期刊
ACS OMEGA
卷 3, 期 10, 页码 13374-13386出版社
AMER CHEMICAL SOC
DOI: 10.1021/acsomega.8b01834
关键词
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资金
- Ministry of Chemicals & Fertilizers, Department of Pharmaceuticals, Government of India
- National Institute of Pharmaceutical Education and Research-Kolkata (NIPER-Kolkata)
- National Science Foundation under NSF ND EPSCoR Award [IIA-1355466]
- State of North Dakota
In the present work, predictive quantitative structure-property relationship models have been developed to predict refractive indices (RIs) of a set of 221 diverse organic polymers using theoretical two-dimensional descriptors generated on the basis of the structures of polymers monomer units. Four models have been developed by applying partial least squares (PLS) regression with a different combination of six descriptors obtained via double cross-validation approaches. The predictive ability and robustness of the proposed models were checked using multiple validation strategies. Subsequently, the validated models were used for the generation of intelligent consensus models (http://teqip.jdvu.ac.in/QSAR_Tools/DTCLab/) to improve the quality of predictions for the external data set. The selected consensus models were used for the prediction of refractive index values of various classes of polymers. The final selected model was used to predict the refractive index of four small virtual libraries of monomers recently reported. We also used a true external data set of 98 diverse monomer units with the experimental RI values of the corresponding polymers. The obtained models showed a good predictive ability as evidenced from a very good external predicted variance
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