6 results about "Molecular mimicry" patented technology

The application relates to an intelligent screening system of a mitochondrion effect molecule, a construction method and application thereof, and belongs to the technical field of molecular biology. The construction method of the intelligent screening system of the mitochondrion effect molecule comprises the following steps: 1, establishing a target protein library; 2, obtaining a data set of the mitochondrion effect molecule; 3, adopting Morgan molecular fingerprint to characterize the mitochondrion effect molecule in the data set, carrying out deduplication and decontamination processing, and then carrying out molecular similarity processing to obtain an input set of a model; and 4, taking accuracy and AUC value as evaluation indexes to construct a support vector machine model. The application utilizes the support vector machine model to carry out prediction in a large amount of molecular data set, and gives a molecule with a high probability score which may have an effective effect on mitochondria, the model is helpful for researchers in the field of mitochondria to reduce parameter adjustment time and improve work efficiency.

The invention discloses a molecular property prediction and drug design system based on graph comparative learning. The molecular property prediction and drug design system comprises a data preprocessing module, a graph coding module, a comparative learning module, a property prediction module and an interpretability analysis module. According to the method, molecules are modeled into an atom-bond graph structure, a self-supervised contrast learning mechanism is adopted to learn general representation from a large-scale unlabeled molecule library, and a graph neural network with multi-view graph enhancement, a molecule similarity keeping mechanism and substructure perception is combined to obtain a multi-view graph structure; accurate prediction of molecular properties, drug-target interaction prediction and molecular optimization design based on property targets are realized.

This invention provides an artificial intelligence-based method for screening antimicrobial drug molecules and its applications, specifically relating to the field of drug screening technology. The method constructs a hierarchical virtual screening system. First, it performs two-stage screening using an affinity prediction model and molecular docking tools, and then constructs the affinity prediction model through machine learning. Subsequently, it integrates drug-likeness assessments such as water solubility and toxicity, as well as molecular similarity calculations, to reduce the probability of obtaining low-drug-likeness drug molecules or potentially cross-resistant molecules, filtering candidate molecules hierarchically. Finally, it verifies antimicrobial activity. This invention solves the problems of low efficiency and system scarcity in screening hundreds of millions of molecules by integrating deep learning with traditional computational methods. While improving screening accuracy, it introduces drug resistance risk screening, effectively reducing research components and shortening the cycle, providing high-quality candidate molecules for the clinical translation of anti-drug-resistant drugs, and has significant clinical translational value and application prospects.

The present invention relates to a method for identifying and characterizing molecules that mimic the binding sites of specific conjugates on various binding surfaces. In an embodiment, the present disclosure provides a method for identifying functional mimetic molecules having a target functional profile that includes one or more functions of a model, comprising: (a) providing one or more mutant libraries of one or more functional template molecules of a model, wherein the mutant library comprises multiple mutants of one or more functional template molecules; (b) providing one or more candidate functional mimetic molecules; (c) evaluating the interactions of the multiple mutant functional template molecules with (i) the model and (ii) one or more candidate functional mimetic molecules, and comparing the evaluations in i) and ii); and (d) identifying one or more functional mimetic molecules having a target functional profile based on the evaluation in c).

The application discloses a molecular similarity calculation method based on a biological activity fingerprint, and comprises the following steps: obtaining a graph neural network for representing a molecule based on the biological activity data of the molecule and a target protein, and obtaining a pre-training vector mapping of each atom in the molecule based on the graph neural network; decomposing the molecule into a target molecule and a query molecule to obtain corresponding subgraphs; summing the pre-training vector mappings of all atoms in each node of the subgraph to obtain the representation s i Of the node in the corresponding subgraph; traversing the maximum common subgraph possibility of the two subgraphs, and calculating the valley base similarity coefficient between the nodes of the query molecule and the target molecule in the two subgraphs, wherein the maximum common subgraph valley base similarity coefficient is used as the similarity of the two molecules. Corresponding systems and applications are also disclosed. The application brings great convenience to ligand-based drug discovery, reduces the dependence of related staff on domain knowledge, improves the efficiency of drug design and development, and shortens the research and development period.

The application relates to the technical field of cheminformatics, and discloses a resistance distance-Kirchhoff iteration-based organic molecule similarity judgment method, which comprises the following steps: obtaining a simplified molecular linear input specification of a molecule to be compared, and converting the molecule to a weighted adjacency matrix representing the relationship between atoms and chemical bonds; expanding the matrix dimension and introducing a symbol parameter to construct an extended adjacency matrix, and converting a discrete connection structure into a parameterized continuous model; subsequently, the molecule graph is equivalent to a resistance network to perform Kirchhoff iteration, the pseudo-inverse of a Laplacian matrix is calculated, and a resistance distance matrix sequence representing global topology is obtained; a characteristic function in the sequence is extracted, integral operation is performed in a specified interval, and a quantitative graph similarity distance is output. The application effectively avoids the non-deterministic polynomial calculation bottleneck of traditional graph matching, completely retains the global topological information of the molecule, eliminates the isomorphism graph judgment ambiguity, and improves the discrimination degree and calculation efficiency of the isomer molecule.