量子点的肝毒性随大小而变化,浓度,和其他结构以及实验参数。为了模拟肝毒性,与含镉量子点相关的折衷数据已用于创建准SMILES的代表。为了更广泛的适用性,将核心直径归一化,并应用相关性理想指数来构建更好的定量特征毒性关系模型。总共创建了8个拆分,并通过拆分1获得了最佳模型,并具有更好的验证集对象预测标准。QSAR模型的质量确定中使用的所有统计标准的值在此处开发的所有八个毒性模型的指定范围内。TGA配体和0.6-0.7nm直径等因素有利于肝毒性,而L-半胱氨酸配体和0.5-0.6nm核心直径有助于降低毒性。Further,智能共识建模过程总共形成40个个人和20个共识模型,最佳的个人和共识模型在基于MAE的标准中是“好”。一致性建模增强了预测能力以及所开发模型的准确性,并增加了所构建模型对量子点肝毒性预测的适用空间。
Liver toxicity of quantum dots varies with size, concentration, and other structural as well as experimental parameters. For modeling hepatotoxicity, the eclectic data associated with cadmium containing quantum dots have been used in the creation of quasiSMILES for their representation. The core diameter is normalized for wider applicability and the index of the ideality of correlation is applied to construct the better quantitative features toxicity relationship models. Total eight splits are created and the best model is obtained through split 1 with better prediction criteria of validation set objects. The values of all statistical criteria used in the quality determination of a QSAR model are within the specified range for all the eight toxicity models developed here. Factors like TGA ligand and 0.6-0.7 nm diameter are favorable for liver toxicity while L-cysteine ligand and 0.5-0.6 nm core diameter are helpful in the reduction of toxicity. Further, the intelligent
consensus modeling process forms a total of 40 individual and 20
consensus models and the best individual and
consensus models are \'Good\' in MAE-based criteria. The
consensus modeling enhances the prediction ability as well as the accuracy of the developed models and increases the applicability space of the built models for hepatotoxicity prediction of quantum dots.