160 results about "Drug design" patented technology

The invention provides an antibody drug conjugate property prediction method based on multi-modal fusion, and belongs to the field of bioinformatics. The method comprises the following steps: firstly, explicitly modeling sequence position information through sine position coding; secondly, introducing a bidirectional cross attention mechanism to establish interaction between a light chain and a heavy chain and alignment between an antigen and an antibody; thirdly, the integrated graph neural network reconstructs the adjacency relation according to the attention weight, and topological features are extracted; and finally, in combination with a double-stage self-adaptive refining module, two-stage treatment of alignment and refining is realized, and each modal feature contribution is adjusted in a self-adaptive manner. And meanwhile, the sequence robustness is improved through Mask perception feature extraction, and the interpretability analysis of the key binding sites is realized through the attention weight. The method can significantly improve the prediction accuracy, and can be widely applied to cancer targeted therapy and drug design optimization.

The invention discloses a deep learning prediction method fused with multi-modal features, which can accurately identify protein hidden binding sites in a ligand-free (apoo) state. The method comprises the following steps of: firstly, constructing a protein graph by taking residues as nodes and taking C alpha distance less than or equal to 14 as edges, wherein node feature sets comprise amino acid one-hot, secondary structures, atomic attributes, protein language model embedding and BLOSUM62 evolutionary information, and edge features comprise distance and angle similarity; then capturing three-dimensional geometric equivariant features by adopting an equivariant graph neural network (EGNN), and modeling a chemical topological relation by using a graph isomorphic network (GINE) with edge features; eGNN and GINE double-branch feature fusion and global dependence integration are realized through gating cross attention and gating multi-head attention; and finally, inputting the fusion features into a Kolmogorov-Arnold network (KAN) classifier, and predicting whether each residue belongs to a hidden binding site or not. The method can adapt to large-scale conformation change without coordinate alignment, AUC and F1 on a standard data set are remarkably superior to those of an existing method, high robustness and generalization are kept for multi-chain protein and complex conformation, and the method can be widely applied to drug target discovery and structure-driven drug design.

The invention discloses a multi-target drug molecule generation model construction method and a multi-target drug design method, and relates to computer drug design and bioinformatics. Determining a corresponding two-dimensional molecular map based on the SMILES sequence; the fully-connected pharmacophore diagram and the two-dimensional molecular diagram are used as input of a GatedGCN module, each atomic node in the two-dimensional molecular diagram is connected with all pharmacophore nodes in the fully-connected pharmacophore diagram in message transmission, and information exchange between different nodes is achieved; the masked SMILES sequence serves as the input of an encoder, and the decoder generates an SMILES sequence conforming to pharmacophore characteristics in an autoregression mode; freezing the network parameters of the GatedGCN module and the encoder; and training the multi-target drug molecule generation model through the elite multi-target molecule set, and reversely optimizing and updating decoder network parameters according to an output result. According to the method, the model fully learns the multi-target molecular structure characteristics, and the multi-target drug molecule generation accuracy is improved.

The invention provides a tissue microvessel analysis method and system for assisting drug design based on artificial intelligence. The method comprises the steps that two channel images of a blood vessel marking channel and a nano probe channel are collected; carrying out preprocessing, registration and feature fusion on the two channel images to obtain a fusion feature tensor for simultaneously coding a blood vessel-nanoparticle space coupling relationship; extracting multi-scale structure information based on the fusion feature tensor to obtain an enhanced feature tensor; constructing double models based on a U-Net + + architecture, and combining the double models with an enhanced feature tensor to obtain a blood vessel mask, a nanoprobe mask and a preprocessed probe intensity graph; and obtaining a permeability index based on the blood vessel mask, the nano probe mask and the preprocessed probe intensity graph, and obtaining a blood vessel typing and drug recommendation design strategy by adopting a clustering and random forest regression algorithm. According to the method, the quantitative analysis precision and efficiency of the vascular permeability are remarkably improved, intelligent linkage of image analysis and a nano-drug design strategy is realized for the first time, and the method has good application potential.

The invention discloses a drug target prediction method based on fragment-level local and global feature fusion, and belongs to the technical field of computational biology and artificial intelligence drug design. Comprising the following steps: acquiring a medicine SMILES character string and a protein amino acid sequence; respectively segmenting the drug SMILES character string and the protein amino acid sequence to obtain a drug structure fragment sequence and a protein functional fragment sequence; and inputting the drug structure fragment sequence and the protein function fragment sequence into a pre-trained drug-target interaction prediction model to obtain a prediction probability of drug-target pair interaction. Compared with the prior art, the method has the advantages that convolution feature extraction, a multi-head attention mechanism and a gating fusion strategy are combined, an end-to-end DTI prediction framework is constructed, and the interaction between drugs and targets can be comprehensively mined.

