公开(公告)号：US20250095783A1

公开(公告)日：2025-03-20

申请号：US18967512

申请日：2024-12-03

Applicant: BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD.

Inventor： Jie GAO ,  Jing HU ,  Xiaomin FANG ,  Xiaonan ZHANG

IPC: G16B30/20 ,  G16B30/10 ,  G16B40/00

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.

公开(公告)号：US20220392585A1

公开(公告)日：2022-12-08

申请号：US17820688

申请日：2022-08-18

Applicant: Beijing Baidu Netcom Science Technology Co., Ltd.

Inventor： Shanzhuo ZHANG ,  Lihang LIU ,  Yueyang HUANG ,  Donglong HE ,  Xiaomin FANG ,  Xiaonan ZHANG ,  Fan WANG ,  Jingzhou HE

IPC: G16C20/70 ,  G16C20/30 ,  G06N3/08

Abstract: A method and apparatus for training a compound property prediction model, a device, a storage medium and a program product. A implementation of the method comprises: acquiring an unannotated compound data set; pre-training a graph neural network using the unannotated compound data set to obtain a pre-trained graph neural network; acquiring a plurality of annotated compound data sets, each annotated compound data set being annotated with one kind of compound property; and performing multi-task training on the pre-trained graph neural network using the plurality of annotated compound data sets, to obtain a compound property prediction model, the compound property prediction model being used to predict a plurality kinds of properties of a compound.

公开(公告)号：US20250092387A1

公开(公告)日：2025-03-20

申请号：US18968907

申请日：2024-12-04

Applicant: BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD.

Inventor： Yang LIU ,  Xiaomin FANG ,  Jie GAO ,  Xiaonan ZHANG ,  Jingzhou HE

IPC: C12N15/10

Abstract: A method and an apparatus for optimizing an mRNA sequence, an mRNA molecule, a pharmaceutical composition, and a use thereof are provided. The disclosure relates to the technical field of artificial intelligence, specifically to technical fields such as biological computing. The method for optimizing the mRNA sequence include: obtaining a first mRNA sequence for synthesizing a protein of interest, where the first mRNA sequence includes a 5′ untranslated region sequence and a coding region sequence; and adjusting the 5′ untranslated region sequence and the coding region sequence with the goal of maximizing a first score of the first mRNA sequence, so as to obtain an optimized second mRNA sequence for synthesizing the protein of interest, where the first score reflects at least one of the following indicators of the first mRNA sequence: translation initiation efficiency, codon adaptation index, and minimum free energy.

公开(公告)号：US20220215899A1

公开(公告)日：2022-07-07

申请号：US17557691

申请日：2021-12-21

Applicant: BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD.

Inventor： Fan WANG ,  Jingzhou HE ,  Xiaomin FANG ,  Xiaonan ZHANG ,  Hua WU ,  Tian WU ,  Haifeng WANG

IPC: G16B15/30 ,  G16B40/00

Abstract: The present disclosure discloses an affinity prediction method and apparatus, a method and apparatus for training an affinity prediction model, a device and a medium, and relates to the field of artificial intelligence technologies, such as machine learning technologies, smart medical technologies, or the like. An implementation includes: collecting a plurality of training samples, each training sample including information of a training target, information of a training drug and a test data set corresponding to the training target; and training an affinity prediction model using the plurality of training samples. In addition, there is further disclosed the affinity prediction method. The technology in the present disclosure may effectively improve accuracy and a training effect of the trained affinity prediction model. During an affinity prediction, accuracy of a predicted affinity of a target to be detected with a drug to be detected may be higher by acquiring a test data set corresponding to the target to be detected to participate in the prediction.