380 results about "Drug design" patented technology

The invention relates to the technical field of computer-aided drug design, in particular to a drug molecule screening and optimizing method based on artificial intelligence prediction, which comprises the following steps: S1, obtaining a dynamic protein conformation set and molecular multi-dimensional characterization: obtaining a dynamic conformation set of a target protein and a physicochemical property spatial distribution diagram of a binding pocket of the dynamic conformation set, a two-dimensional molecular map topological structure and three-dimensional conformation coordinates of the drug molecules are obtained; s2, multi-modal fusion prediction is carried out; s3, generating interpretable optimization guidance; and S4, automatic iterative optimization: performing batch prediction and screening on the new candidate molecular structure, taking the screened optimal molecule as a new starting point, repeatedly executing the interpretability optimization guidance generation step and the step until an iteration termination condition is met, and outputting a final optimized molecule list. Through the multi-modal fusion deep learning model, the interaction strength of the drug molecules and the target protein can be quickly and accurately predicted, and the screening efficiency of the drug molecules is greatly improved.

The invention relates to the technical field of biology, and discloses a chemically modified small nucleic acid drug zero sample prediction method and system based on a large language model and multi-view deep learning. The method comprises the following steps: collecting chemically modified RNA data; performing Token conversion by using an SMILES token regular expression, and performing embedding by using a pre-trained large language model; chemical modification position information is obtained and combined with SMILES in an embedded mode; the RNA sequence is embedded; processing the comprehensive embedded vector by using a self-attention mechanism; fusing RNA sequence embedding and comprehensive embedding by adopting a cross attention mechanism; performing feature extraction and dimension reduction by using a three-dimensional convolutional neural network; and inputting the features into a full-connection layer to predict the drug efficiency. According to the method, high-precision zero-sample prediction of novel chemical modification is realized, the generalization ability and prediction precision of the model are remarkably improved, and a flexible, efficient and accurate method is provided for design and development of RNAi drugs.

The invention provides a drug target affinity prediction algorithm based on protein three-mode and multi-feature fusion, and the algorithm makes full use of sequence information and two-dimensional and three-dimensional structural features of drugs and proteins, and realizes deep integration of data. The technical innovation point lies in that a multi-modal data fusion mechanism and an advanced graph neural network technology are introduced, and the sensitivity and recognition capability of the model to key biomarkers are enhanced through a dynamic attention mechanism. According to the technical advantage, the method has higher accuracy and stability when processing complex biological data, and compared with the prior art, the generalization ability and the interpretability are remarkably improved. In the aspect of technical maturity, the algorithm has passed multiple rounds of verification and shows good prediction performance and adaptability. The method is simple in operation and wide in market application prospect, can assist in new drug discovery and accelerate the drug design process, can also promote the development of the precision medical field, and particularly has important value in targeted therapy and disease prevention strategy formulation.

Methods and apparatus for obtaining representations of proteins and small molecule drugs for synthesis; wherein pre-trained mixed modality protein and natural language fusion models are further trained by supervised fine tuning using reasoning-oriented query—chain-of-thought (CoT) response pairs. The resulting reasoning-oriented neural network is then used to obtain representations of output proteins or small molecule drugs, in response to mixed modality reasoning-oriented input queries specifying conditions on the output. In one embodiment, the neural network is an autoregressive multicapitate transformer whose decoder output heads correspond to the represented modalities. The method returns mixed modality output representations of proteins or small molecule drugs for synthesis or manufacture.