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.

A method for computational drug design includes defining a population of a plurality of compounds. Each compound includes one or more molecular properties. The method includes defining a training set of compounds from the population for which one or more biological properties are known. The method includes selecting, from the population, a subset of one or more compounds that are not in the training set. The method includes determining a subset score of the selected subset based on molecular properties of the one or more compounds in the selected subset, and evaluating the selected subset based on the determined subset score. The subset score is determined based on a frequency of the molecular properties in the population and on a frequency of the molecular properties in a sampled set comprising the training set and the selected subset.

Embodiments of this disclosure provide a method and apparatus for drug design, a device, a medium, and a program product. The method for drug design includes: obtaining protein data representing a three-dimensional structure of a protein and initial molecule data representing an initial molecule to be bound to the three-dimensional structure of the protein. The method further includes: determining first molecular fragment data representing a first molecular fragment in the initial molecule based on the protein data and the initial molecule data. Generating target molecule data representing a target molecule based on the first molecular fragment data and the initial molecule data. A molecular fragment is automatically determined in the initial molecule, and the initial molecule is optimized based on the determined molecular fragment, such that fragment-based artificial intelligence optimization of a drug molecule can be implemented in a targeted manner, thereby reducing time and labor costs of drug discovery.

The application discloses a candidate drug ranking method and system based on topological coding supervision reconstruction, and belongs to the field of bioinformatics. The method comprises the following steps: step S1, obtaining drug design data; step S2, constructing a drug-biological entity association matrix; step S3, calculating a drug side topological matrix for describing the connection relationship between drugs and a biological entity side topological matrix for describing the connection relationship between biological entities according to the association matrix; step S4, constructing a finite-order topological filter and a spectral stable topological filter to form a bidirectional topological evidence field; step S5, obtaining a reconstruction supervision matrix through a supervised reconstruction objective function; and step S6, obtaining candidate drug ranking based on the reconstruction supervision matrix. The application can improve the high-order mechanism recognition ability, robustness and interpretability in drug-target prediction, drug repositioning, candidate molecule screening and molecular generation result ranking.

The invention discloses a design method and application of a tyrosinase (TYR) responsive drug for targeted therapy of melanoma, and belongs to the technical field of biological medicines. Based on the biological mechanism that TYR catalyzes tyrosine oxidation, a self-degradation connecting arm drug release strategy is combined, a similar 3-hydroxybenzyl alcohol structure is introduced to simulate a tyrosine substrate, specific recognition and activation of TYR on the drugs are achieved, and the problems that traditional chemotherapeutic drugs are poor in targeting property and serious in adverse reaction are hopefully solved.

The application relates to the technical field of computer-aided drug design, and provides a molecule generation method, system, medium and equipment for drug discovery, which comprises the following steps: acquiring a SMILES string of a ligand and an amino acid sequence of a target protein, respectively extracting a ligand feature vector and a protein feature vector, splicing and fusing, and then predicting a binding affinity value through a multilayer perception machine; taking the protein feature vector as a condition, generating a new molecular potential representation through a reverse denoising process of a conditional diffusion model; wherein, at each step of the reverse denoising process, a graph-level readout operation is performed on pure noise, an affinity value is predicted through the multilayer perception machine, a gradient of an affinity guidance loss is returned to a noise prediction network, and the new molecule generation is guided to a high affinity area; and the new molecular potential representation is decoded into a SMILES string through a pre-trained molecular language decoder. Novel molecules with high binding potential can be quickly generated.

Provided is the synthesis of 17 novel neutral sp2-sp3 diboron compounds. Diboron reagents are widely utilized as borylation agents for the incorporation of the boron moiety into organic molecules. The incorporation of boron into organic molecules has significant implications in biomedicine and material science. Boron-containing compounds are pivotal in drug design and the creation of novel materials with enhanced properties. The neutral sp2-sp3 diboron compounds are unsymmetrical diborylation agents. These reagents are expected to facilitate the exploration of novel borylation methodologies and potential synthetic applications.

