Metadata only
Date
2020-10Type
- Review Article
Abstract
Deep learning bears promise for drug discovery, including advanced image analysis, prediction of molecular structure and function, and automated generation of innovative chemical entities with bespoke properties. Despite the growing number of successful prospective applications, the underlying mathematical models often remain elusive to interpretation by the human mind. There is a demand for ‘explainable’ deep learning methods to address the need for a new narrative of the machine language of the molecular sciences. This Review summarizes the most prominent algorithmic concepts of explainable artificial intelligence, and forecasts future opportunities, potential applications as well as several remaining challenges. We also hope it encourages additional efforts towards the development and acceptance of explainable artificial intelligence techniques. © 2020, Springer Nature Limited Show more
Publication status
publishedExternal links
Journal / series
Nature Machine IntelligenceVolume
Pages / Article No.
Publisher
NatureOrganisational unit
03852 - Schneider, Gisbert / Schneider, Gisbert
Funding
182176 - De novo molecular design by deep learning (SNF)
More
Show all metadata