CPIScore:一种深度学习方法,用于快速评分和解释蛋白质-连接物结合相互作用
Li Liang1, Yunxin Duan1, Chen Zeng1
1Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China.
Journal of chemical information and modeling
|November 20, 2024
概括
结合变压器和图形卷积网络 (GCN) 的新深度学习方法CPIScore,准确地预测了蛋白质-连接体结合亲和力. 这种计算方法通过识别强大的小分子抑制剂来加速药物发现.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 医学中的人工智能.
背景情况:
- 预测蛋白质-连接体结合亲和力对于药物发现至关重要,但在计算上昂贵.
- 传统的模拟方法耗时,阻碍了实际应用.
- 需要准确的预测模型来简化药物开发管道.
研究的目的:
- 引入CPIScore,这是一种新的深度学习方法,用于增强蛋白质-配体结合亲和力预测.
- 利用变压器和图形卷积网络 (GCN) 来提高准确性和效率.
- 为了证明该方法在识别强效药物候选者的实用性.
主要方法:
- 开发了CPIScore,集成全球序列上下文的变压器和局部分子图形特征的GCN.
- 训练并验证了蛋白质 - 配体结合亲和力数据集的模型.
- 使用Pearson的r.用传统和其他深度学习模型对传统和其他深度学习模型进行性能评估.
- 应用了CPIScore来识别来自多样化和专注化合物库的抑制剂.
主要成果:
- 在测试组中,CPIScore获得了0.74的Pearson's r,表现优于现有方法.
- 该模型在多个标中识别抑制剂时显示出高的丰富率.
- 实验验证了六种已识别的ATR抑制剂中的四种具有10纳米分子活动的ATR抑制剂.
结论:
- CPIScore在预测蛋白质 - 配体结合亲和力方面取得了重大进展.
- 该方法通过准确识别强大的小分子抑制剂来简化药物发现.
- CPIScore有可能大大提高药物开发过程的效率.
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