Artificial Intelligence in Drug Design

被引：193

作者：

Hessler, Gerhard ^{[1
]}

Baringhaus, Karl-Heinz ^{[2
]}

机构：

[1] R&D, Integrated Drug Discovery, Ind Pk Hoechst, D-65926 Frankfurt, Germany

[2] R&D, Ind Pk Hoechst, D-65926 Frankfurt, Germany

来源：

MOLECULES | 2018年 / 23卷 / 10期

关键词：

artificial intelligence; deep learning; neural networks; property prediction; quantitative structure-activity relationship (QSAR); quantitative structure-property prediction (QSPR); de novo design; MACHINE LEARNING-METHODS; DEEP NEURAL-NETWORKS; MOLECULAR DESCRIPTORS; PREDICTION; DISCOVERY; CHEMOINFORMATICS; ARCHITECTURES; FINGERPRINTS; CHEMISTRY; SYSTEM;

D O I：

10.3390/molecules23102520

中图分类号：

Q5 [生物化学]; Q7 [分子生物学];

学科分类号：

071010 ; 081704 ;

摘要：

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.

引用

页数：13

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