iEdgeDTA:集成边缘信息和1D图形卷积神经网络,用于绑定亲和力预测
Natchanon Suviriyapaisal1, Duangdao Wichadakul1,2
1Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University Bangkok 10330 Thailand duangdao.w@chula.ac.th.
RSC advances
|August 28, 2023
概括
这项研究引入了一种基于图形的新型深度学习方法,用于预测药物与蛋白质的结合亲和力,加速药物发现. 人工智能模型有效地估计了相互作用的强度,在不需要复杂的蛋白质接触图的情况下,优于现有的方法.
科学领域:
- 计算化学和生物信息学
- 人工智能在药物发现中的作用
背景情况:
- 传统药物发现是昂贵和耗时的.
- 药物重定向提供了一个具有成本效益的替代方案,但需要有效的查.
- 准确预测药物蛋白相互作用 (结合亲和力) 对于识别可行的候选药物至关重要.
研究的目的:
- 开发一种人工智能驱动的计算方法来估计药物与蛋白质的结合亲和力.
- 克服用于预测建模的药物化合物和蛋白质结构表示的局限性.
- 提高药物发现中药物向相互作用预测的效率和准确性.
主要方法:
- 利用基于图形的深度学习技术来表示具有多维特征的药物化合物.
- 采用预先训练的模型来提取蛋白质特征,并在1D蛋白质序列上应用图形运算.
- 纳入了背景知识,并解决了语言模型中常见的固定长度表示挑战.
主要成果:
- 与基线模型相比,拟议的方法在基准数据集上取得了优异的预测结果.
- 在不需要联系人地图信息的情况下证明了有效性,与其他基于图表的方法不同.
- 提供了关于新型人工智能方法的性能和能力的宝贵见解.
结论:
- 开发的基于图形的深度学习模型显著提高了绑定亲和关系预测的准确性.
- 这种计算方法为传统的实验查方法提供了更有效和更有效的替代方案.
- 该研究强调了人工智能在加速药物发现和重新定位努力方面的潜力.
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