公开(公告)号：US20230245727A1

公开(公告)日：2023-08-03

申请号：US18126887

申请日：2023-03-27

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

Inventor： Donglong HE ,  Lihang Liu ,  Dayong Lin ,  Xiaomin Fang ,  Fan Wang ,  Jingzhou He

IPC: G16C20/30 ,  G16C20/50 ,  G16C20/70

CPC classification number: G16C20/30 ,  G16C20/50 ,  G16C20/70

Abstract: A computer-implemented method is provided. The method includes: obtaining feature information of a molecule to be represented, wherein the molecule includes a plurality of atoms; generating a fully connected graph of the plurality of atoms, wherein the fully connected graph includes a plurality of edges; generating, based on the feature information, a plurality of atom vector representations and a plurality of edge vector representations, wherein the plurality of atom vector representations correspond to the plurality of atoms, respectively, and the plurality of edge vector representations correspond to the plurality of edges, respectively; performing, based on the fully connected graph, at least one aggregation on the plurality of atom vector representations and the plurality of edge vector representations to obtain a plurality of updated atom vector representations; and generating, based on the plurality of updated atom vector representations, a molecular vector representation of the molecule.

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.