Ecotoxicological QSAR modelling of organic chemicals against <i>Pseudokirchneriella subcapitata</i> using consensus predictions approach

The present study provides robust consensus quantitative structure-activity relationship (QSAR) models developed from 334 organic chemicals covering a wide chemical domain for the prediction of effective concentrations of chemicals for 50% and 10% inhibition of algal growth. Only 2D descriptors with definite physicochemical meaning were employed for QSAR model building, whereas development, validation and interpretation were achieved following the strict Organization for Economic Co-operation and Development (OECD) recommended guidelines. Genetic algorithm along with stepwise approach was used in feature selection while the final QSAR models were derived using partial least squares regression technique. The applicability domain of the developed models was also checked. The obtained consensus models were then used to predict 64 organic chemicals having no definite observed responses while the confidence of predictions was checked by the ‘prediction reliability indicator’ tool. The developed models should be applicable for data gap filling in case of new or untested organic chemicals provided they fall within the domain of the model and can also be implemented to design safer alternatives to the environment.本研究基于覆盖广泛化学领域的334种有机化合物，构建了稳健的共识定量构效关系（quantitative structure-activity relationship, QSAR）模型，用于预测对藻类生长产生50%和10%抑制作用的化学品有效浓度。本研究仅采用具有明确理化意义的二维描述符构建QSAR模型，且模型的开发、验证与解读均严格遵循经济合作与发展组织（Organization for Economic Co-operation and Development, OECD）推荐的指南。特征选择环节采用遗传算法结合逐步回归法，最终QSAR模型通过偏最小二乘回归技术构建得到。本研究同时对所构建模型的应用域进行了验证。随后，利用所得到的共识模型对64种暂无明确观测响应的有机化合物进行活性预测，并通过"预测可靠性指标"工具评估预测结果的置信度。所构建的模型可用于填补新型或未测试有机化合物的生态毒性数据空白（需满足其处于模型应用域范围内），同时也可用于设计对环境更安全的化学品替代方案。