期刊
JOURNAL OF CHEMINFORMATICS
卷 10, 期 -, 页码 -出版社
BMC
DOI: 10.1186/s13321-018-0287-6
关键词
Deep learning; De novo drug design; Graph generative model
类别
资金
- National Natural Science Foundation of China [81573273, 81673279 21572010, 21772005]
- National Major Scientific and Technological Special Project for Significant New Drugs Development [2018ZX09735001-003]
- Beijing Natural Science Foundation [7172118]
Recently, deep generative models have revealed itself as a promising way of performing de novo molecule design. However, previous research has focused mainly on generating SMILES strings instead of molecular graphs. Although available, current graph generative models are are often too general and computationally expensive. In this work, a new de novo molecular design framework is proposed based on a type of sequential graph generators that do not use atom level recurrent units. Compared with previous graph generative models, the proposed method is much more tuned for molecule generation and has been scaled up to cover significantly larger molecules in the ChEMBL database. It is shown that the graph-based model outperforms SMILES based models in a variety of metrics, especially in the rate of valid outputs. For the application of drug design tasks, conditional graph generative model is employed. This method offers highe flexibility and is suitable for generation based on multiple objectives. The results have demonstrated that this approach can be effectively applied to solve several drug design problems, including the generation of compounds containing a given scaffold, compounds with specific drug-likeness and synthetic accessibility requirements, as well as dual inhibitors against JNK3 and GSK-3 beta.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据