REINVENT 2.0: An AI Tool for De Novo Drug Design

被引:249
作者
Blaschke, Thomas [1 ]
Arus-Pous, Josep [1 ,2 ]
Chen, Hongming [3 ]
Margreitter, Christian [1 ]
Tyrchan, Christian [4 ]
Engkvist, Ola [1 ]
Papadopoulos, Kostas [1 ]
Patronov, Atanas [1 ]
机构
[1] AstraZeneca, R&D, Discovery Sci, Hit Discovery, S-43183 Gothenburg, Sweden
[2] Univ Bern, Dept Chem & Biochem, CH-3012 Bern, Switzerland
[3] Chem & Chem Biol Ctr, Guangzhou Regenerat Med & Hlth Guangdong Lab, Guangzhou 510530, Peoples R China
[4] AstraZeneca, Biopharmaceut R&D, Early RIA, Med Chem, S-43183 Gothenburg, Sweden
基金
欧盟地平线“2020”;
关键词
SYSTEM; OPTIMIZATION;
D O I
10.1021/acs.jcim.0c00915
中图分类号
R914 [药物化学];
学科分类号
100701 ;
摘要
In the past few years, we have witnessed a renaissance of the field of molecular de novo drug design. The advancements in deep learning and artificial intelligence (AI) have triggered an avalanche of ideas on how to translate such techniques to a variety of domains including the field of drug design. A range of architectures have been devised to find the optimal way of generating chemical compounds by using either graph- or string (SMILES)-based representations. With this application note, we aim to offer the community a production-ready tool for de novo design, called REINVENT. It can be effectively applied on drug discovery projects that are striving to resolve either exploration or exploitation problems while navigating the chemical space. It can facilitate the idea generation process by bringing to the researcher's attention the most promising compounds.
引用
收藏
页码:5918 / 5922
页数:5
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