Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Image-Like Graph Representations for Improved Molecular Property
  Prediction   Research into deep learning models for molecular property prediction has
primarily focused on the development of better Graph Neural Network (GNN)
architectures. Though new GNN variants continue to improve performance, their
modifications share a common theme of alleviating problems intrinsic to their
fundamental graph-to-graph nature. In this work, we examine these limitations
and propose a new molecular representation that bypasses the need for GNNs
entirely, dubbed CubeMol. Our fixed-dimensional stochastic representation, when
paired with a transformer model, exceeds the performance of state-of-the-art
GNN models and provides a path for scalability.

11
The categories of this paper are: hep-lat
9
The categories of this paper are: nlin
20
The categories of this paper are: acc-phys
22
The categories of this paper are: chao-dyn
21
The categories of this paper are: adap-org
37
The categories of this paper are: supr-con
34
The categories of this paper are: chem-ph
2
The categories of this paper are: math
36
The categories of this paper are: cmp-lg
16
The categories of this paper are: stat
24
The categories of this paper are: dg-ga
6
The categories of this paper are: astro-ph
31
The categories of this paper are: ao-sci
18
The categories of this paper are: econ
32
The categories of this paper are: atom-ph
17
The categories of this paper are: q-fin