DeepScaffold: A Comprehensive Tool for Scaffold-Based De Novo Drug Discovery Using Deep Learning.,Journal of Chemical Information and Modeling

当前位置： X-MOL 学术 › J. Chem. Inf. Model. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

DeepScaffold: A Comprehensive Tool for Scaffold-Based De Novo Drug Discovery Using Deep Learning.
Journal of Chemical Information and Modeling ( IF 5.6 ) Pub Date : 2019-12-20 , DOI: 10.1021/acs.jcim.9b00727
Yibo Li _{1,

2} , Jianxing Hu ₁ , Yanxing Wang ₁ , Jielong Zhou ₂ , Liangren Zhang ₁ , Zhenming Liu ₁

Affiliation

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.

中文翻译：

DeepScaffold：使用深度学习的基于支架的从头药物发现的综合工具。

药物设计的最终目标是找到具有所需药理特性的新型化合物。设计保留特定支架作为其核心结构的分子是获得潜在药物候选物的有效方法。我们提出用于药物发现的基于支架的分子生成模型，该模型基于广泛的支架定义（包括Bemis-Murcko支架，环状骨架和具有侧链特性规格的支架）执行分子生成。该模型可以概括所学到的将原子和键添加到给定支架上的化学规则。通过分子对接在DRD2靶标中评估了生成的化合物，结果表明该方法可以有效地解决一些药物设计问题，

更新日期：2019-12-21

点击分享查看原文

点击收藏

公开下载

阅读更多本刊新发论文本刊介绍/投稿指南