比较机器学习和深度学习框架,用于可靠的致癌性预测和活动悬崖分析
Arkaprava Banerjee1, Vinay Kumar1, Kunal Roy1
1Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700 032, India. kunal.roy@jadavpuruniversity.in.
在老鼠中预测化学致癌性可以告知人类健康风险. 这项研究开发了先进的模型,发现使用ARKA描述符和人工神经网络的物流回归显示了致癌性的高预测能力.
科学领域:
- 毒理学 毒理学 毒理学
- 计算化学计算化学
- 生物信息学是一种生物信息学.
背景情况:
- 由于致癌性,工业化学品对人类健康构成风险.
- 致癌性的预测模型对于风险评估至关重要.
- 鼠类致癌性数据作为人类相关性的有价值的代理.
研究的目的:
- 开发可靠的预测模型,用于小鼠的二进制致癌性数据.
- 为了将大鼠的致癌性与人类的致癌性联系起来.
- 为了确定影响化学致癌性的结构特征.
主要方法:
- 采用了基于特征和化学语言建模方法.
- 使用机器学习算法,包括人工神经网络 (ANN),开发了分类跨读结构-活动关系 (c-RASAR) 模型.
- 基于SMILES字符串的模型使用了长短期内存 (LSTM) 架构,并使用了ARKA描述符的逻辑回归.
主要成果:
- 后勤回归RASAR-ARKA模型表现出最好的性能.
- 该ANN c-RASAR模型还显示了对外部数据的高效预测能力.
- 该ARKA框架促进了活动悬崖的识别,并解释了预测错误.
结论:
- 开发的模型为预测化学致癌性提供了一个有效的框架.
- 结构功能分析显示,原子 (氨酸衍生物,尼托胺) 和分支会增加致癌性.
- 发现增加的分子尺寸可以降低致癌效应.
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