D3Rings:一种快速而准确的方法来识别环状系统和深度生成类似药物的循环化合物
Minfei Ma1,2, Xinben Zhang1, Liping Zhou1,2
1Stake Key Laboratory of Drug Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China.
Journal of chemical information and modeling
|January 11, 2024
概括
一种新的方法,D3Rings,用于药物发现,识别复杂的循环化合物. 这些循环分子的扩展数据库,特别是来自自然产品的数据库,改善了对药物开发的连接体发现和结合亲和力.
科学领域:
- 计算化学是一种计算化学.
- 药品化学 药品化学 是一个
- 药物发现 药物发现
背景情况:
- 在药物发现中,探索化学空间寻找高亲和度连接体至关重要.
- 具有复杂循环支架的自然产品提供了新的分子结构.
- 现有的方法难以系统地识别和分类循环化合物.
研究的目的:
- 开发一种新的计算方法 (D3Rings),用于识别多样化的循环化合物结构.
- 分析化学数据库中循环结构的普遍性.
- 利用深度生成模型和扩展数据集用于新药发现.
主要方法:
- 开发D3Rings软件,用于对非循环,单循环,螺旋,融合,桥接,子和宏循环化合物进行分类.
- 在ChEMBL,DrugBank和COCONUT数据库中对循环化合物的统计分析.
- 训练深度生成模型 (VAE,AAE,CharRNN) 来创建扩大的数据集.
- 使用生成的数据集对抗SARS-CoV-2目标的分子对接实验.
主要成果:
- D3Rings成功地识别和分类了各种循环化合物类型.
- 自然产品表现出丰富多样化的复杂环状结构 (螺旋环,宏循环,融合/桥梁).
- 扩大的复合数据集改善了分子对接结果,循环结构显示出卓越的性能.
- 生成的化合物显示出新药发现的潜力.
结论:
- D3Rings方法促进了循环化合物的系统分析.
- 探索结构新的循环化合物对于发现有力的药物配体至关重要.
- 扩展具有多样化的循环结构的复合图书馆增强了药物发现工作.
- 开发的D3Rings软件是公开的.
相关概念视频
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