【摘 要】
：
皮肤为阻挡环境物质暴露的主要屏障,可以阻挡许多有害的物质.在评估化合物暴露之伤害时,化合物穿皮能力是重要的考虑因素.过去研究一般利用实验方法测量化合物穿皮能力,然而耗时费力,因此若能开发准确的计算方法预测化合物穿皮能力来初步筛选化合物,再利用实验方法确认化学物质穿透皮肤组织的能力,便能加速化合物暴露风险评估过程并减少实验动物使用,同时也能应用于发展穿皮药物.本研究首先从文献中搜集了197个化合物之
【机 构】
：
高雄医学大学药学系;高雄医学大学 高雄医学大学药学系
论文部分内容阅读
皮肤为阻挡环境物质暴露的主要屏障,可以阻挡许多有害的物质.在评估化合物暴露之伤害时,化合物穿皮能力是重要的考虑因素.过去研究一般利用实验方法测量化合物穿皮能力,然而耗时费力,因此若能开发准确的计算方法预测化合物穿皮能力来初步筛选化合物,再利用实验方法确认化学物质穿透皮肤组织的能力,便能加速化合物暴露风险评估过程并减少实验动物使用,同时也能应用于发展穿皮药物.本研究首先从文献中搜集了197个化合物之穿皮系数数据,接着将数据分为训练数据集及测试数据集,并将化合物转换为结构与物化特性描述子.本研究利用循序特征筛选算法(sequential feature selection algorithm)搭配十二种机器学习(machine learning)算法针对训练数据集进行重要特征筛选与模型建构,之后利用测试数据集进行模型预测能力评估.初步研究发现M5P决策树算法表现最佳,相关系数达到0.8118.而相较于过去文献发表的的复线性回归预测模型,其预测模型的相关系数仅有0.6923.进行特征筛选后得到最重要的三个特征为XLogP、SP6、SHBint3,并发现物理化学描述子较结构描述子预测能力佳.研究并发现脂溶性较低时,氢键强度与穿皮系数呈正相关.本研究开发的皮肤穿透系数预测模型准确率佳且可解读,预测能力优于过去发表的人工类神经网络、高斯过程回归还有复线性回归模型.
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