通过在化学相似性网络上传播,探索用于识别的化学空间
Jungseob Yi1, Sangseon Lee2, Sangsoo Lim3
1Interdisciplinary Program in Artificial Intelligence, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul, 08826, South Korea.
Computational and structural biotechnology journal
|September 8, 2023
概括
这项研究引入了一种新的网络传播方法,用于药物发现中的领先识别. 该方法通过分析化学相似性网络,有效地优先考虑候选药物,提高发现新治疗化合物的效率.
科学领域:
- 计算化学是一种计算化学.
- 化学信息学 化学信息学
- 药物发现 药物发现
背景情况:
- 标识对于药物发现至关重要,通常采用机器学习 (ML) 和深度学习 (DL).
- 当前的ML/DL方法可能无法充分利用化合物相似性信息.
- 数据挖掘方法在高效搜索具有低假阳性率的大型化学空间方面面临挑战.
研究的目的:
- 开发一种高效的数据挖掘方法,用于使用网络传播 (NP) 来识别.
- 将复合相似性网络与深度学习相结合,以提高候选人优先级.
- 解决现有的ML/DL和数据挖掘技术在发现方面的局限性.
主要方法:
- 开发了一种基于网络传播 (NP) 的数据挖掘方法.
- 利用了14个基于指纹的化学相似性网络.
- 采用深度学习药物向相互作用模型来缩小最初的候选人.
- 应用NP来根据活动得分 (例如IC50) 优先考虑化合物.
主要成果:
- 在BindingDB实验中,NP方法成功地为特定目标识别了以前未被标记的化合物.
- 确定了24个用于CLK1目标的候选线索.
- 五个可合成的CLK1候选物中,有两种通过结合性试验进行实验验证.
结论:
- 拟议的NP框架对于从ZINC等大型复合数据库中进行标识是有效的.
- 该方法通过结合基于网络的相似性来提高药物候选者的优先级.
- 这种方法为加速药物发现的早期阶段提供了有价值的工具.
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