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.

本研究提供了从334种有机化学物质开发的稳健的共有定量结构-活性关系（QSAR）模型，该化学物质涵盖了广泛的化学领域，用于预测有效抑制50％和10％藻类生长的化学物质的浓度。QSAR模型的构建只使用具有明确理化意义的2D描述符，而遵循严格的经济合作与发展组织（OECD）推荐指南则进行了开发，验证和解释。遗传算法和逐步方法一起用于特征选择，而最终的QSAR模型是使用偏最小二乘回归技术得出的。还检查了开发模型的适用范围。然后，将获得的共识模型用于预测没有明确观察到的响应的64种有机化学品，同时通过“预测可靠性指标”工具检查预测的可信度。如果新有机化学物质或未经测试的有机化学物质属于模型范围之内，则开发的模型应适用于数据缺口的填补，并且也可用于设计更安全的环境替代品。