Abstract: A method and an apparatus for identifying a heterogeneous graph and a property corresponding to a molecular space structure and a computer device are provided. The method includes: characterizing a topology structure included in a heterogeneous graph to generate feature information; generating feature vectors corresponding to key nodes on the topology structure included in the heterogeneous graph according to sampling information obtained by sampling the heterogeneous graph and the feature information; aggregating the feature vectors to generate a graph representation vector corresponding to the heterogeneous graph; and classifying the heterogeneous graph according to the graph representation vector to obtain a classification prediction result of the heterogeneous graph.

Abstract: The present disclosure describes methods, apparatus, and storage medium for node classification and training a node classification model. The method includes obtaining a target node subset and a neighbor node subset corresponding to the target node subset from a sample node set labeled with a target node class, a neighbor node in the neighbor node subset being associated with a target node in the target node subset; extracting a feature subset of the target node subset based on the neighbor node subset by using a node classification model, the feature subset comprising a feature vector of the target node; performing class prediction for the target node subset according to the feature subset, to obtain a predicted class probability subset; and training the node classification model with a target model parameter according to the predicted class probability subset and a target node class subset of the target node subset.

Abstract: This application relates to a target object detection method and apparatus, a non-transitory computer-readable storage medium, and a computer device. The method includes: obtaining a to-be-detected image; inputting the to-be-detected image into a target object detection model; generating, by the target object detection model, a prediction diagram corresponding to the to-be-detected image, the prediction diagram describing a relation degree to which pixels of the to-be-detected image belong to a target detection object; and performing region segmentation on the prediction diagram to obtain a target detection object region. In addition, a method and an apparatus for training an object detection model into the target object detection model, a non-transitory computer-readable storage medium, and a computer device are also provided.

Abstract: The present disclosure provides a compound and use thereof as a glucagon receptor antagonist. The compound includes a compound A, or an isomer, metabolite, prodrug, pharmaceutically acceptable ester, or pharmaceutically acceptable salt of the compound A. The glucagon receptor antagonist in the present disclosure has a novel skeleton structure and a low half maximal inhibitory concentration on the glucagon receptor.

Abstract: An electronic device obtains structural data of a reference molecule. The electronic device performs structural separation on the structural data of the reference molecule to obtain group data of a molecular segment group corresponding to the reference molecule. The electronic device performs feature processing on the group data of the molecular segment group to obtain a candidate segment for replacing the fragment segment. The electronic device generates structural data of a reconstructed molecule based on the candidate segment and the side chain segment.

Abstract: This application discloses a method for processing bioinformatic data performed by a computer device. The method includes: acquiring protein attribute information of a reference protein substance; generating a predicted protein fragment at the protein adjusting region in the reference protein substance by applying a protein prediction model to the protein attribute information, the protein prediction model being configured to predict a protein substance binding to a target protein substance; identifying a similar protein fragment matching the predicted protein fragment in a protein fragment database; virtually synthesizing the similar protein fragment and the reference protein substance to obtain synthetic substance auxiliary information; and the synthetic substance auxiliary information being configured to assist in generation of an antibody protein substance binding to the target protein substance.

Abstract: An electronic device generates, according to a connection graph structure corresponding to a reference drug molecule, an atomic latent vector corresponding to the reference drug molecule. The device performs atom masking processing on the atomic latent vector to obtain a scaffold latent vector and a sidechain latent vector included in the atomic latent vector. The device generates a target scaffold latent vector with a target transition degree between the scaffold latent vector and the target scaffold latent vector according to a spatial distribution of the scaffold latent vector. The device generates a transitioned drug molecule according to the target scaffold latent vector and the sidechain latent vector.

Abstract: 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.

Abstract: 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.

Abstract: The present application relates to an automatic feeding device for animals, comprising a food storage container, a food supply container, a feeding container, a quantitative supply mechanism, a driving mechanism, and a controller. The food storage container has a material inlet and a material outlet; the food supply container has a food supply inlet and a food supply outlet, and the food supply inlet is in butt joint with the material outlet; the quantitative supply mechanism is provided between the food supply inlet and the food supply outlet; the driving mechanism is provided below the quantitative supply mechanism; the feeding container is provided outside the food supply container below the food supply outlet; the controller sends a control signal which controls the driving mechanism to drive the quantitative supply mechanism to quantitatively export food which then falls into the feeding container. The automatic feeding device for animals achieves quantitative feeding.

Abstract: This application provides a protein structure information prediction method and apparatus, a device, and a storage medium, and relates to the field of biological information technologies. The method includes: performing sequence alignment query in a first database according to an amino acid sequence of a protein to obtain multi-sequence aligned data; performing feature extraction on the multi-sequence aligned data to obtain an initial sequence feature; processing the initial sequence feature by using a sequence feature augmentation model to obtain an augmented sequence feature of the protein; and predicting structure information of the protein according to the augmented sequence feature. When the structure information of the protein is predicted based on artificial intelligence (AI), the foregoing solution can improve the prediction efficiency of protein structure information while ensuring the prediction accuracy of the protein structure information.

Abstract: This application relates to a target object detection method and apparatus, a non-transitory computer-readable storage medium, and a computer device. The method includes: obtaining a to-be-detected image; inputting the to-be-detected image into a target object detection model; generating, by the target object detection model, a prediction diagram corresponding to the to-be-detected image, the prediction diagram describing a relation degree to which pixels of the to-be-detected image belong to a target detection object; and performing region segmentation on the prediction diagram to obtain a target detection object region. In addition, a method and an apparatus for training an object detection model into the target object detection model, a non-transitory computer-readable storage medium, and a computer device are also provided.

Abstract: The present disclosure describes methods, apparatus, and storage medium for node classification and training a node classification model. The method includes obtaining a target node subset and a neighbor node subset corresponding to the target node subset from a sample node set labeled with a target node class, a neighbor node in the neighbor node subset being associated with a target node in the target node subset; extracting a feature subset of the target node subset based on the neighbor node subset by using a node classification model, the feature subset comprising a feature vector of the target node; performing class prediction for the target node subset according to the feature subset, to obtain a predicted class probability subset; and training the node classification model with a target model parameter according to the predicted class probability subset and a target node class subset of the target node subset.

Abstract: A method and an apparatus for identifying a heterogeneous graph and a property corresponding to a molecular space structure and a computer device are provided. The method includes: characterizing a topology structure included in a heterogeneous graph to generate feature information; generating feature vectors corresponding to key nodes on the topology structure included in the heterogeneous graph according to sampling information obtained by sampling the heterogeneous graph and the feature information; aggregating the feature vectors to generate a graph representation vector corresponding to the heterogeneous graph; and classifying the heterogeneous graph according to the graph representation vector to obtain a classification prediction result of the heterogeneous graph.

Abstract: An apparatus, method and storage medium for controlling a pathologic microscope are provided. The method includes obtaining a pathological digital image from an incident optical path of the pathologic microscope; performing artificial intelligence (AI) analysis on the pathological digital image to generate AI analysis information; and controlling an augmented reality (AR) projection component to project the AI analysis information on a microscopic field of the pathologic microscope on an outgoing optical path.