一个多分支神经网络用于使用相似信息来预测药物目标亲和力
Jing Chen1,2, Xiaolin Yang1, Haoyu Wu1
1School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China.
ACS omega
|August 26, 2024
概括
这项研究介绍了GASI-DTA,这是一种用于药物标亲和力预测的新型深度学习模型. 它有效地整合了药物和蛋白质相似性,序列和结构信息,在加速药物发现方面表现优于现有的方法.
科学领域:
- 计算化学是一种计算化学.
- 生物信息学是一种生物信息学.
- 药物发现 药物发现
背景情况:
- 预测药物向亲和力 (DTA) 对于有效的药物发现至关重要.
- 当前基于图形的深度学习模型通常会单独分析药物和蛋白质,限制相互作用的洞察力.
- 现有的基于网络的模型依赖于知识图,这可能是资源密集的.
研究的目的:
- 为DTA预测开发一种新的深度学习框架,该框架包含药物和蛋白质相似性信息.
- 克服仅使用分子结构或依赖外部知识图的模型的局限性.
- 提高DTA预测的准确性和效率,以加速药物发现.
主要方法:
- 提出了一个新的网络框架,可以自主提取药物和蛋白质相似性信息.
- 设计了一个多分支神经网络,GASI-DTA,集成相似性,序列和分子结构数据.
- 在两个基准数据集和三个冷启动场景上对模型进行了评估.
主要成果:
- 在大多数指标上,GASI-DTA显著超过了基于图形结构的最新方法.
- 该模型在DTA预测中显示了与现有的基于网络的模型相比的实质优势.
- 实验结果验证了整合相似性信息的有效性,以改善DTA预测.
结论:
- 拟议的GASI-DTA模型为药物向亲和力预测提供了一种强大的新方法.
- 自主提取相似性信息可以提高模型性能,并减少对外部知识图的依赖.
- 这项工作为加速药物发现管道提供了宝贵的工具.
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