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深度HDAC3i:利用基于深度学习的可解释框架加速发现HDAC3抑制剂

Saeed Ahmed, Nalini Schaduangrat, Ittipat Meewan

IEEE transactions on computational biology and bioinformatics

|August 29, 2025

概括

我们开发了DeepHDAC3i,一个新的深度学习框架,用于仅使用分子结构数据识别基因脱乙酶3抑制剂 (HDAC3i). 这种工具准确地预测HDAC3i,有助于癌症治疗的发展.

科学领域:

遗传学和计算药物发现.

背景情况:

表观遗传修饰可以调节基因表达而不会改变DNA序列.
基因脱乙酶 (HDAC) 抑制剂在癌症治疗中使用,但缺乏特异性.
需要选择性HDAC抑制剂来改善癌症治疗结果.

研究的目的:

开发一个新的,可解释的深度学习框架,DeepHDAC3i,用于精确的HDAC3抑制剂 (HDAC3i) 的体识别.
仅使用SMILES标记来识别抑制剂,而无需3D连接体结构.

主要方法:

使用五种分子编码方法 (CDKExt,KR,KRC,Pubchem,RDKit) 来从SMILES标记中提取多视图特征.
使用弹性网进行最佳特征选择和1D卷积神经网络 (1D-CNN) 进行模型构建.
杆式的沙普利添加式扩展用于特征解释性.

主要成果:

在独立测试组中,DeepHDAC3i获得了高性能,精度为0.965,MCC为0.930,AUC为0.985.
与传统机器学习和深度学习模型相比,表现出优异的性能,精度,F1,AUC和MCC显著提高.
该框架为驱动HDAC3i识别的关键特征提供了可解释的见解.

结论:

DeepHDAC3i是一种高度准确和可解释的深度学习工具,用于识别HDAC3抑制剂.
该框架为癌症治疗中药物发现提供了成本高效和快速的方法.
深度HDAC3i的性能优于现有的方法,为精确的HDAC3i识别提供了有价值的工具.

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