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Artificial Intelligence in Drug Design

1
R&D, Integrated Drug Discovery, Industriepark Hoechst, 65926 Frankfurt am Main, Germany
2
R&D, Industriepark Hoechst, 65926 Frankfurt am Main, Germany
*
Author to whom correspondence should be addressed.
Molecules 2018, 23(10), 2520; https://doi.org/10.3390/molecules23102520
Received: 5 September 2018 / Revised: 21 September 2018 / Accepted: 22 September 2018 / Published: 2 October 2018
(This article belongs to the Special Issue Molecular Modeling in Drug Design)
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PDF [2374 KB, uploaded 2 October 2018]
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Abstract

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. View Full-Text
Keywords: artificial intelligence; deep learning; neural networks; property prediction; quantitative structure-activity relationship (QSAR); quantitative structure-property prediction (QSPR); de novo design artificial intelligence; deep learning; neural networks; property prediction; quantitative structure-activity relationship (QSAR); quantitative structure-property prediction (QSPR); de novo design
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Hessler, G.; Baringhaus, K.-H. Artificial Intelligence in Drug Design. Molecules 2018, 23, 2520.

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