Abstract
Virtual screening methods were adopted for modeling and prediction of semi conductivity of Schiff base molecules. The predictive models built using data mining methods that were generated from descriptor based technology was able to give an alternative method to the currently used HOMO-LUMO gap based prediction methodologies. The predictions using the discriminative classifiers such as, Naïve Bayes, Random forest, Support Vector Machine and Decision tree analysis in the machine learning algorithms could predict new semi-conductor molecules.
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Abbreviations
- CSV:
-
Comma separated values
- ARFF:
-
Attribute-relation file format
- AI:
-
Artificial intelligence
- ML:
-
Machine learning
- VS:
-
Virtual screening
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Acknowledgements
The authors are grateful to the support given by the Centre for Cheminformatics, Department of Chemistry, Malabar Christian College, Calicut, aided by University Grants Commission (UGC).
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Sajeev, R., Athira, R.S., Nufail, M. et al. Computational predictive models for organic semiconductors. J Comput Electron 12, 790–795 (2013). https://doi.org/10.1007/s10825-013-0486-3
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DOI: https://doi.org/10.1007/s10825-013-0486-3