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图形转换器DTI:基于图形转换器的方法用于药物向相互作用预测.
Mengmeng Gao1, Daokun Zhang2, Yi Chen1
1School of Biological Science and Medical Engineering, Southeast University, Nanjing, China.
GraphormerDTI通过使用人工智能 (AI) 来预测药物向相互作用 (DTI) 来增强药物发现. 这种新的图形变压器方法提高了新分子的预测准确性,减少了药物开发的成本和时间.
科学领域:
- 计算化学是一种计算化学.
- 人工智能在药物发现中的作用
- 机器学习用于生物信息学.
背景情况:
- 传统的药物发现是耗时且昂贵的.
- 人工智能 (AI) 通过预测药物向相互作用 (DTI) 来加速药物查.
- 现有的人工智能方法在有限的标记DTI数据中扎,并未充分利用分子结构信息.
研究的目的:
- 开发一种先进的机器学习方法,用于准确的DTI预测.
- 提高人工智能模型对样本外分子的概括能力.
- 为了利用分子拓结构,提高DTI预测.
主要方法:
- 拟议的GraphormerDTI,利用图形变压器神经网络来建模分子结构.
- 采用代的基于变压器的消息传递用于嵌入分子图形.
- 集成图形变压器与1D-卷积神经网络 (1D-CNN) 和注意力机制用于DTI预测.
主要成果:
- 在三个基准数据集中,GraphormerDTI在分子外DTI预测方面表现出卓越的性能.
- 取得了卓越的表现,超过了五个最先进的基线.
- 与传导性DTI预测的最佳基线相对应的性能.
结论:
- 图形DTI有效地模拟分子结构,使得精确的DTI预测.
- 这种方法显示了新型药物分子的强大泛化能力.
- GraphormerDTI提供了一个强大的工具,可以加速人工智能驱动的药物发现.
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