TEFDTA:一个变压器编码器和指纹表示组合预测方法,用于绑定和非绑定药物标亲和力
Zongquan Li1,2, Pengxuan Ren2, Hao Yang2
1School of Information Science and Technology, ShanghaiTech University, Shanghai, 201210, China.
Bioinformatics (Oxford, England)
|December 23, 2023
概括
一个新的变压器编码器和指纹结合的药物目标亲和力预测方法 (TEFDTA) 模型准确地预测了共价和非共价药物目标结合亲和力,改进了现有的方法.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 机器学习 机器学习
背景情况:
- 准确预测药物标结合亲和力对于有效的药物发现至关重要.
- 当前的深度学习方法往往忽略了共价结合相互作用,这是药物开发中的一个关键领域.
- 提高对非共价相互作用和共价相互作用的结合亲和力预测的准确性仍然是一个挑战.
研究的目的:
- 开发一种基于注意力的新型模型,用于预测药物标结合 afinity.
- 解决预测共价结合相互作用现有方法的局限性.
- 提高药物向亲和力预测模型的准确性和范围.
主要方法:
- 提出了一个变压器编码器和指纹组合预测方法用于药物目标亲和力 (TEFDTA).
- 利用蛋白质和药物分子的独特分子表示.
- 在非结合相互作用上训练模型,并使用精选的数据集对共价相互作用进行了微调.
- 引入了一个手动纠正的戴维斯数据库,用于非绑定交互.
主要成果:
- 与现有方法相比,TEFDTA表现出了显著的改进.
- 在预测非共价结合亲缘关系方面平均改善了7.6%.
- 在预测共价结合亲缘关系方面显示出了62.9%的显著平均改善.
- 该模型有效地识别了活动悬崖,并区分了影响结合亲和力的微妙结构差异.
结论:
- 开发的TEFDTA模型为非共价和共价药物向相互作用提供了增强的预测能力.
- 该模型处理共价结合的能力代表了药物发现工具的重大进步.
- 在识别活动悬崖和理解结构-活动关系方面,TEFDTA显示出前景.
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