Designing molecules with autoencoder networks.
review · Level V
Where this comes from
- Record sourced from PubMed, PMID 38177601.
- Also identified by DOI 10.1038/s43588-023-00548-6.
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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.
Medical subject headings
- Drug Discovery
- Neural Networks, Computer