounds are determined and used to determine a reward function value for each compound. A probability density, under a target distribution in which latent codes are sampled through the auto- encoder from the CSL with probability proportional to tempered reward function values, is determined using the reward function values. Policy parameters are updated using at least a difference between the probability density and the target distribution, using an a-divergence based objective function.

The invention belongs to the field of computational chemistry, and particularly relates to a multi-parameter collaborative dynamic screening method for adsorbed species on the surface of a catalyst. Comprising the following steps that an initial crystal structure is constructed and optimized through VESTA software, and a stable crystal structure model is obtained through density functional theory optimization; integrating working condition parameters such as a solvent, pH and potential, and drawing a Bayer diagram to label adsorption species and a mapping relation; and calculating a zero charge point and capacitance to obtain electrochemical parameters, and determining stable adsorption species under actual reaction through constant potential molecular dynamics simulation. According to the scheme, a deep structure-activity relationship between an active site structure and catalytic activity under a working condition is established through integration of all working condition parameters, labeling of adsorption species by a Boubye diagram, constant potential molecular dynamics simulation and the like; the problems that a systematic method combined with actual working condition parameters is lacked in prediction of the adsorbed substances on the surface of the catalyst, and real adsorbed species are difficult to accurately screen are solved, and accurate capture and efficient screening are achieved.

This disclosure describes novel methods and systems for identifying a ligand binding site (such as a cryptic ligand binding pocket) or ligand binding residues of a protein. The disclosed methods and systems employ "Efficient Sequence-based cryptic Pocket prediction" (ESP) leveraging advanced Protein Language Models (PLMs) and demonstrate significant improvement in predictive efficacy compared to existing machine learning (ML)-based cryptic pocket prediction methods. In the disclosed methods and systems, detection of cryptic pockets is achieved via training on readily available, non-cryptic pocket-specific data from a PDBBind dataset, rather than costly simulation and post-processing. Further, while SOTA's predictions often include positive signal broadly distributed over a target structure, the disclosed methods and systems produce more focused predictions and thus significantly increase downstream utility.

Known mass spectral data of a library of spectra corresponding to known compounds or known mass spectral data determined from a database of known compounds are compressed using a neural network encoder, producing a group of corresponding compressed known representations of known mass spectral data. Experimental mass spectral data of an experimental mass spectrum is compressed using the neural network encoder, producing a compressed experimental representation of the experimental mass spectral data. The experimental representation is compared to the group of known representations and each comparison is scored. At least one comparison with a score above a predetermined score threshold is selected. A known compound is determined from the selected at least one comparison. The known compound is identified as a compound of the experimental spectrum.

The invention discloses a virtual drug screening method and system based on adaptation during testing. The method comprises the following steps that a historical data set of protein pocket-small molecule pairing is collected; constructing a double-encoder framework consisting of a protein pocket encoder and a small molecule encoder; performing joint training on the double-encoder architecture based on the historical data set to obtain a small molecule screening model; and obtaining protein pocket data, and screening and sequencing the candidate small molecules corresponding to the protein pocket data based on the small molecule screening model to obtain corresponding active compounds. According to the method, the adaptation technology during testing is introduced into the virtual drug screening task for the first time, structural specificity dynamic adaptation of the model to an unknown sample is achieved on the premise of not depending on a test label, and the generalization performance is remarkably improved.

An automated chemical formulation apparatus includes: a data reception unit that receives an input chemical material dataset including chemical material information, chemical composition information, chemical formulation information and property information thereof; a predicting model generation unit that trains a first machine learning model using the input chemical material dataset to generate a predicting model for predicting a chemical formulation based on target property information of a target material; and a formulation prediction unit that sets a boundary condition based on the input chemical material dataset, generates a new input dataset including at least one of chemical material information, chemical composition information, and chemical formulation information within the boundary condition, inputs the new input dataset to the predicting model, and sets predetermined one or more pieces of target property information to perform prediction, thereby outputting a first group of chemical formulation data.

