Designing molecules with autoencoder networks
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Date
2023-11
Publication Type
Review Article
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yes
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Abstract
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.
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published
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3 (11)
Pages / Article No.
922 - 933
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Software
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Organisational unit
03852 - Schneider, Gisbert / Schneider, Gisbert
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Funding
182176 - De novo molecular design by deep learning (SNF)