222 results about "Virtual screening" 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 hexapeptide with a hypoglycemic effect as well as a preparation method and application thereof, and belongs to the technical field of biological medicines. The amino acid sequence of the hexapeptide is Ile-Trp-Asp-Pro-His-Phe, and the amino acid sequence of the hexapeptide is as shown in the specification. The novel hexapeptide IWDPHF with prominent DPP-IV inhibition ability is successfully identified from mulberry leaf proteolysis products through liquid chromatography-mass spectrometry and a virtual screening method, the hexapeptide IWDPHF shows the blood glucose reducing effect superior to that of part of known peptides in an in-vitro enzyme activity inhibition experiment and a zebra fish hyperglycemia model, and no obvious side effect exists; the compound has a good potential of being developed into a hypoglycemic functional product or a drug lead compound. The invention further provides the mulberry leaf peptide with the peptide fragment IWDPHF, and the mulberry leaf peptide can be applied to preparation of drugs or food for reducing blood sugar. The invention provides a new scheme and theoretical basis for treatment of diabetes, and has good market prospect and application potential.

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.

The invention discloses a composite double-protein hypoglycemic peptide as well as a preparation method and application thereof, and belongs to the technical field of animal and plant source double-protein active peptides. The method comprises the following steps: performing computer simulation enzyme digestion and virtual screening by utilizing bioinformatics to determine a protein raw material and protease, performing enzymolysis on animal and plant double proteins (soybean protein and casein) serving as raw materials by adopting neutral protease, and performing separation, purification and structural identification, thereby obtaining the protein. The protein peptide fragment with high alpha-glucosidase inhibitory activity is obtained by virtually screening molecular docking, and the amino acid sequences of the protein peptide fragment are shown as SEQ ID No: 6, SEQ ID No: 9 and SEQ ID No: 24. The compound double-protein peptide sequence provided by the invention has an inhibiting effect on the activity of alpha-glucosidase, can be used for preventing and treating diabetes mellitus, and can be used for long-term health care or treatment of people with impaired glucose tolerance or diabetics as a functional food ingredient.

According to the screening method for the potential protein biomarkers and the drug targets of the gastric cancer, Mendel randomization analysis in a proteome range is adopted, the genetic causal relationship between circulating plasma protein and the risk of the gastric cancer is evaluated, and finally the remarkably related protein is identified. According to the screening method of the potential protein biomarker and the drug target of the gastric cancer, provided by the invention, the potential association between circulating plasma protein and the gastric cancer is systematically revealed by integrating Mendel randomization, single-cell RNA sequencing analysis, space transcriptome analysis, virtual drug screening, molecular docking, molecular dynamics simulation and other methods.

The invention relates to the field of drug discovery and delivery collaboration, in particular to an autonomous evolutionary drug discovery and delivery collaboration method and system based on a large language model. The method comprises the following steps: aiming at a given biological target three-dimensional structure, generating a candidate molecular library which is complementary with a target pocket and gives consideration to druggability through an SE (3) isovariant hybrid generation model; according to a two-stage funnel type high-throughput virtual screening process, screening out the molecule with the highest comprehensive potential from the candidate molecule library; generating a customized delivery scheme through a three-stage process; in a digital twinborn model for simulating a real in-vivo tumor microenvironment, a targeted delivery process of a drug and carrier complex is simulated, and efficiency is evaluated; atomic-scale performance confirmation is carried out on a medicine, carrier and target point ternary system through molecular dynamics simulation. The invention is suitable for drug research and development.

The invention discloses a generative adversarial network optimization method for small molecule drug virtual screening, and belongs to the field of computer-aided drug design. The method comprises the following steps: constructing a small molecule drug data set; building a GAN basic model comprising a generator and a discriminator; performing multi-objective optimization training (optimizing chemical effectiveness, combining affinity and structural diversity) on the model through a joint loss function; generating candidate molecules by using the optimized model, and screening through a threshold value; and carrying out molecular docking verification on the screening result and outputting a final result. The quality of generated molecules is improved through multi-objective optimization, the drug research and development cycle is shortened, and the method is suitable for efficiently screening potential drug molecules.