The invention is applicable to the technical field of drug research and development, and provides a lead compound discovery method based on a large language model, which comprises the following steps: determining a target spot and constructing an initial compound library; generating fragments and forming an initial fragment library; calculating the frequency of occurrence of the fragments and the affinity contribution degree of the fragments to the target protein, and updating the fragment library; fragment sampling weight calculation and candidate fragment selection; the large language model generates molecules according to the candidate fragments; evaluating the quality of the generated molecules and updating the molecule library; and repeating the cycle until a preset iteration round number is reached. A fragment-based drug design method is combined with the large language model, rich domain knowledge of the large language model is used for replacing human experts to guide drug design, fragments are used as prompts to stimulate the internal potential of the large language model, the advantages of the two methods complement each other, exploration of the whole chemical space is completed, and the drug design efficiency is improved. The lead compound reaching the human expert level is designed in actual use, and a new technical means is provided for drug research and development.

The invention discloses a high-precision single cell classification method and device based on an artificial intelligence algorithm, and the method comprises the steps: firstly, converting original single cell data into a csv format, and constructing an expression matrix to describe the expression levels of genes under different conditions; thirdly, preliminarily dividing single cell categories through dimensionality reduction and clustering analysis; further, in combination with PPI and GO information, a graph neural network method is used for carrying out quantitative characterization and classification on the genes; then, constructing a single cell data embedding model, extracting features through an encoder and a decoder, and training the model through cross validation and hyper-parameter optimization; and finally, a hidden layer of an encoder in the model is used as an embedded expression matrix of the single cells, the layer comprises original single cell expression information, PPI, GO and other multi-dimensional data, and various downstream tasks including single cell accurate classification can be realized. The method is helpful for deeply understanding a gene expression mode, and provides data support for disease treatment and drug design.

The invention discloses a molecule generation method and device, a storage medium, equipment and a program product, which are applied to a molecule generation scene. The method comprises the following steps: obtaining known modal protein information corresponding to a target protein; performing feature processing on the known modal protein information based on a feature processing module of the trained multi-modal language model to obtain multi-modal standardized features corresponding to the target protein; based on a molecule generation module of the trained multi-modal language model, performing molecule generation processing on the multi-modal standardized features to obtain a target molecule aiming at the target protein; the trained multi-modal language model is obtained by training based on a multi-modal data set, and the multi-modal data set comprises a sequence modal sample, a structure modal sample and a pocket modal sample of a sample protein corresponding to a sample molecule. According to the method, high-quality drug molecules can be generated under the condition that the target protein lacks certain modal information, and the drug design efficiency is improved.

The invention discloses a reaction site prediction method and device based on chemical and physical prior driving, and the method comprises the steps: extracting set features through the multi-modal input of a fusion molecular map, an SMILES sequence and a three-dimensional conformation; generating atomic embedding by using a message passing neural network, and calculating a mixed feature fusing a topological path and a three-dimensional distance; combining the key type weight to construct a graph position code of chemical environment correction; injecting the mixed distance and the charge difference into a Transform attention mechanism, and explicitly modeling an inter-atomic long-range electron effect; a model is jointly trained through double tasks of comparative learning and mask prediction, the comparative learning adopts a directional negative sample to enhance generalization, and mask prediction synchronously recovers an atom type and a charge transfer matrix; and finally, injecting quantum chemistry priori constraint attention weights such as a Fuzzy well function, outputting an atomic-scale reaction activity probability, generating a thermodynamic diagram, and realizing high-precision and interpretable active site labeling. According to the method, the drug design and reaction mechanism analysis efficiency can be remarkably improved.

Methods and apparatus for obtaining representations of proteins and small molecule drugs for synthesis; wherein pre-trained mixed modality protein and natural language fusion models are further trained by supervised fine tuning using reasoning-oriented query—chain-of-thought (CoT) response pairs. The resulting reasoning-oriented neural network is then used to obtain representations of output proteins or small molecule drugs, in response to mixed modality reasoning-oriented input queries specifying conditions on the output. In one embodiment, the neural network is an autoregressive multicapitate transformer whose decoder output heads correspond to the represented modalities. The method returns mixed modality output representations of proteins or small molecule drugs for synthesis or manufacture.