This invention relates to the field of medical data and discloses a method for real-time monitoring of the efficacy of traditional Chinese medicine, Mongolian medicine, and Western medicine based on biosensor technology. This method enables real-time monitoring of drug efficacy and personalized medication guidance. The process includes collecting real-time multimodal biosignals from the user's body surface or minimally invasive body fluid samples to obtain raw sensor signals and acquire sensor array output; extracting real-time pharmacokinetic characteristic signals based on a preset pharmacokinetic model and mapping them to efficacy assessment parameters according to drug type; comparing the efficacy assessment parameters with a preset safe and effective concentration range to generate real-time medication status indicators and dosage adjustment prompts. This invention designs quantitative assessment methods for different types of drugs, enabling real-time comparison to generate efficacy status codes and dosage adjustment suggestions. It comprehensively generates a medication guidance report and transmits it in real-time to the user or clinical monitoring end, allowing for immediate adjustment of the medication regimen and ensuring safe and effective medication.

This application provides a method for screening inhibitors targeting CYP7A1 and their use in the preparation of anti-liver cancer drugs, relating to the field of tumor targeted drug design technology. The method for screening inhibitors targeting CYP7A1 protein includes: using the crystal structure of human cholesterol 7α-hydroxylase as a receptor model, precise docking screening is performed using software to screen drug-like molecules with molecular weights of 250-500 Da and lipid-water partition coefficients of -1 to 5 from a compound database; a graph neural network active learning model is used to predict and rank the protein-small molecule binding affinity of compounds in the database; the ΔG between candidate molecules and CYP7A1 is calculated using molecular mechanics / generalized Born surface area methods, with molecules having ΔG ≤ -40 kcal / mol considered potential inhibitors. The inhibitors of this application do not rely on the enzymatic activity of CYP7A1 to achieve anti-tumor effects and do not significantly affect cholesterol and bile acid metabolism levels.

The invention relates to the technical field of pharmacy, in particular to a dibenzyl butane type lignan LCA analogue as well as a preparation method and application thereof. Based on the structural characteristics of a JAK1 inhibitor, LCA is taken as a skeleton, druggability optimization and a direct drug design and structure simplification strategy based on a receptor structure are comprehensively utilized, a nitrogen-containing heterocyclic ring and a nitrogen-containing functional group are introduced into a side chain, systematic structural modification and simplification are carried out, and a series of novel LCA derivatives are designed and synthesized. The invention aims to obtain an anti-RA JAK1 inhibition candidate compound with higher activity, higher selectivity and potential.

The invention provides an application of 2-indolecarboxylic acid in preparation of a tumor targeted therapy drug. Through computer-aided drug design and molecular docking optimization, 2-indolecarboxylic acid shows high binding energy to an HER2 kinase structural domain. In HER2 positive NCI-N87 gastric cancer cells, the 2-indolecarboxylic acid significantly inhibits cell proliferation and induces G0 / G1 phase arrest and apoptosis. The mechanism research shows that the 2-indoleformic acid can play a role by regulating and controlling the HER2 / Akt / beta-catenin pathway (Western blot verification). In an NCI-N87 cell tumor-bearing nude mouse model, 2-indolecarboxylic acid (15 mg / kg) significantly inhibits tumor growth, and does not cause obvious weight loss or organ toxicity. Immunohistochemical analysis shows that the positive rate of a tumor tissue proliferation marker Ki-67 in a treatment group is remarkably reduced, and apoptosis detection shows that the proportion of TUNEL positive cells is remarkably increased, which indicates that 2-indolecarboxylic acid inhibits tumor cell proliferation and promotes apoptosis at the same time. The invention provides a lead compound with clinical transformation potential for HER2 targeted therapy.

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 belongs to the technical field of pesticide synergists, and discloses a use of an amide compound as a pesticide synergist. The application takes CYP6AE48 of Spodoptera litura as a main target, adopts a method combining computer-aided drug design technology and biological effect screening, and screens an amide compound, which is N-{5-[2-(2-{3-[2-(2-methylphenoxy)ethoxy]benzyl}hydrazino)-2-oxoethyl]-1,3,4-thiadiazol-2-yl}benzamide. It is known through experiments that the compound has a good inhibitory effect on CYP6AE48 of Spodoptera litura, can prevent degradation of lambda-cyhalothrin and chlorantraniliprole, and can play a synergistic effect as a pesticide synergist in pest control.

The present disclosure provides a polypeptide generation method, device, electronic equipment and storage medium, relates to the technical field of artificial intelligence, in particular to biological computing technology. The method comprises the following steps: obtaining a protein target and a reference polypeptide corresponding to the protein target, wherein the protein target is a protein causing pathological changes; generating a candidate polypeptide corresponding to the reference polypeptide; and inputting the protein target and the candidate polypeptide into an affinity evaluation module for screening to obtain a target polypeptide, wherein the affinity evaluation module comprises at least two levels of evaluation sub-modules arranged in a preset evaluation order, and the later the evaluation sub-module in the evaluation order, the higher the accuracy of the evaluation sub-module. The present disclosure can balance the accuracy and efficiency, and improve the "performance-price ratio" of the polypeptide drug design process.

