DeepScaffold: A Comprehensive Tool for Scaffold-Based De Novo Drug Discovery Using Deep Learning
DeepScaffold: A Comprehensive Tool for Scaffold-Based De Novo Drug Discovery Using Deep Learning
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DOI：
10.1021/acs.jcim.9b00727
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发表时间：
2020-01-01
影响因子：
5.6
通讯作者：
Liu, Zhenming
中科院分区：
文献类型：
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作者：
Li, Yibo;Hu, Jianxing;Liu, Zhenming
药物设计的最终目标是寻找具有理想药理学性质的新型化合物。设计保留特定支架作为其核心结构的分子是获得潜在候选药物的有效方法。我们提出了一个基于支架的分子生成模型的药物发现，进行分子生成的基础上广泛的支架定义，包括Bemis-Murcko支架，环状骨架，和支架规格的侧链属性。该模型可以概括在给定支架上添加原子和键的化学规则。通过DRD2靶点的分子对接对生成的化合物进行了评价，结果表明，该方法可以有效地应用于解决几个药物设计问题，包括生成含有给定支架的化合物和具有特定对接分数的潜在候选药物的从头药物设计。
The ultimate goal of drug design is to find novel compounds with desirable pharmacological properties. Designing molecules retaining particular scaffolds as their core structures is an efficient way to obtain potential drug candidates. We propose a scaffold-based molecular generative model for drug discovery, which performs molecule generation based on a wide spectrum of scaffold definitions, including Bemis-Murcko scaffolds, cyclic skeletons, and scaffolds with specifications on side-chain properties. The model can generalize the learned chemical rules of adding atoms and bonds to a given scaffold. The generated compounds were evaluated by molecular docking in DRD2 targets, and the results demonstrated that this approach can be effectively applied to solve several drug design problems, including the generation of compounds containing a given scaffold and de novo drug design of potential drug candidates with specific docking scores.