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公开(公告)号：US20230035954A1
公开(公告)日：2023-02-02
申请号：US17844094
申请日：2022-06-20
Inventor： Jing HU , Guodong ZHAO
IPC: G16B45/00 , G16B40/20 , G16H50/50 , G06K9/62 , G06F16/901
Abstract: The present disclosure discloses a method for establishing a medicine synergism prediction model, a prediction method and corresponding apparatus, and relates to deep learning and artificial intelligence (AI) medical technologies in the field of AI technologies. A specific implementation solution includes: acquiring a relation graph, nodes in the relation graph including medicine nodes and protein nodes, and edges indicating that interaction exists between the nodes; collecting, from the relation graph, a medicine node pair with definite synergism and a label of whether the medicine node pair has synergism as training samples; and training the medicine synergism prediction model by taking the medicine node pair in the training samples as input to the medicine synergism prediction model and taking the label of whether the medicine node pair has synergism as target output; wherein the medicine synergism prediction model is obtained by learning the relation graph based on a graph convolutional network.
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公开(公告)号：US20230077818A1
公开(公告)日：2023-03-16
申请号：US17844103
申请日：2022-06-20
Inventor： Jing HU , Guodong ZHAO
Abstract: The present disclosure discloses a method for establishing a medicine synergism prediction model, a prediction method and corresponding apparatus, and relates to deep learning and artificial intelligence (AI) medical technologies in the field of AI technologies. A specific implementation solution includes: acquiring a relation graph, nodes in the relation graph including medicine nodes and protein nodes, and edges indicating that interaction exists between the nodes; collecting, from the relation graph, a medicine node pair with definite synergism and a label of whether the medicine node pair has synergism as training samples; and training the medicine synergism prediction model by taking the medicine node pair in the training samples as input to the medicine synergism prediction model and taking the label of whether the medicine node pair has synergism as target output; wherein the medicine synergism prediction model is obtained by learning the relation graph based on a graph attention network.
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公开(公告)号：US20250095783A1
公开(公告)日：2025-03-20
申请号：US18967512
申请日：2024-12-03
Inventor： Jie GAO , Jing HU , Xiaomin FANG , Xiaonan ZHANG
Abstract: A method for obtaining an antibody sequence includes: obtaining first features of amino acids at different sequence positions according to an antigen multiple sequence alignment (MSA) sequence, an antibody MSA sequence, and a concatenated sequence of the antigen MSA sequence and the antibody MSA sequence; obtaining second feature of the amino acids at different 3D coordinates according to a graph constructed according to a reference antigen-antibody complex; fusing the first features of amino acids at different sequence positions with the second features of amino acids at 3D coordinates corresponding to the different sequence positions, and obtaining probability information of each of the amino acids at different positions in the antibody sequence according to fused features; and obtaining a target antibody sequence according to the amino acids and their probability information at different positions in the antibody sequence.
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