The invention discloses application of 20-Deacetyltaxupine X in preparation of a medicine for preventing and / or treating amyotrophic lateral sclerosis, and belongs to the technical field of medicinal chemistry. The novel serine-threonine kinase CDC7 inhibitor and CDC7 form a new hydrogen bond, and guidance is provided for design of CDC7 inhibitor drugs. The novel serine-threonine kinase CDC7 inhibitor has a protection effect on an amyotrophic lateral sclerosis cell model, and can be used as an amyotrophic lateral sclerosis resisting medicine. The novel serine-threonine kinase CDC7 inhibitor has a protective effect by inhibiting apoptosis of an amyotrophic lateral sclerotic cell model.

The invention belongs to the technical field of new applications of medicines, and particularly relates to an aniline pyridine TEAD degradation agent, a preparation method thereof and an application of the degradation agent in preparation of antitumor drugs. According to the research, a novel aniline pyridine TEAD degradation agent with a clear structure is designed and synthesized by coupling a TEAD palmitoylation inhibitor niflumic acid serving as a lead compound with a CRBN type E3 ligase ligand through connecting chains with different lengths and different types by virtue of a computer-aided drug design means. The influence of the optimal compound (XY-3L) on cancer cell proliferation and cell functions is detected in an in-vitro cell experiment, and the curative effect of the optimal compound (XY-3L) in a melanoma lung metastasis model is explored in vivo.

Methods for determining a representation of a protein complex, given a constituent target complex of that protein complex are presented; where the constituent target complex is a single entity constituent or subcomplex of the protein complex; and wherein a protein complex is a complex of some combination of one or more of proteins, nucleic acids, metal ions, and small molecules. A recursive neural network is devised, wherein for each iteration of the recursion, a representation of the output constituent of the protein complex together with the input constituent target complex is passed into the neural network as input for the next iteration. Some embodiments of the invention include design and manufacturing of effective synthetic biologic drugs, monoclonal antibody (mAb) drug, Antibody Drug Conjugate (ADC), peptide ligand drug, and small molecule drugs (SMDs).

The invention discloses an anti-tumor compound as well as a preparation method and application thereof, belongs to the technical field of medicines, and particularly relates to a compound with an anti-tumor activity and a specific chemical structure. The compound is a 6 '-phenyl-2' H-spiro [cyclohexane-1, 1 '-isoquinoline]-3' (4 'H)-ketone compound, the structure of the compound is shown as a general formula (I) or (II), or R is a hydrogen atom, a chlorine atom, a methoxyl group or a fluorine atom monosubstituted at different positions on a benzene ring. According to a tumor cell line test, namely human diffuse large B lymphoma cells, it is proved that the compound has the effect of inhibiting tumor activity. The raw materials of the compound are easy to obtain, and experiments prove that the compound has a good anti-cancer effect and has a good application prospect in the field of design, research and development of anti-tumor drugs.

A chemical reaction energy barrier and transition state combined prediction method based on a bimodal reaction characterization architecture and a diffusion model comprises the following steps: generating standard input data containing three-dimensional geometric information and an atomic mapping alignment relationship through an automatic data standardization preprocessing module; a bimodal reaction representation skeleton is constructed, the skeleton enhances perception of three-dimensional geometric information by using an isovariant graph encoder, and global chemical context information is captured by using a self-supervised pre-trained sequence encoder; through a cross-modal alignment fusion module, the two kinds of modal information are efficiently fused to form a unified reaction expression; based on the unified representation, high-precision chemical reaction energy barrier prediction is performed through a downstream regression prediction network, and a transition state atomic coordinate structure is generated by using an isovariant diffusion model. According to the method, the generalization ability of the model on a small-scale transition state data set is effectively improved, and an efficient and accurate prediction tool is provided for computational chemistry and drug design.

The invention discloses a drug design strategy for releasing hydrogen sulfide based on enzymolysis of thiocarbonyl and application of the drug design strategy in indole parent nucleus drug modification, a hydrogen sulfide prodrug is characterized in that an existing drug with a carboxylic acid structure is vulcanized to form a thiocarboxylic acid structure, and the thiocarboxylic acid structure can release H2S under an enzyme catalysis condition; thiocarboxylic acid can be hydrolyzed by esterase and released to obtain hydrogen sulfide, toxic and side effects of existing drugs are improved by means of anti-inflammation, myocardial protection, gastrointestinal tract protection and the like of hydrogen sulfide, physiological functions of hydrogen sulfide are exerted at the same time, and clinical application of hydrogen sulfide is achieved.

