title: Predicting binding affinities of diverse pharmaceutical chemicals to human serum plasma proteins using QSPR modelling approaches.
abstract: The prediction of the plasma protein binding (PPB) affinity of chemicals is of paramount significance in the drug development process. In this study, ensemble machine learning-based QSPR models have been established for a four-category classification and PPB affinity prediction of diverse compounds using a large PPB dataset of 930 compounds and in accordance with the OECD guidelines. The structural diversity of the chemicals was tested by the Tanimoto similarity index. The external predictive power of the developed QSPR models was evaluated through internal and external validations. In the QSPR models, XLogP was the most important descriptor. In the test data, the classification QSPR models rendered an accuracy of >93%, while the regression QSPR models yielded r(2) of >0.920 between the measured and predicted PPB affinities, with the root mean squared error <9.77. Values of statistical coefficients derived for the test data were above their threshold limits, thus put a high confidence in this analysis. The QSPR models in this study performed better than any of the previous studies. The results suggest that the developed QSPR models are reliable for predicting the PPB affinity of structurally diverse chemicals. They can be useful for initial screening of candidate molecules in the drug development process.
authors: a ETRC , Gomtinagar, Lucknow , India.
authors: N
authors: N
authors: Basant
authors: b Environmental Chemistry Division , CSIR-Indian Institute of Toxicology Research , Lucknow , India.
authors: S
authors: S
authors: Gupta
authors: b Environmental Chemistry Division , CSIR-Indian Institute of Toxicology Research , Lucknow , India.
authors: K P
authors: KP
authors: Singh
mesh_terms: D001798:Blood Proteins / Q000378:metabolism*; D006801:Humans; D000069550:Machine Learning; D004364:Pharmaceutical Preparations / Q000737:chemistry* / Q000378:metabolism*; D011485:Protein Binding; D021281:Quantitative Structure-Activity Relationship
chemical_list: D001798:Blood Proteins; D004364:Pharmaceutical Preparations
keywords: Plasma protein binding; classification QSPR; ensemble machine learning; molecular descriptors; regression QSPR; structural diversity