The disclosure provides a process of designing and optimizing supramolecular therapeutics. The disclosure also provides a method for designing and optimizing antibody drug conjugates.

The disclosure provides a process of designing and optimizing supramolecular therapeutics. The disclosure also provides a method for designing and optimizing antibody drug conjugates.

The present invention relates to enzyme inhibitors. More specifically, the present invention relates to ligand-directed covalent modification of proteins; method of designing same; pharmaceutical formulation of same; and method of use.

The present invention relates to enzyme inhibitors. More specifically, the present invention relates to ligand-directed covalent modification of proteins; method of designing same; pharmaceutical formulation of same; and method of use.

A method for determining a drug molecule property is provided. In the method, a text string of a drug molecule is obtained. The text string indicates a structural formula of the drug molecule. Three-dimensional structure information of the drug molecule is obtained. The three-dimensional structure information is generated according to the structural formula indicated by the text string. A drug-forming property of the drug molecule is determined based on a molecular property prediction network, the drug-forming property of the drug molecule being determined by the molecular property prediction network according to the three-dimensional structure information.

A method for determining a drug molecule property is provided. In the method, a text string of a drug molecule is obtained. The text string indicates a structural formula of the drug molecule. Three-dimensional structure information of the drug molecule is obtained. The three-dimensional structure information is generated according to the structural formula indicated by the text string. A drug-forming property of the drug molecule is determined based on a molecular property prediction network, the drug-forming property of the drug molecule being determined by the molecular property prediction network according to the three-dimensional structure information.

This disclosure provides a drug screening method and apparatus, an electronic device, and a computer-readable storage medium. The method includes: determining a structural feature of a protein molecule and a structural feature of a target molecule; obtaining a concatenated node feature corresponding to the protein molecule and the target molecule based on a node information passing sub-network in a drug screening model, the structural feature of the protein molecule, and the structural feature of the target molecule, the node information passing sub-network being a graph neural network; and predicting a first predicted activity value after the protein molecule and the target molecule are bound according to the concatenated node feature.

This disclosure provides a drug screening method and apparatus, an electronic device, and a computer-readable storage medium. The method includes: determining a structural feature of a protein molecule and a structural feature of a target molecule; obtaining a concatenated node feature corresponding to the protein molecule and the target molecule based on a node information passing sub-network in a drug screening model, the structural feature of the protein molecule, and the structural feature of the target molecule, the node information passing sub-network being a graph neural network; and predicting a first predicted activity value after the protein molecule and the target molecule are bound according to the concatenated node feature.

A method and a system for material design utilizing machine learning are provided, where the underlying joint distribution p(S,P) of structure (S)-property (P) relationships is explicitly learned simultaneously and is utilized to directly generate samples (S,P) in a single step utilizing generative techniques, without any additional processing steps. The subspace of structures that meet or exceed the target for property P is then identified utilizing conditional generation of the distribution (e.g., p(P)), or through randomly generating a large number of samples (S,P) and filtering (e.g., selecting) those that meet target property criteria.

A computer-implemented method of designing a molecule and determining a route to synthesise the molecule is provided. The method comprises: receiving one or more desired properties of the molecule; generating one or more candidate molecules using a first machine learning technique that uses the one or more desired properties of the molecule as an input; and for at least one candidate molecule, computing one or more routes to synthesise the candidate molecule using a second machine learning technique.