Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Comparison of Atom Representations in Graph Neural Networks for
  Molecular Property Prediction   Graph neural networks have recently become a standard method for analysing
chemical compounds. In the field of molecular property prediction, the emphasis
is now put on designing new model architectures, and the importance of atom
featurisation is oftentimes belittled. When contrasting two graph neural
networks, the use of different atom features possibly leads to the incorrect
attribution of the results to the network architecture. To provide a better
understanding of this issue, we compare multiple atom representations for graph
models and evaluate them on the prediction of free energy, solubility, and
metabolic stability. To the best of our knowledge, this is the first
methodological study that focuses on the relevance of atom representation to
the predictive performance of graph neural networks.

20
The categories of this paper are: acc-phys
37
The categories of this paper are: supr-con
17
The categories of this paper are: q-fin
25
The categories of this paper are: patt-sol
21
The categories of this paper are: adap-org
12
The categories of this paper are: nucl-th
31
The categories of this paper are: ao-sci
26
The categories of this paper are: bayes-an
33
The categories of this paper are: plasm-ph
0
The categories of this paper are: hep-ph
22
The categories of this paper are: chao-dyn
19
The categories of this paper are: eess
32
The categories of this paper are: atom-ph
15
The categories of this paper are: nucl-ex
7
The categories of this paper are: hep-th
28
The categories of this paper are: alg-geom