Unswerving modeling of hepatotoxicity of cadmium containing quantum dots using amalgamation of quasiSMILES, index of ideality of correlation, and consensus modeling

Abstract

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.

Keywords:

• Quantum dots
• hepatotoxicity
• quasiSMILES
• IIC
• consensus modeling

Acknowledgments

The authors are thankful to Dr. Andrey A. Toropov and Dr. Alla P. Toropova, Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy for providing CORAL software. The authors are also thankful to their respective universities for providing the infrastructure. We are also grateful to Prof. Kunal Roy, Drug Theoretics and Cheminformatics Lab., Dept. of Pharmaceutical Technology, Jadavpur University, India for providing an Intelligent Consensus Predictor tool.

Disclosure statement

No potential conflict of interest was reported by the authors.

Author contributions

Authors have done equivalent contributions to this work.

Data availability statement

All the data used and produced in this work is included in the manuscript and supplementary files.