通过异质模型融合序列和结构知识,以准确和解释性地预测药物标亲和力
Xin Zeng1, Kai-Yang Zhong1, Bei Jiang2
1College of Mathematics and Computer Science, Dali University, Dali 671003, China.
Molecules (Basel, Switzerland)
|December 23, 2023
概括
新型深度学习模型S2DTA通过整合序列和图形结构特征来增强药物向亲和力 (DTA) 预测. 这种方法显著提高了对现有方法的准确性,有助于药物发现.
科学领域:
- 计算化学计算化学
- 生物信息学是一种生物信息学.
- 药物发现 药物发现 药物发现
背景情况:
- 药物向亲和力 (DTA) 预测对于药物发现至关重要,但目前的计算方法缺乏预测准确性.
- 现有的模型往往无法捕捉分子相互作用的复杂结构细节.
研究的目的:
- 开发一种新的深度学习模型,S2DTA,用于准确的DTA预测.
- 使用异质模型利用融合序列和图形结构特征.
主要方法:
- 开发了S2DTA,这是一个深度学习模型,将药物SMILES,目标和口袋序列特征与图形结构信息融合在一起.
- 采用基于图形和语义网络的异质模型进行特征集成.
- 利用双向自我注意机制进行可解释性分析.
主要成果:
- 与DeepDTA,GraphDTA和DeepDTAF相比,S2DTA表现出更高的预测准确性. 这是一个很好的例子.
- 实现了平均绝对误差 (MAE) 的25.2%降低和根平均平方误差 (RMSE) 的20.1%降低.
- 在皮尔森相关系数 (PCC),斯皮尔曼,一致性指数 (CI) 和R平方 (R2) 中显著改善.
结论:
- 不同质的模型的整合大大提高了DTA预测中的预测准确度.
- S2DTA是一种有效和准确的工具,用于预测药物向亲和力,推进药物发现工作.
- 解释性分析证实了模型的可靠性.
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