The application discloses a drug design method and device based on topological data analysis, equipment and medium, relates to the technical field of computer-aided drug design, and the method comprises the steps of obtaining a plurality of three-dimensional structure conformations of a target protein, determining the binding pocket region of each three-dimensional structure conformation, and constructing a conformation set; calculating a topological descriptor representing the topological relationship between atoms based on the atomic coordinates of the binding pocket region of each three-dimensional structure conformation, and converting the topological descriptor into a corresponding conformation feature vector; performing a pooling operation on the conformation feature vector corresponding to each three-dimensional structure conformation to generate a unified topological feature representation representing the common topological feature mode of the target protein binding pocket under a plurality of three-dimensional structure conformations; and screening or generating candidate drugs based on the unified topological feature representation. The common topological invariant information of a plurality of conformations is used to represent the flexible protein binding pocket, the screening omission caused by single conformation representation is reduced, and the representation accuracy and the calculation efficiency are considered.

The invention discloses application of a triple-helix structural element in RNA (Ribonucleic Acid) drug design, and belongs to the field of biological medicines. According to the application, an ENE element or a mutant of the ENE element is used as a nucleic acid stabilizing element, and the interaction between 3 'UTR and poly (A) is optimized, so that the mRNA stability is enhanced, and the protein translation efficiency is further improved, and therefore, the problem of low protein expression quantity in RNA therapy is effectively solved. The mRNA sequence optimization strategy provided by the invention provides a new method for vaccine design in the field of infectious disease and tumor immunotherapy, and has a wide application prospect.

The invention discloses a molecular fragment library based on protein binding site properties and a construction method and application thereof, and belongs to the field of computer-aided drug design. The construction method comprises the following steps: acquiring three-dimensional structure data of a protein-ligand compound, and preprocessing to obtain standardized protein binding sites and ligand molecules; cutting the standardized ligand molecules by adopting an iterative molecular cutting algorithm to generate molecular fragments; identifying and quantifying an interaction between the molecular fragment and an amino acid residue in the normalized protein binding site; and associating the molecular fragments, the information of the amino acid residues interacting with the molecular fragments and the corresponding interaction modes, and constructing a molecular fragment library. The interaction mode of molecular fragments and specific amino acid residues is labeled, so that the targeting property of the fragment library is improved; and the candidate fragments with specific interaction potential can be quickly positioned, so that the drug design efficiency is improved.

The invention provides a structure-based property distribution external targeting drug design method and system, and relates to the technical field of drug design. According to the structure-based property distribution external targeting drug design method provided by the invention, the corresponding distribution external molecule generation diffusion model is designed and trained, and the model can better meet the requirements of the medical field on design of the external distribution property targeting drug. According to the model, through noise amplification and property guidance of the out-of-distribution position-type sampling module, directional offset of property distribution is achieved, the generation success rate of out-of-distribution property molecules is greatly increased, and candidate drug molecules meeting preset chemical property requirements can be stably output. Through differential weight regulation and control of the structure self-adaptive property control weighting module, the generation of out-of-distribution properties is realized, and meanwhile, the specific interaction of drug molecules and target protein binding sites is ensured, so that the generated out-of-distribution property molecules still have high binding affinity with target proteins.

This invention discloses a multimodal synthetic molecule generation method based on chemical reaction perception, relating to the field of drug design technology. The method includes the following steps: obtaining the SMILES sequence of an initial inactive molecule, generating atomic role information, and then capturing the two-dimensional topological structure through graph attention network encoding; simultaneously converting the SMILES sequence into a token sequence and generating word embeddings to form a hybrid representation that combines structural information and syntactic logic; converting the ID of the candidate template into a global feature vector, integrating it into the hybrid representation, and constructing a decoder context memory; then predicting and outputting the multiple reactant sequences with the highest probabilities through beam search to solve for multiple synthetic paths; evaluating each synthetic path, eliminating molecules with invalid structures or excessive molecular weights, and outputting the optimal synthetic path. This invention addresses the problem that existing sequence paradigms neglect rich two-dimensional molecular topological structures, making it difficult to simultaneously obtain candidate molecules with high synthetic success rates and structural diversity that meet drug-likeness requirements.