The invention provides an application of liquiritin in preparation of a PPAR gamma receptor partial agonist. Through a structure-based high-throughput virtual screening technology, it is found that liquiritin can be used as a partial agonist of a PPAR gamma receptor, and the relative activation efficiency of liquiritin is 35.9% of that of rosiglitazone; in-vitro experiments prove that liquiritin can remarkably promote glucose uptake and consumption of HepG2 cells and does not induce adipocyte differentiation; in-vivo experiments prove that liquiritin can effectively improve the blood glucose level of an insulin resistance mouse model induced by high fat diet and reduce the level of serum proinflammatory factors. Compared with thiazolidinedione compounds, liquiritin not only can relieve and treat insulin resistance or related metabolic diseases, but also does not cause adverse reactions such as weight gain, fat accumulation and myocardial hypertrophy, provides a new candidate compound for developing safe and effective anti-diabetic drugs, and has a wide application prospect.

Virtual screening methods are disclosed for predicting interacting compounds, from a chemical library, for proteins. The methods using an Extremely Randomized Trees (Extra Trees) machine learning model in which parent node(s) represent a decision in relation to a target-compound complex, based on a plurality of features corresponding to a protein target of the complex and a plurality of features corresponding to a compound of the complex, and leaf nodes classify the target-compound complex as either interacting or non- interacting. The Extra Trees model outputs a probability that target-compound complex is interacting, based on aggregated classifications from each decision tree. Applying the Extra Trees model to a further protein target and compounds produces a probability that each compound will form an interacting complex with the further protein target. The compounds are then ranked based on the respective probabilities, and highest probability compounds are then explored using a docking model.

The invention discloses a characterization optimization and soft label construction method for a drug-target interaction prediction model, and the method comprises the following steps: (1) constructing a molecular characterization space based on a Molformer pre-training model, carrying out the feature space similarity analysis of a negative sample through employing a K-Means clustering algorithm, and eliminating redundant samples through setting a dynamic threshold value, thereby achieving the equalization of positive and negative samples; (2) designing a soft label optimization mechanism based on a logarithmic function, converting a pKd value in a DAVIS data set into soft labels continuously distributed in a (0, 1) interval, and reserving binding strength gradient information to enhance the sensitivity of the model to a weak binding compound; and (3) integrating the above data enhancement strategies, and training a drug target interaction prediction model through a five-fold cross validation method to improve the model prediction performance. The method verifies the significant advantages of the method in relieving the problems of data offset and information loss, and also provides a high-robustness data processing framework for virtual drug screening of deep learning.

This invention relates to systems and methods for adaptive discovery and mixed-variable optimization of synthesizable microelectronic materials, and applications of the same. Specifically, an exemplary system includes a virtual screening (VS) module to extract information from literatures of a knowledge base by text mining, a ML-assisted conceptual exploration (CE) module to identify candidate material families for the specific class of compound materials based on the extracted information via a combination of ML models and to generate exogenous models of objective functions f(x, y) and constraint functions g(x, y), and an adaptive discovery (AD) engine to generate and optimize design of the newly discovered compound materials. The AD engine includes a mixed-variable ML module, a mixed-integer optimization (MIO) module, and a high-fidelity evaluation (HFE) module, which are iteratively and sequentially executed.

The application provides a drug virtual screening method and device based on deep learning, wherein the method comprises the following steps: inputting all candidate compound small molecules in a candidate molecule database into a pre-trained molecular encoder respectively to obtain molecular vectorization representation of the candidate compound small molecules; constructing an index structure corresponding to the candidate compound small molecules based on the molecular vectorization representation of the candidate compound small molecules; inputting a protein target to be matched into a pre-trained protein target encoder to obtain protein target vectorization representation corresponding to the protein target; and matching the protein target vectorization representation based on the index structure to obtain a target compound small molecule corresponding to the protein target. The method establishes a full-amount mapping function of the target and the molecule through a vector calculation mode, realizes high-throughput virtual screening in a second-level calculation time, realizes rapid virtual screening of a full-amount candidate molecule library, improves the precision of the virtual screening, and increases the possibility of drug discovery.

The invention discloses a mulberry leaf antihypertensive active peptide as well as a preparation method and application thereof. According to the method, mulberry leaves are used as raw materials to prepare mulberry leaf albumin, enzymolysis parameters of enzymolysis time, pH, enzyme-substrate ratio and substrate concentration are optimized through a single factor experiment and an orthogonal experiment, and optimal enzymolysis conditions for preparing the ACE inhibitory peptide crude product are provided; the high-activity ACE inhibitory peptide is identified through further purification, polypeptide sequence determination and virtual screening, the ACE inhibitory peptide has extremely high solubility and good ACE inhibitory activity, and the IC50 values of the two peptides VPSCFDLTGK and RLPDFHGL with the highest activity are 8.23 [mu] mol / L (8.765 [mu] g / mL) and 23.01 [mu] mol / L (23.58 [mu] g / mL) respectively. The active peptide can be used as an angiotensin converting enzyme inhibitory peptide, and achieves the purpose of reducing blood pressure by inhibiting ACE activity.

