About: Chemical space exploration based on recurrent neural networks: applications in discovering kinase inhibitors

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Chemical space exploration based on recurrent neural networks: applications in discovering kinase inhibitors
Creator	Li, Xuanyi Lin, Kejiang Xu, Yinqiu Yao, Hequan
Source	PMC
abstract	With the rise of artificial intelligence (AI) in drug discovery, de novo molecular generation provides new ways to explore chemical space. However, because de novo molecular generation methods rely on abundant known molecules, generated molecules may have a problem of novelty. Novelty is important in highly competitive areas of medicinal chemistry, such as the discovery of kinase inhibitors. In this study, de novo molecular generation based on recurrent neural networks was applied to discover a new chemical space of kinase inhibitors. During the application, the practicality was evaluated, and new inspiration was found. With the successful discovery of one potent Pim1 inhibitor and two lead compounds that inhibit CDK4, AI-based molecular generation shows potentials in drug discovery and development. [Image: see text]
has issue date	2020-06-08(xsd:dateTime)
bibo:doi	10.1186/s13321-020-00446-3
has license	cc-by
sha1sum (hex)	4c4099b7844c8912bf1f3753c3174ae8a6ddad2c
schema:url	https://doi.org/10.1186/s13321-020-00446-3
resource representing a document's title	Chemical space exploration based on recurrent neural networks: applications in discovering kinase inhibitors
has PubMed Central identifier	PMC7278228
schema:publication	J Cheminform
resource representing a document's body	covid:4c4099b7844c8912bf1f3753c3174ae8a6ddad2c#body_text
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