PredPS:基于注意力的图形神经网络,用于预测人体血中化合物的稳定性
Woo Dae Jang1, Jidon Jang1, Jin Sook Song1
1Data Convergence Drug Research Center, Korea Research Institute of Chemical Technology, Daejeon 34114, Republic of Korea.
Computational and structural biotechnology journal
|July 24, 2023
概括
研究人员开发了基于注意力的图形神经网络PredPS,以预测人类血的稳定性. 该工具通过有效预测化合物降解,节省时间和资源,帮助早期发现药物.
科学领域:
- 计算化学是一种计算化学.
- 药物的发现和开发.
- 生物信息学是一种生物信息学.
背景情况:
- 人体血中的化合物稳定性对于有效的药物输送和体内疗效至关重要.
- 快速的血降解可能导致治疗结果不佳.
- 预测人体血稳定的现有工具是有限的,没有开源软件可用.
研究的目的:
- 开发一个开源软件程序,用于预测人体血稳定性.
- 创建一个准确和高效的in silico模型来评估化合物在血中的降解.
- 通过提供可靠的稳定性预测工具来支持早期药物发现.
主要方法:
- 开发一种以注意力为基础的图形神经网络模型,名为PredPS.
- 培训和验证使用组合的内部和开源数据集.
- 与现有的机器学习和深度学习算法进行比较分析.
主要成果:
- 与其他测试的算法相比,PredPS表现出更高的性能.
- 实现了高预测准确度,接收器操作特征曲线 (AUC) 下面面积为90.1%.
- 通过5倍交叉验证报告的准确率为83.5%,灵敏度为82.3%,特异性为84.6%.
结论:
- 在早期药物发现中,PredPS是预测人类血稳定性的有效工具.
- 采用像PredPS这样的in silico等离子体稳定性预测模型可以加速高通量选.
- PredPS源代码和Web服务器的可用性有助于其在研究环境中被采用.
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