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通过与CNNscore简单的融合来增加对接分数选功率.

Huicong Liang1, Aowei Xie2, Ning Hou1

  • 1Marine Biomedical Research Institute of Qingdao, School of Medicine and Pharmacy, Key Laboratory of Marine Drugs, Chinese Ministry of Education, Ocean University of China, Qingdao, P. R. China.

Journal of computational chemistry
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概括
此摘要是机器生成的。

这项研究介绍了一种新的深度学习增强分子对接得分策略. 结合传统和深度学习分数,显著提高了虚拟查能力,识别了潜在的TYK2抑制剂.

关键词:
这是一种Tyk2抑制剂.深度学习是一种深度学习.评分功能是一个得分函数.选功率 选功率 选功率 选功率 选功率虚拟选 虚拟选 虚拟选

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科学领域:

  • 计算化学是一种计算化学.
  • 药物发现 药物发现
  • 生物信息学是一种生物信息学.

背景情况:

  • 基于结构的虚拟选 (SBVS) 依赖于评分函数 (SFs) 来预测蛋白质-连接体结合亲和力.
  • 由于近似和简化,传统的SF具有局限性.
  • 基于深度学习 (DL) 的SF在捕捉复杂交互方面表现有前途.

研究的目的:

  • 开发和验证一种新的对接分数融合策略,整合传统和基于DL的分数.
  • 为了提高分子对接的选能力,用于药物发现.
  • 为了确定TYK2标的新型抑制剂.

主要方法:

  • 开发了一种融合策略,将GNINA的卷积神经网络 (CNN) 的姿势得分与传统的对接得分相结合.
  • 融合策略包括将Watvina的对接得分乘以CNN的得分.
  • 该方法在各种数据集上得到验证,并应用于对TYK2.2进行近120亿个分子的选.

主要成果:

  • 融合战略展示了最先进的选能力,超过了传统方法.
  • 通过CNNscore乘以Watvina对接得分被证明是有效的.
  • 两种有前途的TYK2抑制剂的IC50值为9.99μM和13.76μM在体外被确定.

结论:

  • 开发的对接分数融合策略显著增强了虚拟选能力.
  • 这种综合方法为识别有力的候选药物提供了一个强大的工具.
  • 该方法成功地为具有挑战性的TYK2标识出了新型抑制剂.