MetaAMPK:使用超学习神经网络准确预测腺单酸盐激活蛋白激酶激活剂
Andi Endang Kusuma Intan1, Darlene Nabila Zetta2, Kanokwan Jarukamjorn3
1Graduate School in the Program of Research and Development in Pharmaceuticals, Pharmaceutical Sciences, Faculty of Pharmaceutical Sciences, Khon Kaen University, Khon Kaen 40002, Thailand.
ACS omega
|October 6, 2025
概括
这项研究介绍了MetaAMPK,这是一种深度学习模型,可以准确地预测腺氨酸单酸 (AMP) 激活蛋白激酶 (AMPK) 的激活剂. 这种计算方法加速了对代谢障碍的新AMPK调节器的发现.
科学领域:
- 计算化学和化学信息学
- 生物医学数据科学是生物医学数据科学.
- 药物的发现和开发.
背景情况:
- 氨酸单酸 (AMP) 激活蛋白激酶 (AMPK) 是细胞代谢的关键调节剂,也是2型糖尿病和非酒精性脂肪肝等代谢性疾病的重要治疗点.
- 由于复杂的生物数据,预测AMPK激活器具有挑战性,需要先进的计算方法来加速药物发现和降低成本.
研究的目的:
- 开发一个高精度的in silico药物发现管道,用于预测腺单酸盐 (AMP) 激活蛋白激酶 (AMPK) 激活剂.
- 创建一个新的深度学习模型,MetaAMPK,利用双向长期短期记忆 (BiLSTM) 和卷积神经网络 (CNN) 架构的元学习者.
主要方法:
- 开发了MetaAMPK深度学习框架,将meta-learners与BiLSTM和CNN结合起来.
- 编码多功能层,包括12个分子指纹和概率特征,以提高预测准确度.
- 通过Y随机化,顺序重要性,适用性域分析和独立化合物的概括测试验证实模型性能.
主要成果:
- 该MetaAMPK模型实现了高精度 (0.91),AUC (0.96) 和MCC (0.82),证明了对AMPK活动的可靠预测.
- 结构重要性分析证实了该模型能够根据分子结构对AMPK激活剂进行分类.
- 分子对接研究确定了伪柏林,β-lapachone和donepezil作为强大的AMPK激活剂,与甲胺相比具有更高的结合亲和力.
结论:
- 该MetaAMPK框架提供了一个高度准确和强大的计算工具,用于预测AMPK激活器.
- 这种方法显著提高了代谢障碍药物发现管道的效率.
- 这些已识别的化合物显示出作为针对AMPK通路的新型治疗剂的潜力.
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