Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Optimizing Molecules using Efficient Queries from Property Evaluations   Machine learning based methods have shown potential for optimizing existing
molecules with more desirable properties, a critical step towards accelerating
new chemical discovery. Here we propose QMO, a generic query-based molecule
optimization framework that exploits latent embeddings from a molecule
autoencoder. QMO improves the desired properties of an input molecule based on
efficient queries, guided by a set of molecular property predictions and
evaluation metrics. We show that QMO outperforms existing methods in the
benchmark tasks of optimizing small organic molecules for drug-likeness and
solubility under similarity constraints. We also demonstrate significant
property improvement using QMO on two new and challenging tasks that are also
important in real-world discovery problems: (i) optimizing existing potential
SARS-CoV-2 Main Protease inhibitors toward higher binding affinity; and (ii)
improving known antimicrobial peptides towards lower toxicity. Results from QMO
show high consistency with external validations, suggesting effective means to
facilitate material optimization problems with design constraints.

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The categories of this paper are: q-alg
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The categories of this paper are: econ
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The categories of this paper are: atom-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: cond-mat
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The categories of this paper are: cmp-lg
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The categories of this paper are: astro-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: quant-ph
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The categories of this paper are: supr-con
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The categories of this paper are: math
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The categories of this paper are: hep-lat
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The categories of this paper are: nucl-th
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The categories of this paper are: alg-geom
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The categories of this paper are: patt-sol
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The categories of this paper are: chem-ph