Embodiments of the present disclosure provide a molecular property prediction method, related device and medium. The method splits and recombines a first molecule in an unlabeled molecule dataset to obtain a new second molecule, determines a training sample based on the first molecule and the second molecule, and optimizes a molecular encoder using the training sample. Then, the molecular property prediction model based on the optimized molecular encoder is trained using a labeled molecule dataset to achieve accurate prediction of the molecular property. Embodiments of the present disclosure can fully utilize the substructure information inside the molecule to improve the accuracy and generalization ability of the prediction, optimize the molecular representation ability of the molecular encoder, and enable the target molecular encoder to better understand the combination relationship between the molecular fragments, thereby improving the representation quality and prediction accuracy of complex molecular structures. Embodiments of the present disclosure can be applied to drug discovery, material science, molecular virtual screening and other scenarios.

The application discloses a virtual screening method of a small-molecule compound targeting TLR4. The application screens potential compounds capable of combining with TLR4 through three-dimensional molecular docking, and preliminarily proves that three compounds, Z1410232649, F27210326 and HY-N0029, have better TLR4 inhibiting effects through experimental verification. Among them, Z1410232649 performs most outstandingly, can significantly inhibit the release of inflammatory factors IL-6, TNF-alpha and IFN-gamma induced by LPS, and can be used as a novel anti-inflammatory drug or immunotherapy drug targeting TLR4.

The application provides a kind of multimodal drug virtual screening method, comprising the following steps: selecting pre-trained graph transformer GT model, based on public data set, fine-tuning graph transformer GT model;Using active learning strategy to select molecules, and carrying out Vina-GPU+ docking, using molecular docking score to fine-tune graph transformer GT model;Using the biological activity experimental data of target target to fine-tune the model;Reasoning on pre-screening compound library molecules;According to molecular score, sorting, according to the needs of selecting the first N molecules as candidate compounds.The application greatly reduces the number of molecules that need to be docked when performing virtual screening on a large-scale compound library, shortens the time required for virtual screening, improves the accuracy of virtual screening, and significantly reduces the cost and time of drug development.

The invention discloses a urate oxidase mutant and application thereof in improving the thermal stability of urate oxidase, and belongs to the technical field of biology. The urate oxidase mutant disclosed by the invention is T68L / T75S / E222D / K299E, T68L / T75S / K299E or T75S / K299E, and the urate oxidase mutant disclosed by the invention is T68L / T75S / K299E. According to the method, the key sites influencing the thermal stability are mined in the modes of directed evolution, high-throughput screening, multiple virtual screening and the like, and the stability is improved through mutation. And finally, a plurality of key sites and mutants influencing the stability are excavated, and the thermal stability is improved.

The invention relates to the technical field of biological medicines, in particular to a preparation method of an inhibitor targeting a MAX-PD-L1 promoter specific binding motif and a double-target inhibitor, through ChIP-seq and site-directed mutagenesis experiments, a core binding motif of MAX and a PD-L1 promoter, such as 5 '-CAC [GA] TG-3', is defined, it is ensured that the inhibitor only targets a key site of MAX-PD-L1 interaction, and the activity of the MAX-PD-L1 promoter specific binding motif is improved. The off-target effect is avoided, and a high-specificity target spot is provided for subsequent inhibitor design. The cell permeability of the DNA aptamer screened based on the specific binding motif is improved after cholesterol modification, and the affinity of the DNA aptamer is obviously higher than that of a traditional antibody. A small molecule compound virtually screened through a molecular docking model is optimized through hydrogen bond and hydrophobic interaction, and then the binding affinity with MAX is improved. After treatment with the inhibitor, the combination inhibition rate of MAX and the PD-L1 promoter is high, the transcriptional activity of PD-L1 is obviously reduced, the killing rate of T cells to tumor cells is also improved, and immune escape is effectively blocked.

The invention relates to the technical field of deep learning, in particular to an application of a deep learning technology in drug research and development, and particularly relates to a virtual screening algorithm based on a gene expression profile and comparative learning, which comprises the following steps: on the basis of comparative learning, defining a drug and a matched expression profile as positive samples and other pairs in batches as negative samples; a double-feature encoder is adopted, and the cosine similarity is used as a scoring function to carry out similarity measurement. And the two loss function training models are combined, so that the performance of virtual drug screening is improved. In a word, the method marks an important step of virtual drug screening.

