Abstract: A compound property prediction method is provided for an electronic device. The method includes obtaining chemical structure information of a target compound, the chemical structure information including an atom and a chemical bond, modeling a chemical structure graph according to the chemical structure information, the chemical structure graph including a first node corresponding to the atom and a first edge corresponding to the chemical bond, constructing an original node feature of the first node and an original edge feature of the first edge, performing a message propagation on the first edge according to the original node feature of the first node and the original edge feature of the first edge to obtain propagation state information of the first edge, and predicting properties of the target compound according to the propagation state information of the first edge.

Abstract: The present disclosure provides a microphone, comprising a shell structure, a vibration pickup assembly, a vibration pickup assembly, wherein the vibration pickup assembly is accommodated in the shell structure and generates vibration in response to an external sound signal transmitted to the shell structure, and at least two acoustoelectric conversion elements configured to respectively receive the vibration of the vibration pickup assembly to generate an electrical signal, wherein the at least two acoustoelectric conversion elements have different frequency responses to the vibration of the vibration pickup assembly.

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 compound property analysis method is provided. The method includes obtaining, according to a molecular structure of a compound, a feature vector of the compound, the feature vector including a node vector of each node and an edge vector of each edge, processing the feature vector by using a feature map extraction model branch to obtain a graph representation vector, and processing the graph representation vector by using a classification model branch to obtain a property of the compound. Thus, in the process of compound property analysis, the graph representation vector that can accurately represent a feature of the compound is obtained based on a graph data structure of the compound, and a classification property of the compound may be obtained based on the graph representation vector, thereby improving the accuracy of determining the classification property of the compound. Apparatus and non-transitory computer-readable storage medium counterpart embodiments are also provided.

Abstract: A compound property prediction method is provided for an electronic device. The method includes obtaining chemical structure information of a target compound, the chemical structure information including an atom and a chemical bond, modeling a chemical structure graph according to the chemical structure information, the chemical structure graph including a first node corresponding to the atom and a first edge corresponding to the chemical bond, constructing an original node feature of the first node and an original edge feature of the first edge, performing a message propagation on the first edge according to the original node feature of the first node and the original edge feature of the first edge to obtain propagation state information of the first edge, and predicting properties of the target compound according to the propagation state information of the first edge.

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.