在图表上进行多任务学习 卷积残留神经网络用于选多目标抗癌化合物
Thanh-Hoang Nguyen-Vo1, Trang T T Do1, Binh P Nguyen2
1Ho Chi Minh City Open University, 97 Vo Van Tan, District 3, Ho Chi Minh City 70000, Vietnam.
Journal of chemical information and modeling
|August 28, 2024
概括
一个新的计算模型,iACP-GCR,使用图形卷积残余神经网络识别多目标抗癌化合物. 这种方法通过超越现有方法来加速发现潜在的抗癌药物.
科学领域:
- 计算化学是一种计算化学.
- 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:
- 对抗癌症药物的实验查是耗时且资源密集的.
- 需要计算方法来加快识别潜在的抗癌化合物.
- 多目标药物发现旨在识别有效对抗多种癌症类型的化合物.
研究的目的:
- 开发一种先进的计算模型,用于识别多目标抗癌化合物.
- 提高抗癌药物候选查的效率和准确性.
- 为研究人员提供公开可访问的工具.
主要方法:
- 利用在图形卷积残余神经网络 (GCRNs) 上的多任务学习,使用两种快捷连接类型.
- 在NCI-60数据集上训练并评估了iACP-GCR模型,包括九种癌症类型.
- 将模型的性能与三个先进的计算多任务学习方法进行了比较.
主要成果:
- 与现有的先进计算方法相比,iACP-GCR模型表现出优异的性能.
- 双捷径连接的整合提高了预测效率.
- 该模型成功地以高精度识别了多目标抗癌化合物.
结论:
- iACP-GCR为抗癌药物发现提供了一种强大而高效的计算方法.
- 该模型在识别多目标化合物的有效性对癌症治疗具有重大前景.
- 一个公共的Web服务器已经部署,以促进更广泛的研究社区的访问和利用.
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