The invention provides an FGF23 small-molecule inhibitor and pharmaceutical application, and belongs to the technical field of biological medicine. According to the present invention, based on the FGF23-FGFR1c binding surface, computer virtual screening is performed in LC 50KLibrary and MCE library compound libraries to screen the small molecule D-gluconic acid capable of binding FGF23; the invention provides application of D-gluconic acid or salt of D-gluconic acid in preparation of an FGF23 inhibitor and application of D-gluconic acid or salt of D-gluconic acid in preparation of a medicine for treating diseases caused by FGF23 increase. The application proves that D-gluconic acid can inhibit an FGFR1 / ERK signal channel at the downstream of FGF23, is expected to provide a new treatment strategy for diseases caused by FGF23 rise, and has patent medicine potential.

The invention discloses a method for preparing a VA-ECMO lung injury treatment medicine by using ginkgolide A to regulate YARS1, and relates to the technical field of biological medicine, the method comprises the following steps: by using ginkgolide A as a YARS1 protein regulator, preparing the medicine for treating the VA-ECMO lung injury through virtual screening, binding affinity confirmation and cell efficacy confirmation; wherein the virtual screening is based on a protein structure model of YARS1, and bilobalide A with high affinity binding energy with YARS1 is screened out through molecular docking. It is proved that ginkgolide A can effectively improve the lung ventilation function by regulating YARS1, repair the alveolar epithelial barrier structure and inhibit the inflammatory oxidative stress reaction, and the lung injury treatment effect and clinical transformation safety under the support of VA-ECMO are improved.

The invention belongs to the technical field of medicinal chemistry, and particularly relates to a quinoxaline compound as well as a preparation method and application thereof. Specifically, the quinoxaline compound provided by the invention is screened by adopting virtual screening and pharmacophore modes, is novel in structure, has relatively good inhibitory activity on MELK, and solves the problem of insufficient development of an existing MELK inhibitor that the activity is not ideal enough, the chemical structure is not diversified enough and the like.

The invention discloses a macromolecular protein cutting method and system based on multi-source protein feature scoring, and a storage medium, and belongs to the field of computational biology and intelligent drug research and development. In order to solve the technical problem that macromolecular protein is difficult to directly input into an existing calculation model, an amino acid position cutting comprehensive score is obtained by obtaining multi-source feature data such as a protein disorder region and structural domain annotation, candidate cutting sites are screened and a final cutting scheme is determined in combination with double constraints of a structural domain hard boundary and fragment length, and the method is suitable for large-scale industrial production. And generating a protein fragment adaptive to downstream calculation. According to the method, the integrity of the protein structural domain is guaranteed, the cut fragment can be directly used as model input such as AlphaFold, the accuracy and stability of structural prediction and molecular docking are improved, the method is suitable for scenes such as virtual screening and computer-aided drug design, and the problem that an existing cutting method is lack of systematic consideration is solved.

The application provides a target-specific virtual screening method and system based on machine learning. Active molecules and inactive molecules are docked with multiple conformations of a target, and protein-ligand interaction features of the docked molecules and related features of the ligands are extracted as input features of a machine learning model. A scoring function model is constructed using multiple machine learning models, and based on the advantages of multiple target-specific scoring function models, an integrated target-specific scoring function model is finally obtained by combining a model integration method. The integrated model can more effectively process complex protein-ligand interaction data, has excellent prediction performance and virtual screening capability, provides more reliable prediction results, and improves the virtual screening capability.

The invention relates to the technical field of biology, in particular to a method for improving affinity of a nano antibody based on computer-aided design. According to the method, a series of bioinformatics tools for nano antibody affinity maturation are investigated and researched in literatures, and part of databases, servers and software are optimized to construct a set of computer-aided design (CAD) nano antibody affinity improvement method by evaluating the operability and the improvement effect of the tools. In order to verify the effectiveness and universality of the method, based on the method, a high-affinity mutant nano antibody is virtually screened out of an Anti-Nectin-4 camel source nano antibody NBNT-1 and an Anti-PD-L1 shark source nano antibody NBNT4 screened in a synthetic library through model construction, model evaluation, site prediction, molecular docking, structural analysis, mutation prediction and mutation evaluation. Traditional in-vitro affinity maturation methods, such as error-prone PCR, are low in screening efficiency, large in randomness introduced by mutation, long in experimental period, large in workload and difficult to accurately optimize the affinity of target molecules. According to the method, various defects of a traditional in-vitro affinity maturation method are overcome, and the success rate and efficiency of affinity maturation are improved.

