在分子生成模型中将目标结构信息纳入一个简单的方法
Wenyi Zhang1,2,3, Kaiyue Zhang1,2, Jing Huang1,2,3
1Westlake AI Therapeutics Lab, Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China.
Journal of chemical information and modeling
|June 15, 2023
概括
这项研究引入了一种用于药物发现的新深度学习方法,该方法使用3D目标结构来生成新型分子. 这种方法显著改善了基于结构的药物设计的命中生成.
科学领域:
- 计算化学是一种计算化学.
- 人工智能在药物发现中的作用
- 分子建模分子建模
背景情况:
- 深度学习生成模型越来越多地用于药物发现.
- 基于结构的药物设计需要有效的方法来产生与特定标结合的新分子.
研究的目的:
- 用深度学习生成模型开发基于结构的药物设计的新方法.
- 将目标3D结构信息纳入分子生成中,以改进药物候选物识别.
主要方法:
- 预测对接分数的传递信息的神经网络与生成神经网络作为奖励函数相结合.
- 针对特定目标的分子集被构建用于培训,以解决可转移性问题.
- 采用了两轮培训过程来完善生成模型.
主要成果:
- 与传统的对接相比,这种新方法显示出成功生成的100倍增加.
- 生成的分子与已批准的药物或已知的活性联结物对特定点具有相似性.
- 在没有对活性/无活性化合物的事先知识的情况下,可以实现化学空间的准确指导探索.
结论:
- 提出的方法为基于结构的分子生成提供了一个通用和高效的解决方案.
- 这种方法通过有效地导航化学空间来增强候选药物的发现.
- 它通过利用3D结构数据和先进的深度学习技术来克服传统方法的局限性.
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