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Structure-aware conditional variational auto-encoder for constrained molecule optimization
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DOI：10.1016/j.patcog.2022.108581.png)
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The goal of molecule optimization is to optimize molecular properties by modifying molecule structures. Conditional generative models provide a promising way to transfer the input molecules to the ones with better property. However, molecular properties are highly sensitive to small changes in molecular struc-tures. This leads to an interesting thought that we can improve the property of molecules with lim-ited modification in structure. In this paper, we propose a structure-aware conditional Variational Auto-Encoder, namely SCVAE, which exploits the topology of molecules as structure condition and optimizes the molecular properties with constrained structural modification. SCVAE leverages graph alignment of two-level molecule structures in an unsupervised manner to bind the structure conditions between two molecules. Then, this structure condition facilitates the molecule optimization with limited struc-tural modification, namely, constrained molecule optimization, under a novel variational auto-encoder framework. Extensive experimental evaluations demonstrate that structure-aware CVAE generates new molecules with high similarity to the original ones and better molecular properties. (c) 2022 Elsevier Ltd. All rights reserved.
Keyword:
Molecule optimization
Conditional generation
Drug discovery
期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
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