The invention discloses a machine learning-based inhibitor drug screening method for targeting KRAS G12D mutation, and the method is used for predicting the binding capacity of candidate small molecules and KRAS G12D protein targets by constructing an integrated graph neural network (GNN) and molecular feature embedded deep learning model. Compared with a traditional virtual screening method, the method has remarkable advantages in the aspects of improving the recognition capacity of KRAS G12D mutation specific small molecules and reducing the false positive rate and has high application potential and industrial transformation value, and molecular dynamics simulation results of screened compounds show that compared with existing KRAS G12D mutation targeted drugs in research, the method has the advantages that the application potential and industrial transformation value of the screened compounds are greatly improved, and the application prospect of the KRAS G12D mutation targeted drugs is widened. The screened compound has a more stable binding trend with targeting protein in MD simulation, is expected to become a new KRAS G12D targeting inhibitor, and provides new possibility for treating pancreatic cancer.

The invention relates to a 4-hydroxy-5-(3-(4-methoxyphenyl)-5-methyl-1-phenyl-1H-pyrazol-4-yl) cyanobenzene compound and an application of the 4-hydroxy-5-(3-(4-methoxyphenyl)-5-methyl-1-phenyl-1H-pyrazol-4-yl) cyanobenzene compound in a medicine for treating diabetes mellitus. The compound as shown in the following formula (1) is obtained through virtual screening in combination with experimental verification, and the compound has remarkable alpha-glucosidase inhibitory activity. In-vitro and in-vivo experiments prove that the compound effectively reduces the generation of glucose and delays the digestion and decomposition of starch by inhibiting the activity of alpha-glucosidase. The invention discloses the compound and the application thereof in the field of preparation of anti-diabetic drugs for the first time, the alpha-glucosidase inhibitory activity of the compound is high, and a new direction is provided for development of diabetes treatment drugs. Formula (1) below

The application provides a HBV inhibitor screening method based on a molecule-gene interaction constraint graph convolution network. In view of the limitation of a traditional drug discovery method in processing complex biological data, the application is based on a constructed compound library verified by anti-HBV in-vitro activity, a plurality of gene targets associated with the corresponding compound and an interaction network thereof, a graph data processing capacity of a graph convolution network model is used, and a molecule-gene interaction constraint graph convolution network model is constructed. The model combines an interaction matrix of a target protein corresponding to the gene, a gene feature matrix and a compound activity label, and effectively predicts the biological activity category of the compound. The specific steps include data processing, graph data generation, graph convolution network model training, hyperparameter optimization and model evaluation. The model parameter AUC value is 0.97, and the model effect is good. The application provides a new path and idea for virtual screening of anti-HBV drugs, and has potential application value.

The invention discloses haliotis discus hannai I-type collagen peptide as well as a screening method and application thereof, and belongs to the technical field of bioactive peptides. The peptide sequences of the haliotis discus hannai I type collagen peptide are GAAGDK and DSQSAR; the screening method specifically comprises the following steps: (1) virtual enzymolysis of the haliotis discus hannai I-type collagen; (2) screening the bioactive peptide; and (3) molecular docking. According to the invention, through rational design and a virtual screening strategy, a human gastrointestinal tract digestion process is simulated to carry out targeted virtual enzymolysis, a multi-target molecular docking technology is combined, brand-new bifunctional peptides GAAGDK and DSQSAR are accurately predicted and screened from a specific collagen sequence, and aiming at a dual-action mechanism of AGE-RAGE axis and oxidative stress, the application has the advantages of high sensitivity, high sensitivity and high stability. The composition has remarkable synergistic interaction potential in the aspects of preventing diabetic complications and delaying skin and body senescence, and a brand new core raw material is provided for developing next-generation anti-saccharification functional food and skin care products.

The invention discloses a parameterized constraint system and method for efflux pump avoidance and off-target risk inhibition, which are applied to the field of biological medicines, and aims to solve the dual problem that toxic risks are caused by efflux pump mediated drug resistance enhancement and off-target combination of existing antibacterial drugs. The method comprises the following steps: constructing an efflux pump identification avoidance five-dimensional feature space and a target spot specificity geometric-electrical constraint boundary, performing structure mapping and deviation degree evaluation on candidate molecules, and triggering directional modification; the target matching is verified through molecular docking, and if the target does not reach the standard, the pharmacophore is locally adjusted; virtual screening is carried out based on an off-target protein database, a secondary structure disturbance mechanism is started for high-risk molecules, and a stereoisomerism or electrical reversal group is introduced to destroy non-target binding; according to the method, collaborative optimization of antibacterial efficacy, drug resistance avoidance and safety risk is achieved, and the research and development efficiency and druggability of candidate molecules are remarkably improved.