化合物-蛋白质相互作用的整个蛋白质组映射
Venkat R Chirasani1,2, Jian Wang1, Congzhou Sha1
1Department of Pharmacology, Penn State College of Medicine, Hershey, PA, 17033, USA.
Current research in chemical biology
|December 21, 2023
概括
这项研究引入了一种新的深度学习方法,用于预测意外的药物蛋白相互作用,减少昂贵的实验查. 德里夫特网络服务器使得药物目标和结合碎片的高通量识别成为可能.
科学领域:
- 药物发现和开发 药物发现和开发
- 计算化学计算化学
- 生物信息学是一种生物信息学.
背景情况:
- 非标药物结合经常导致有毒副作用,导致临床试验失败.
- 由于大量的潜在生物标,对非故意的蛋白质结合物的实验性鉴定是昂贵的.
- 预测化合物-蛋白相互作用对于安全有效的药物开发至关重要.
研究的目的:
- 开发一种计算策略,用于蛋白质组范围内的化合物-蛋白质相互作用的映射.
- 为了使药物目标和约束部分的高通量识别.
- 创建一个用户友好的网络服务器,用于药物目标识别.
主要方法:
- 开发了一个结合化学相似性原理和深度学习的管道.
- 生物活性分子与化学上相似的注释"诱"化合物相匹配.
- 深度学习模型被用来排名潜在的化合物-蛋白质相互作用.
主要成果:
- 成功实施了一种用于预测非标药物结合的新策略.
- 开发的管道使得化合物-蛋白质相互作用的蛋白质组范围的映射成为可能.
- 一个名为DRIFT的网络服务器被构建,用于有效地识别药物目标.
结论:
- 拟议的深度学习策略为实验查提供了一个具有成本效益的替代方案,用于识别非目标效应.
- 驱动网络服务器 (DRIFT) 便于实现高通量,多链接的目标识别.
- 这种方法有助于理解药物毒性,并优化药物开发管道.
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