Cargando…

Artificial Intelligence in Drug Design

Artificial Intelligence (AI) plays a pivotal role in drug discovery. In particular artificial neural networks such as deep neural networks or recurrent networks drive this area. Numerous applications in property or activity predictions like physicochemical and ADMET properties have recently appeared...

Descripción completa

Detalles Bibliográficos
Autores principales: Hessler, Gerhard, Baringhaus, Karl-Heinz
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6222615/
https://www.ncbi.nlm.nih.gov/pubmed/30279331
http://dx.doi.org/10.3390/molecules23102520
_version_ 1783369247529893888
author Hessler, Gerhard
Baringhaus, Karl-Heinz
author_facet Hessler, Gerhard
Baringhaus, Karl-Heinz
author_sort Hessler, Gerhard
collection PubMed
description Artificial Intelligence (AI) plays a pivotal role in drug discovery. In particular artificial neural networks such as deep neural networks or recurrent networks drive this area. Numerous applications in property or activity predictions like physicochemical and ADMET properties have recently appeared and underpin the strength of this technology in quantitative structure-property relationships (QSPR) or quantitative structure-activity relationships (QSAR). Artificial intelligence in de novo design drives the generation of meaningful new biologically active molecules towards desired properties. Several examples establish the strength of artificial intelligence in this field. Combination with synthesis planning and ease of synthesis is feasible and more and more automated drug discovery by computers is expected in the near future.
format Online
Article
Text
id pubmed-6222615
institution National Center for Biotechnology Information
language English
publishDate 2018
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-62226152018-11-13 Artificial Intelligence in Drug Design Hessler, Gerhard Baringhaus, Karl-Heinz Molecules Review Artificial Intelligence (AI) plays a pivotal role in drug discovery. In particular artificial neural networks such as deep neural networks or recurrent networks drive this area. Numerous applications in property or activity predictions like physicochemical and ADMET properties have recently appeared and underpin the strength of this technology in quantitative structure-property relationships (QSPR) or quantitative structure-activity relationships (QSAR). Artificial intelligence in de novo design drives the generation of meaningful new biologically active molecules towards desired properties. Several examples establish the strength of artificial intelligence in this field. Combination with synthesis planning and ease of synthesis is feasible and more and more automated drug discovery by computers is expected in the near future. MDPI 2018-10-02 /pmc/articles/PMC6222615/ /pubmed/30279331 http://dx.doi.org/10.3390/molecules23102520 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Hessler, Gerhard
Baringhaus, Karl-Heinz
Artificial Intelligence in Drug Design
title Artificial Intelligence in Drug Design
title_full Artificial Intelligence in Drug Design
title_fullStr Artificial Intelligence in Drug Design
title_full_unstemmed Artificial Intelligence in Drug Design
title_short Artificial Intelligence in Drug Design
title_sort artificial intelligence in drug design
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6222615/
https://www.ncbi.nlm.nih.gov/pubmed/30279331
http://dx.doi.org/10.3390/molecules23102520
work_keys_str_mv AT hesslergerhard artificialintelligenceindrugdesign
AT baringhauskarlheinz artificialintelligenceindrugdesign