基于预先训练的模型输出作为嵌入的可解释药物向亲和力预测,并基于结构意识的特征融合交叉注意力
Fang Zheng1, Juanjuan Zhao2,3, Zihang Yuan1
1College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, 209 University Street, Yuci District, Jinzhong, 030600, China.
Molecular diversity
|April 25, 2025
概括
这项研究引入了一种新的方法,通过从序列数据中模拟3D蛋白口袋数据来预测药物向相互作用 (DTI). 这种方法降低了计算成本,并提高了药物发现的预测准确性.
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
- 生物信息学是一种生物信息学.
背景情况:
- 蛋白质口袋对于药物向相互作用 (DTI) 的预测至关重要.
- 预测3D口袋数据是计算密集且耗时的.
- 准确的特征表示是有效的DTI预测的关键.
研究的目的:
- 开发一种高效的方法,从序列数据中模拟3D蛋白质口袋数据.
- 提高蛋白质口袋和小分子的特征表示,以改善DTI预测.
- 为DTI预测创建一个可解释的模型.
主要方法:
- 通过结构交叉注意力 (CASD) 使用序列数据模拟3D口袋数据.
- 利用预先训练的模型输出来增强特征表示.
- 采用相同对象的结构交叉注意力 (CASS) 来改善小分子表示.
- 利用令牌级或节点级的交叉注意力来捕获细粒度的交互.
主要成果:
- 拟议的模型在DTI预测方面实现了最先进的性能.
- 该方法有效模拟3D口袋数据,减少计算工作量.
- 增强的特征表示导致更准确的DTI预测.
- 该模型为氨基酸和原子提供可解释的贡献分数.
结论:
- 这种新的方法显著提高了DTI预测的效率和准确性.
- 从序列数据中模拟3D口袋数据是直接预测的可行替代方案.
- 该模型的可解释性有助于理解药物向相互作用.
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