The invention relates to the field of computer-aided drug design (CADD) and molecular informatics, in particular to a multi-modal representation learning method based on cyclopeptide molecules, which is used for predicting cell membrane permeability of cyclopeptide. The method mainly comprises the following steps: (1) data collection: integrating cyclic peptide permeability data from a ChEMBL database, a CycPeptMPDB database and a CyclicPepeda database and patent literatures; (2) multi-modal learning: for different modal data, a deep learning model is adopted to extract feature representations of the data; the method comprises the following steps of: encoding an SMILES sequence by using ChemBERTa (ChemBERTa); using Vision Transform to extract molecular image features, and learning a molecular image structure and 3D coordinate information based on GNN; (3) multi-modal feature fusion: adopting a self-adaptive extensible fusion mechanism, integrating SMILES feature information into image, graph and 3D coordinate features through a cross-modal feature fusion mechanism, and splicing all modal features to obtain multi-modal molecular representation; and (4) permeability prediction: sending the multi-modal molecular representation into a full connection layer for regression prediction so as to evaluate the permeability of the cyclopeptide.

The invention belongs to the field of drug design, and particularly discloses a virtual screening method of a small-molecule inhibitor targeting YTHDF1 protein, which comprises the following steps: acquiring a YTHDF1 protein structure, and carrying out homologous modeling to generate various conformation proteins; small molecules combined with the YTHDF1 protein are obtained, bait molecules are generated, and ligand molecules are formed; a ligand molecule is in butt joint with a binding site on the YTHDF1, and the compound is characterized through PLEC fingerprints; the method comprises the following steps: constructing a specificity scoring function by using a machine learning algorithm, carrying out scoring screening on a compound to be screened and a compound butted with YTHDF1 molecules, carrying out affinity prediction, and screening out the small-molecule inhibitor targeting YTHDF1. According to the virtual screening method provided by the invention, the YTHDF1 targeting molecules with high affinity can be accurately screened on a large scale, candidate molecules are provided for research and development of medicines for treating YTHDF1 related diseases, and a foundation is laid for clinical tests and medicine optimization.

The invention relates to the field of artificial intelligence assisted drug discovery, in particular to a protein large language model-based molecule generation and optimization method, which comprises the following steps of: acquiring amino acid sequence information and three-dimensional structure information of a target protein pocket; encoding the amino acid sequence of the protein pocket by using a protein encoder constructed based on a protein large language model to obtain a protein pocket feature vector; using a context encoder module to encode the context information according to a preset molecule generation mode (de novo generation or optimization based on a seed compound) to obtain a latent vector; and fusing the protein pocket feature vector with the latent vector. According to the method, accurate representation of the protein pocket is realized by utilizing the protein large language model, and a generation-screening-optimization iterative drug design strategy is developed by supporting a unified framework of two generation modes, so that the targeting specificity of generated molecules and the overall efficiency of drug design are improved.

The invention provides a method set system for predicting dynamic structure change of protein A beta 42 based on a deep learning method. The method comprises the following steps: extracting PDB of each frame from a molecular dynamics track; the 2DIR of each PDB is calculated; splitting the PDB according to the functional characteristic region; an alpha-carbon distance matrix distance map of each PDB is calculated, and an alpha-carbon distance matrix distance map of each PDB is calculated; according to the trained deep learning model, predicting an alpha-carbon distance matrix distance map of each region; constructing a protein three-dimensional structure; merging fragments and comparing the fragments with a three-dimensional structure: aligning the optimized three PDB segments, and merging the three PDB segments into a complete PDB protein three-dimensional structure; and finally, solving the RMSD error between the predicted PDB and the real PDB. According to the method, a division and treatment strategy is adopted, three sections of different regions in a protein structure are predicted by utilizing 2DIR, and finally, a three-dimensional structure is successfully constructed. According to the method, a new method is provided for exploring the structure and dynamic change of the Abeta42, a new view angle for researching the Alzheimer's disease is opened up, and important information is also provided for drug design.

