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.

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.

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.