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通过集成图表注意力 (GATNN) 和密集的神经网络 (DNN) 提高DILI毒性预测

Agung Surya Wibowo¹, Kil To Chong², Hilal Tayara³

¹Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Korea; Department of Electrical Engineering, Telkom University, Bandung, West Java 40257, Indonesia.

|March 3, 2025

概括

预测药物诱导性肝损伤 (DILI) 毒性对于药物安全至关重要. 一个新的注意力神经网络模型图表显示了改善的DILI预测性能,减少了昂贵的实验室评估.

科学领域:

计算毒理学计算毒理学
药物的发现和开发.
在药理学中的机器学习.

背景情况:

药物诱导性肝损伤 (DILI) 在药物开发中构成重大风险,需要准确的预测方法.
传统的DILI实验室评估是耗时且昂贵的.
计算方法,利用分子数据,如简化分子输入线输入系统 (SMILES) 代码,为DILI毒性预测提供了一个有希望的替代方案.

研究的目的:

开发和验证一个改进的计算模型来预测DILI毒性.
利用现有的可靠数据集和先进的机器学习技术来提高预测准确度.
为DILI毒性评估提供免费可访问的计算工具.

主要方法:

一个先前建立的表现最好的DILI预测模型的复制.
将图形注意力神经网络 (GATNN) 与现有模型架构集成.
使用简化分子输入线输入系统 (SMILES) 代码进行分子表示.
使用精度,灵敏度,MCC,AUC和F1得分等指标进行性能评估.

主要成果:

提议的GATNN增强模型在多个指标上表现出高于先前模型的性能.
获得了75.14%的精度,85.2%的灵敏度,39.9%的MCC,75.7%的AUC和82.5%的F1分数.
该模型的增强性能表明在预测DILI毒性方面具有更高的准确性.

结论:

开发的GATNN模型提供了一个更准确,更有效的计算方法来预测DILI毒性.
这种方法可以在药物开发的早期阶段通过识别潜在的肝毒性物质来显著帮助.
该代码的开源可用性有助于更广泛的采用和进一步研究DILI预测.

关键词:

在DILI中毒性.药物开发药物开发图表注意力神经网络的神经网络.标记 SMILES 的代码.

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