Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: SMILES Transformer: Pre-trained Molecular Fingerprint for Low Data Drug
  Discovery   In drug-discovery-related tasks such as virtual screening, machine learning
is emerging as a promising way to predict molecular properties. Conventionally,
molecular fingerprints (numerical representations of molecules) are calculated
through rule-based algorithms that map molecules to a sparse discrete space.
However, these algorithms perform poorly for shallow prediction models or small
datasets. To address this issue, we present SMILES Transformer. Inspired by
Transformer and pre-trained language models from natural language processing,
SMILES Transformer learns molecular fingerprints through unsupervised
pre-training of the sequence-to-sequence language model using a huge corpus of
SMILES, a text representation system for molecules. We performed benchmarks on
10 datasets against existing fingerprints and graph-based methods and
demonstrated the superiority of the proposed algorithms in small-data settings
where pre-training facilitated good generalization. Moreover, we define a novel
metric to concurrently measure model accuracy and data efficiency.

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The categories of this paper are: q-alg
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The categories of this paper are: ao-sci
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The categories of this paper are: nucl-th
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The categories of this paper are: hep-th
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The categories of this paper are: supr-con
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The categories of this paper are: patt-sol
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The categories of this paper are: math-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: econ
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The categories of this paper are: solv-int
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The categories of this paper are: comp-gas
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The categories of this paper are: alg-geom
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The categories of this paper are: astro-ph
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The categories of this paper are: q-fin
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The categories of this paper are: acc-phys
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The categories of this paper are: adap-org