使用KarmaDock进行高效准确的大型库联体对接.
Xujun Zhang1, Odin Zhang1, Chao Shen1
1Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China.
Nature computational science
|January 4, 2024
概括
KarmaDock是一种新的深度学习方法,可以加速连接器对接,提高结合姿势的准确性,并估计药物发现的结合强度. 这种方法提高了虚拟查的效率,并确定了潜在的候选药物.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 药理学中的人工智能
背景情况:
- 基于结构的虚拟选严重依赖于连接器对接,这是识别潜在候选药物的关键步骤.
- 现有的对接工具在速度,姿势质量和绑定亲和力预测准确性方面面临限制.
- 深度学习方法有希望,但需要进一步开发全面的对接解决方案.
研究的目的:
- 介绍KarmaDock,一个深度学习框架,旨在克服传统和当前深度学习对接方法的局限性.
- 将对接加速,精确的结合姿势生成/校正以及准确的结合强度估计整合到一个模型中.
- 提高在药物发现中虚拟查的效率和可靠性.
主要方法:
- 开发了一个三阶段的深度学习模型,KarmaDock.
- 阶段1:蛋白质和带编码器学习分子内相互作用表示.
- 阶段2:E (n) 等价图神经网络与自身注意力对联体构成更新和后处理的化学可信性.
- 第三阶段:用于结合强度评分的混合密度网络.
主要成果:
- 在四个基准数据集中,KarmaDock表现出强的性能.
- 该模型成功地加速了对接,改善了姿势生成,并准确地估计了结合亲和力.
- 在现实世界的查中,KarmaDock确定了白细胞氨酸激酶 (LTK) 的有效活性抑制剂.
结论:
- KarmaDock为基于结构的药物发现提供了连接器对接技术的重大进步.
- 综合方法提高了虚拟选过程中的速度,准确性和可靠性.
- KarmaDock在识别LTK抑制剂方面的成功突出了其发现新疗法的潜力.
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