299 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.

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 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 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.

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 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 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 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.

The present disclosure relates to the field of drug design, in particular to a method and apparatus for providing a molecular structure of a drug. The method comprises the following steps: inputting a first condition feature and a first random noise into a diffusion model to obtain a candidate molecular structure; determining the conformation of the compound according to the binding energy between the candidate molecular structure and the target protein structure; determining a first pharmacophore according to the interaction between a candidate molecular structure and a target protein structure in the conformation of the compound; determining a conformation score according to the binding energy and the interaction; associatively storing the candidate molecular structure, the first pharmacophore and the conformation score as data in a condition database; according to the conformation scores of all the data in the condition database, screening out a first number of data from the condition database, and generating a second condition feature according to one or more first pharmacophores in the first number of data; and inputting the second condition feature and the second random noise into the diffusion model to obtain an updated candidate molecular structure.

The invention relates to the technical field of medicines, and particularly discloses a screening target of an antibacterial drug, application and a screening method, and the screening method comprises the following steps: testing the interaction between a candidate drug and the target; functional verification of the target protein; testing the antibacterial activity of the candidate drugs; and carrying out an in-vitro antibacterial curve test, evaluating the sterilization or antibacterial effect of the candidate drug on different bacteria, and comparing the change of bacterial gene expression before and after the treatment of the candidate drug through omics analysis (transcriptome and proteome analysis). A new direction is provided for research and development of antibacterial drugs by discovering a new antibacterial target, such as Spx and SodA, of the ISP I except for acting on bacterial ribosomes, a new drug without cross resistance with traditional antibiotics can be developed according to the drug design of the new target, the antibacterial effect is improved, the new antibacterial target of the ISP I in the colimycin and the action mechanism of the new antibacterial target are disclosed, and the application of the ISP I in the colimycin in preparation of the antibacterial drugs is promoted. And a new thought and a new method are provided for research and development of antibacterial drugs.

The present invention relates to the technical field of computer-aided drug design, and in particular to a method and system for calculating the solvation free energy of drug molecules. The present invention evaluates the solute-solvent interaction at the atomic level by calculating the non-bonded interaction between each solute atom and a water molecule. To increase the calculation speed, an outwardly extending water solvent layer is constructed outside the solute molecule. The solvation free energy of the solute molecule is predicted by calculating the interaction energy between each atom in the solute molecule and the solvent layer, thereby realizing accurate simulation of the free energy of the dissolution process of the drug molecule.

The invention discloses a tyrosinase inhibitor, a preparation method thereof and application of the tyrosinase inhibitor in preparation of a product for inhibiting tyrosinase activity. Based on the protein structure of the TYR, the TYR inhibitor which is novel in structure and higher in curative effect and selectivity is found by adopting reasonable drug design and structural modification strategies.

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.

The invention relates to the technical field of artificial intelligence and drug design, and provides a three-dimensional drug molecule generation method and system based on a Bayesian flow network, and the method comprises the following steps: inputting real initial molecule data into a drug molecule generation model combining the Bayesian flow network and a diffusion model; wherein noise molecular features are generated according to prior statistical knowledge; in combination with the noise molecular characteristics and random Gaussian noise obtained from a noise schedule, introducing noise into current molecular data to generate noisy molecular data; meanwhile, updating data distribution in the prior statistical knowledge based on Bayesian inference; denoising the noisy molecule data to generate predicted drug molecules; and repeating the steps until the molecular structure converges or reaches a preset step number T to obtain a drug molecule generation result.

The invention relates to the technical field of molecular docking, in particular to an automatic molecular docking and screening analysis method which comprises the following steps: S1, aligning protein structures; s2, processing the receptor protein file; s3, carrying out batch molecular docking; s4, calculating the distance between the substrate and the catalytic site; s5, performing combined screening on the combination energy and the spatial relationship; and S6, summarizing and visualizing results. According to the method, a large-scale molecular docking task can be efficiently and accurately completed, and powerful technical support is provided for the fields of drug design, enzymology research and the like.

The invention discloses a small molecule drug design method and system, and the system comprises a FaaS function management module which is used for packaging and managing algorithm functions in molecular drug design; the embedded module is used for providing browsing, testing, debugging and visual calling capabilities of the FaaS function, and when a user calls a certain function, automatically generating a calling statement, uploading parameters, receiving a result and rendering the result; the flow arrangement and customization module is used for combining a plurality of FaaS functions to form a complete drug design flow and generating an executable flow description file after flow design is completed; and the execution scheduling and elastic resource module is used for dynamically evaluating required resources according to flow task parameters submitted by a user, scheduling functions to computing nodes in a distributed manner, and intelligently predicting a computing bottleneck and dynamically adjusting a resource quota and a queue strategy according to historical execution data. According to the system, the overall throughput rate and the response speed of the system during molecular drug design can be improved on the basis of HPC.