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Designing molecules with autoencoder networks
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DOI：10.1038/s43588-023-00548-6.png)
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Autoencoders are versatile tools in molecular informatics. These unsupervised neural networks serve diverse tasks such as data-driven molecular representation and constructive molecular design. This Review explores their algorithmic foundations and applications in drug discovery, highlighting the most active areas of development and the contributions autoencoder networks have made in advancing this field. We also explore the challenges and prospects concerning the utilization of autoencoders and the various adaptations of this neural network architecture in molecular design. Autoencoders are versatile tools for molecular informatics with the opportunity for advancing molecule and drug design. In this Review, the authors highlight the active areas of development in the field and explore the challenges that need to be addressed moving forwards.
Keyword:
PRINCIPAL COMPONENT ANALYSIS
CHEMICAL LANGUAGE
NEURAL-NETWORKS
GENERATION
REPRESENTATION
OPTIMIZATION
GRAPHS
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18.3
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3.1K
被引数:
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Molecular generative model based on conditional variational autoencoder for de novo molecular design
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MOLECULESOAAI
MOLECULESIF4.6
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