Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Graph-Level Representation for Drug Discovery   Predicating macroscopic influences of drugs on human body, like efficacy and
toxicity, is a central problem of small-molecule based drug discovery.
Molecules can be represented as an undirected graph, and we can utilize graph
convolution networks to predication molecular properties. However, graph
convolutional networks and other graph neural networks all focus on learning
node-level representation rather than graph-level representation. Previous
works simply sum all feature vectors for all nodes in the graph to obtain the
graph feature vector for drug predication. In this paper, we introduce a dummy
super node that is connected with all nodes in the graph by a directed edge as
the representation of the graph and modify the graph operation to help the
dummy super node learn graph-level feature. Thus, we can handle graph-level
classification and regression in the same way as node-level classification and
regression. In addition, we apply focal loss to address class imbalance in drug
datasets. The experiments on MoleculeNet show that our method can effectively
improve the performance of molecular properties predication.

2
The categories of this paper are: math
37
The categories of this paper are: supr-con
29
The categories of this paper are: funct-an
28
The categories of this paper are: alg-geom
11
The categories of this paper are: hep-lat
24
The categories of this paper are: dg-ga
6
The categories of this paper are: astro-ph
26
The categories of this paper are: bayes-an
20
The categories of this paper are: acc-phys
30
The categories of this paper are: q-alg
10
The categories of this paper are: q-bio
21
The categories of this paper are: adap-org
15
The categories of this paper are: nucl-ex
33
The categories of this paper are: plasm-ph
9
The categories of this paper are: nlin
25
The categories of this paper are: patt-sol