Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Graph Models for Retrosynthesis Prediction   Retrosynthesis prediction is a fundamental problem in organic synthesis,
where the task is to identify precursor molecules that can be used to
synthesize a target molecule. A key consideration in building neural models for
this task is aligning model design with strategies adopted by chemists.
Building on this viewpoint, this paper introduces a graph-based approach that
capitalizes on the idea that the graph topology of precursor molecules is
largely unaltered during a chemical reaction. The model first predicts the set
of graph edits transforming the target into incomplete molecules called
synthons. Next, the model learns to expand synthons into complete molecules by
attaching relevant leaving groups. This decomposition simplifies the
architecture, making its predictions more interpretable, and also amenable to
manual correction. Our model achieves a top-1 accuracy of $53.7\%$,
outperforming previous template-free and semi-template-based methods.

19
The categories of this paper are: eess
30
The categories of this paper are: q-alg
12
The categories of this paper are: nucl-th
0
The categories of this paper are: hep-ph
18
The categories of this paper are: econ
5
The categories of this paper are: gr-qc
35
The categories of this paper are: mtrl-th
8
The categories of this paper are: hep-ex
9
The categories of this paper are: nlin
37
The categories of this paper are: supr-con
28
The categories of this paper are: alg-geom
11
The categories of this paper are: hep-lat
22
The categories of this paper are: chao-dyn
7
The categories of this paper are: hep-th
31
The categories of this paper are: ao-sci
34
The categories of this paper are: chem-ph