Predicting mosquito repellents for clothing application from molecular fingerprint-based artificial neural network SAR models

Spraying repellents on clothing limits toxicity and allergy problems that can occur when the repellents are directly applied to skin. This also allows the use of higher doses to ensure longer lasting effects. As the number of repellents available on the market is limited, it is necessary to propose new ones, especially by using in silico methods that reduce costs and time. In this context SAR models were built from a dataset of 2027 chemicals for which repellent activity on clothing was measured against <i>Aedes aegypti</i>. The interest of using either the ECFP or MACCS fingerprints as input neurons of a three-layer perceptron was evaluated. Transformation of MACCS bit strings into disjunctive tables led to interesting results. Models obtained with both types of fingerprints were compared to a model including physicochemical and topological descriptors.

将驱避剂喷涂于衣物表面，可规避直接涂抹于皮肤时可能引发的毒性与过敏反应。此举亦支持使用更高剂量，以保障驱避效果更为持久。鉴于市场上可获取的驱避剂品类有限，开发新型驱避剂实属必要，尤其可借助可降低研发成本与周期的虚拟（in silico）计算方法。在此研究背景下，团队基于包含2027种化合物的数据集构建了构效关系（SAR）模型，该数据集针对埃及伊蚊（Aedes aegypti）的衣物驱避活性完成了测定。研究评估了以扩展连接指纹（ECFP）或MACCS指纹作为三层感知机输入神经元的应用效果。将MACCS比特字符串转换为析取表的预处理方式，取得了颇具参考价值的实验结果。本研究将基于两种指纹构建的模型，与纳入物理化学及拓扑描述符的模型进行了对比分析。