通过药物分子和蛋白质序列特征的综合多式分析来提高抑制剂-激酶亲和力预测
Zhenxing Li1, Kaitai Han1, Zijun Wang1
1Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, China.
International journal of biological macromolecules
|April 7, 2025
概括
研究人员开发了KinNet,一种多式融合模型,以准确预测抑制剂-激酶结合亲和力. 这一进步有助于通过改进酶抑制剂开发来帮助针对癌症治疗的药物发现.
科学领域:
- 计算生物学是一种计算生物学.
- 药物发现 药物发现
- 分子建模分子建模
背景情况:
- 准确预测抑制剂-激酶结合亲和力对于药物开发和精准医学至关重要.
- 像CDK,MAP激酶,GSK,CMGC和RTK这样的基因酶是细胞信号的关键调节者,是癌症等疾病的主要药物标.
研究的目的:
- 开发人类酶抑制剂结合亲和力的先进预测模型.
- 引入KinNet,一个创新的多式联接融合模型,旨在提高预测精度.
主要方法:
- KinNet集成了GraphKAN用于药物分子结构特征捕获和Conv-Enhanced Mamba用于蛋白质序列和分子指纹特征提取.
- 该模型利用内核函数和可学习的激活函数来优化特征表示.
- 它将Conv1D用于局部特征提取与Mamba用于长序列处理相结合.
主要成果:
- 与现有方法相比,KinNet模型显示出更高的预测准确度.
- 实验结果验证了该模型在预测抑制剂-激酶结合亲和力方面的有效性.
结论:
- KinNet提供了一个强大的计算框架,用于推进药物发现和开发酶向疗法.
- 该模型有可能阐明抑制剂-激酶结合机制,加速治疗的发展.
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