LabeoRohita,鲤鱼家族中的一种鱼类,在南亚国家具有重要的饮食和水产养殖重要性。然而,L.rohita的栖息地经常面临各种有害农药和源自工业和农业径流的有机化合物的暴露。单独研究每种潜在有害化合物的影响是具有挑战性的。在这种情况下,例如定量结构-活性关系(QSAR)和定量读取结构-活性关系(q-RASAR)的计算机技术可用于构建能够同时评估许多化合物的毒性的算法模型。我们利用美国EPA的ToxValDB数据库来整理有关L.rohita急性中位致死浓度(LC50)毒性的数据。实验变量包括研究类型(死亡率),研究持续时间(范围从0.25小时到4小时),暴露路线(静态,Flowthrough,和更新),暴露方法(饮用水),和化学品类型(工业化学品和药品)。使用此数据集,我们开发了基于回归的QSAR和q-RASAR模型,以基于化学描述符预测对L.rohita的化学毒性。在基于回归的QSAR模型中预测L.rohita毒性的关键描述符包括F05[S-Cl],SpMax_EA(ri),s4_relPathLength_2和SpDiam_AEA(ed)。这些描述符可用于估计未测试化合物的毒性,并基于这些描述符的存在或不存在帮助开发具有较低毒性的化合物。QSAR和q-RASAR模型都是有价值的工具,可用于了解造成毒性的化学物质的结构特征,并通过预测与L.rohita相关的新未测试化合物的毒性来填补水生毒性数据的空白。最后,采用开发的最佳模型预测297种外部化学物质,对L.rohita最有毒的物质被确定为氯氟氰菊酯,硫氰酸异冰片酯,和多效唑,虽然毒性最小的包括乙酸乙酯,乙基硫脲,和正丁酸.
Labeo rohita, a fish species within the Carp family, holds significant dietary and aquacultural importance in South Asian countries. However, the habitats of L. rohita often face exposure to various harmful pesticides and organic compounds originating from industrial and agricultural runoff. It is challenging to individually investigate the effects of each potentially harmful compound. In such cases, in silico techniques like Quantitative Structure-Activity Relationship (QSAR) and quantitative Read-Across Structure-Activity Relationship (q-RASAR) can be employed to construct algorithmic models capable of simultaneously assessing the toxicity of numerous compounds. We utilized the US EPA\'s ToxValDB database to curate data regarding acute median lethal concentration (LC50) toxicity for L. rohita. The experimental variables included study type (mortality), study duration (ranging from 0.25 h to 4 h), exposure route (static, flowthrough, and renewal), exposure method (drinking water), and types of chemicals (industrial chemicals and pharmaceuticals). Using this dataset, we developed regression-based QSAR and q-RASAR models to predict chemical toxicity to L. rohita based on chemical descriptors. The key descriptors for predicting the toxicity of L. rohita in the regression-based QSAR model include F05[S-Cl], SpMax_EA(ri), s4_relPathLength_2, and SpDiam_AEA(ed). These descriptors can be employed to estimate the toxicity of untested compounds and aid in the development of compounds with lower toxicity based on the presence or absence of these descriptors. Both the QSAR and q-RASAR models serve as valuable tools for understanding the chemicals\' structural features responsible for toxicity and for filling gaps in aquatic toxicity data by predicting the toxicity of newly untested compounds in relation to L. rohita. Finally, the developed best model was employed to predict 297 external chemicals, the most toxic substances to L. rohita were identified as cyhalothrin, isobornyl thiocyanatoacetate, and paclobutrzol, while the least toxic ones included ethyl acetate, ethylthiourea, and n-butyric acid.