State Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, P. R. China.
State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, P. R. China.
Nucleic Acids Res. 2024 Jul 5;52(W1):W450-W460. doi: 10.1093/nar/gkae446.
Addressing health and safety crises stemming from various environmental and ecological issues is a core focus of One Health (OH), which aims to balance and optimize the health of humans, animals, and the environment. While many chemicals contribute significantly to our quality of life when properly used, others pose environmental and ecological health risks. Recently, assessing the ecological and environmental risks associated with chemicals has gained increasing significance in the OH world. In silico models may address time-consuming and costly challenges, and fill gaps in situations where no experimental data is available. However, despite their significant contributions, these assessment models are not web-integrated, leading to user inconvenience. In this study, we developed a one-stop comprehensive web platform for freely evaluating the eco-environmental risk of chemicals, named ChemFREE (Chemical Formula Risk Evaluation of Eco-environment, available in http://chemfree.agroda.cn/chemfree/). Inputting SMILES string of chemicals, users will obtain the assessment outputs of ecological and environmental risk, etc. A performance evaluation of 2935 external chemicals revealed that most classification models achieved an accuracy rate above 0.816. Additionally, the $Q_{F1}^2$ metric for regression models ranges from 0.618 to 0.898. Therefore, it will facilitate the eco-environmental risk evaluation of chemicals in the OH world.
解决各种环境和生态问题引发的健康和安全危机是“One Health”(同一健康)的核心重点,其目的是平衡和优化人类、动物和环境的健康。虽然许多化学品在正确使用时对我们的生活质量有重大贡献,但其他化学品则对环境和生态健康构成风险。最近,在同一健康领域,评估与化学品相关的生态和环境风险变得越来越重要。计算模型可以解决耗时和昂贵的挑战,并在没有实验数据的情况下填补空白。然而,尽管这些评估模型有重要的贡献,但它们没有与网络集成,给用户带来不便。在这项研究中,我们开发了一个一站式的综合网络平台,用于自由评估化学品的生态环境风险,名为 ChemFREE(Chemical Formula Risk Evaluation of Eco-environment,可在 http://chemfree.agroda.cn/chemfree/ 上获取)。用户只需输入化学品的 SMILES 字符串,就可以获得生态和环境风险等评估结果。对 2935 种外部化学品的性能评估表明,大多数分类模型的准确率都高于 0.816。此外,回归模型的 $Q_{F1}^2$ 指标范围在 0.618 到 0.898 之间。因此,该平台将促进同一健康领域的化学品生态环境风险评估。