通过使用广泛的毒性数据,对识别结构性警报的先进方法进行全面评估
Ning-Ning Wang1,2,3, Yuan-Hang He4, Xin-Liang Li1,2,3
1Department of Pharmacy, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, People's Republic of China.
这项研究评估了七种基结构提取工具的药物毒性. 总体而言,PySmash_circular表现最好,增强了QSAR模型,并为药物开发提供了有价值的毒性亚结构.
科学领域:
- 计算毒理学计算毒理学
- 药物的发现和开发.
- 化学信息学 化学信息学
背景情况:
- 亚结构性警报 (SA) 对于药物开发和毒性评估至关重要.
- 有许多自动下结构提取工具,但它们的比较性能尚不清楚.
- 评估这些工具对于可靠的药物安全预测至关重要.
研究的目的:
- 为了全面分析和比较七种流行的基层结构提取工具.
- 在毒性评估中确定用于亚结构提取的最佳工具.
- 评估子结构对定量结构-活动关系 (QSAR) 模型的影响.
主要方法:
- 使用43个毒性数据集对7个工具 (生物警报,KRFP,MoSS,Pysmash_circular,Pysmash_group,Pysmash_path,SARpy) 的评估.
- 提取的基结构,预测模型,提取效率和QSAR模型增强的比较.
- 分析的重点是基础结构信息,预测准确性和计算性能.
主要成果:
- PySmash_circular表现出最好的整体性能,在信息传递和基于规则的模型方面表现出色.
- PySmash_path和Bioalerts显示出强的结果,但效率较低.
- SARpy产生了最好的基于规则的预测模型,但只专注于精度.
- 所有测试的子结构都改善了QSAR模型的解释性和有毒化合物的识别.
结论:
- 在毒性预测中,建议使用PySmash_circular进行基层提取.
- 为43个毒性终点设置的基准结构已公开发布.
- 这项工作有助于选择最佳工具并推进计算毒理学研究.
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