A drug design method based on an autoregressive model is provided, which relates to the field of drug design technologies. The method includes: applying a sub-word tokenization algorithm to biological text processing, training protein and ligand information in data sets to obtain a protein tokenizer and a ligand tokenizer, and constructing a tokenizer of the autoregressive model; processing and transforming original data in the data sets into a text form, and encoding by the tokenizer to construct a training data set for the autoregressive model; training the autoregressive model by the training data set, so that the autoregressive model can understand SMILES representations of ligands and learn an interaction mode between proteins and ligands; generating predicted ligands by using the trained autoregressive model, and post-processing through a chemical information tool to acquire candidate ligands with specific chemical structures; and evaluating and optimizing the candidate ligands to determine target candidate molecules.

Methods and apparatus for determining protein and ligand sequence, structure, and docking site given a target protein sequence and structure are presented. A multicapitate transformer architecture with a number of heads including a sequence head and a structure head is introduced, wherein given a target protein sequence and structure, a candidate ligand is generated, wherein the transformer's sequence head yields the ligand sequence and the structure head yields the ligand structure and docking site. Non-capitate weights are shared between the output heads. In one embodiment, a discriminative feature localization method is used to optimize the target protein's input structure representation towards the desired ligand effect class. The methods and apparatus presented enable design and synthesis of both peptide ligands and small molecule drugs each with specified ligand effect categories.

Methods and apparatus for determining a representation of a protein-protein complex, given a constituent target complex of the protein-protein complex are presented; where the constituent target complex is some subset of the protein-protein complex. A recursive transformer neural network is devised, wherein for each iteration of the recursion, a representation of the output constituent protein complexed with the input constituent target complex is passed into the transformer 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 a prediction method based on combined feature coding and DNA binding sites, and belongs to the field of site prediction. The method comprises the following steps of: eliminating low variance features of an initial data set and setting a recombination rate threshold marking sample; determining a task type according to the input label and using a classification or regression algorithm; optimizing the parameters by using Optuna and distributing the parameters to corresponding parameters; randomly distributing training and testing data sets; respectively inputting the data set into a plurality of algorithms including a random forest, a deep forest and the like for training; selecting a model with the best performance as a final prediction model according to the evaluation indexes; and finally, predicting the test set through the optimal model. According to the method, excellent high precision and low error are achieved, meanwhile, better portability is achieved, and a new thought is provided for research of an efficient recombination system in drug design.

The invention discloses a novel PARP / CK2 double-target kinase inhibitor small molecule as well as a pharmaceutical composition and application thereof, the small molecule takes benzofuran pyrimidone as a parent drug effect ring and a benzene ring as a connector in drug design, and is coupled with a 1-amino-2-methylisothiourea group, so that the novel PARP / CK2 double-target kinase inhibitor is obtained. The compound has good inhibitory activity on exogenous PARP enzyme, exogenous CK2 alpha enzyme and proliferation activity of various cancer cells, and can be used for preparing antitumor drugs. # imgabs0 #

Methods and apparatus for protein and drug design using multicapitate (“two or more headed”) neural networks, wherein one head, a sequence head, is trained to generate the sequence of a protein, and another head, a structure head, is trained to generate the structure of the protein; and wherein the neural network is configured to accept a representation of a specified condition as input, and output a representation of a protein's sequence and structure. The structure head and sequence head each have their own loss functions, and the weights of the neural network body are shared, and jointly updated during training. Non-limiting examples of specified input conditions include representations of associated proteins and / or sets of properties of the desired output protein. Some embodiments of the invention include for the design and synthesis of effective peptide drug ligands, synthetic biologic antibody drugs, antibody drug conjugates, and monoclonal antibody (mAb) drugs.