The invention discloses a high-entropy alloy multi-element combination screening method and system based on large language model (LLM)-genetic algorithm (GA) collaborative optimization and hybrid coding, and the system comprises a literature intelligent analysis module, a genetic algorithm optimization module and an experimental verification module, extracting element activity characteristics from the multi-source literature; a genetic algorithm (GA) optimization module adopts a binary-real number hybrid coding mechanism, and candidate metal combinations are generated through collaborative optimization of element type selection and proportional distribution; and the experiment verification module verifies optimization results in batches through a high-throughput experiment interface, and feeds back experiment data to the knowledge base and the algorithm parameter library. Through deep cooperation of the LLM and the genetic algorithm, the research and development period of a traditional high-entropy alloy is shortened to the hour level from the thousand level, and the design efficiency of a multi-element catalyst is remarkably improved.

The invention discloses a rigid chemical kinetic equation solving method based on a hard conservation constraint PINN model, and belongs to the technical field of chemical kinetic equation solving. According to the method, a hard conservation constraint strategy is introduced into the PINN, only calculation is carried out in the calculation process, s3 and s1 are indirectly deduced through a conservation expression, it is ensured that the PINN can conform to the solution of an equation set in the training process, meanwhile, the material conservation law is strictly obeyed, and the invariance of the material conservation quantity is ensured. Compared with a neural network model depending on pure data driving or a soft constraint mode adopted in a PINN, the method has the advantages that the prediction precision is improved on the basis of ensuring physical consistency, and the relative error between a prediction solution and a numerical solution is effectively reduced.

The invention relates to the technical field of machine learning and traditional Chinese medicine research and development, and discloses a traditional Chinese medicine compound screening method based on machine learning, which comprises the following steps: carrying out structure entropy pre-detection on an unknown compound to filter a sample with structure loss, projecting the sample to a structured feature space which is defined by a plurality of prototype prescriptions and has monarch, minister, assistant and guide roles, generating a prototype space projection vector; according to the method, a feature representation normal form is constructed, the internal compatibility of the compound is intelligently translated into structured input which can be understood by a machine learning model, and therefore the reliability of the compound is improved, the reliability of the compound is improved, and the reliability of the compound is improved. The classical example-based analogy interpretability is realized, meanwhile, the judgment capability of the screening system on the self-cognitive boundary is also improved, and the prescription innovation opportunity can be actively identified and protected.

The invention focuses on the field of cosmetic material screening, and particularly relates to an organic acid screening technology efficiently acting on skin targets. The method is characterized by comprising the following steps: firstly, locking a key skin target receptor according to a target skin care effect; then, pre-selecting potential small molecular organic acids based on the use frequency; then, precise docking of a receptor and organic acid ligands is realized by using an advanced molecular docking technology, and binding energy of all ligands and the receptor is calculated; finally, according to the binding energy, the most adaptive organic acid is efficiently screened out. According to the technology, through computer simulation, the binding mode and affinity of micromolecule and macromolecular targets are deeply predicted, and an efficient organic acid ligand screening method is successfully constructed. The method can quickly and accurately predict the interaction between the organic acid and the skin target spot, assists in accurately customizing the optimal organic acid formula according to different skin care requirements, and realizes the maximization of the skin care effect.

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

Compositions, methods, kits, systems, and software are provided for non-invasive prenatal testing for autosomal recessive diseases. Next generation sequencing is used to sequence maternal and fetal DNA isolated from maternal plasma by probe capture. The fetal fraction of the sequencing reads for DNA isolated from maternal plasma is estimated by counting single nucleotide polymorphisms (SNPs) for which an allele is detected that is present in the paternal haplotype but absent in the maternal haplotype, based on the assumption that SNPs having a paternal allele belong to the fetal DNA. The fetal fraction is bioinformatically enriched by excluding sequencing reads over a specified length via in-silico size selection, which increases fetal genotype prediction accuracy. Parental haplotype information together with the read ratios observed at the linked SNPs is used to predict the fetal genotype at a site of a mutation linked to the autosomal recessive disease.