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公开(公告)号：US20220392585A1
公开(公告)日：2022-12-08
申请号：US17820688
申请日：2022-08-18
Inventor： Shanzhuo ZHANG , Lihang LIU , Yueyang HUANG , Donglong HE , Xiaomin FANG , Xiaonan ZHANG , Fan WANG , Jingzhou HE
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.
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2.发明申请公开(公告)号：US20220122697A1
公开(公告)日：2022-04-21
申请号：US17565282
申请日：2021-12-29
Inventor： Lihang LIU , Jieqiong Lei , Xiaomin Fang , Donglong He , Fan Wang
Abstract: A method for predicting a compound property, apparatuses, an electronic device, a computer readable storage medium, and a computer program product are provided. The method includes: for each first sample compound of first sample compounds, acquiring spatial structure information of a spatial structure formed by atoms and chemical bonds that constitute the first sample compound; training, using the first sample compounds as input samples and pieces of corresponding spatial structure information as output samples, to obtain a spatial structure prediction model; and continuing training, using second sample compounds as input samples and pieces of corresponding property information as output samples, to obtain the compound property prediction model on the basis of the spatial structure prediction model.
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