The invention discloses a nanometer anti-tumor drug design optimization method based on a Transform model, and belongs to the field of drug design research assisted by artificial intelligence. The method comprises the following steps: acquiring characteristics of the nano anti-tumor drug and related data of delivery of the nano anti-tumor drug in a tumor area, and dividing the data characteristics into classification characteristics, numerical characteristics and drug delivery efficiency characteristics; performing feature coding on the data, and dividing a data set after feature coding into a training set, a test set and a verification set; respectively carrying out feature fusion on the training set, the test set and the verification set, constructing an input sequence, and carrying out position coding; a Transform model based on Decoder-only is constructed, the training set after position coding is input into the Transform model for training, and the Transform model is made to learn and generate a nano-drug design parameter sequence meeting the target; and outputting a nano-drug design parameter sequence result conforming to the target delivery efficiency by utilizing the trained Transform model.

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 belongs to the technical field of medicines, and provides a novel method for overcoming multidrug resistance of tumors based on innate immune regulation. The invention relates to an application of an agonist of an innate immune STING pathway and an ENPP1 inhibitor in overcoming multidrug resistance of tumors and enhancing an anti-tumor curative effect. The STING agonist or the ENPP1 inhibitor is combined with an anti-tumor chemical drug for application, so that the effects of overcoming the multi-drug resistance of chemotherapeutic drugs and enhancing the anti-tumor curative effect are achieved. The STING agonist / or ENPP1 inhibitor has the effect of reversing multidrug resistance of tumors, and can enhance the sensitivity of cancer cells to chemotherapeutic drugs and monoclonal antibody drugs, so that the curative effect of antitumor drugs is enhanced, and the STING agonist / or ENPP1 inhibitor is expected to be developed into a novel reversal agent drug for multidrug resistance tumors. The novel anti-multidrug resistance strategy provides a new thought for drug design and cancer treatment, and has a great application prospect in clinical treatment of cancers.

Novel systems and methods for drug design and screening exploiting dynamics of protein post-translational modifications are provided.

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 an application of a sulfanyl acetamide compound with a structure shown in a formula I in preparation of a medicine for preventing and / or treating tumors and idiopathic pulmonary fibrosis. The compound can be stably combined with key amino acid residues of DDR1 protein, has a remarkable inhibition effect on phosphorylation activation of the DDR1 protein and various tumor cells, plays an anti-tumor role by effectively inhibiting protein activity, and provides an effective lead compound for subsequent drug design. # imgabs0 #

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.

According to the PDA in-situ polymerization drug perfusion delivery system provided by the invention, the interaction relationship of a perfusion drug system on an organism signal channel and immune response is studied; through manganese metal immunoregulation, based on copper death synergistic therapy, novel protein drug immunotherapy and enhancement of the inhibition action mechanism of traditional chemotherapy on bladder cancer cells, various drugs are organically combined by utilizing dopamine-mediated in-situ bladder intracavity polymerization loaded drugs and combining with the characteristics of polydopamine, so that the bladder cancer cell inhibition effect is enhanced, and the bladder cancer cell inhibition effect is enhanced. A novel multi-effect-in-one multifunctional bladder perfusion drug system is formed, the complexity of drug design is simplified, the bioavailability of drugs is improved, and it is ensured that multi-mode collaborative treatment in the bladder is achieved.

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 fusion protein pocket sequence and structure information molecule generation optimization method and system. The method comprises the following steps: coding a protein pocket sequence based on a Transform encoder; representing a three-dimensional structure of a protein pocket by using a graph, introducing a random walk position code and a graph attention mechanism into a structure encoder, and carrying out protein pocket structure encoding; characterizing the small molecules using a self-referenced embedded character string (SELFIES); the sequence output and the structure output of a protein pocket are combined with small molecule SELFIES representation, after decoding is conducted through a Transform decoder, fusion output is obtained through an attention mechanism, and finally SELFIES characters are generated according to word list probability distribution of the fusion output; and optimizing the physicochemical properties of the generated molecules by using a strategy gradient method of reinforcement learning. Drug molecules with high binding affinity, rich diversity and excellent physicochemical properties can be generated, and the drug design performance is improved.

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.