Islam, Md AtaulPillay, Tahir S.2016-06-222016-04Islam, MA & Pillay, TS 2016, 'Simplified molecular input line entry system-based descriptors in QSAR modeling for HIV-protease inhibitors', Chemometrics and Intelligent Laboratory Systems, vol. 153, pp. 67-74.0169-7439 (print)1873-3239 (online)10.1016/j.chemolab.2016.02.008http://hdl.handle.net/2263/53291Simplified molecular input line entry system (SMILES) descriptor based quantitative structure–activity relationship (QSAR) study was performed on a set of HIV-protease inhibitors to explore the structural functionalities for inhibition of the HIV-protease. For this purpose a set of HIV-inhibitors was collected from the literature along with their inhibitory constants. Monte Carlo optimization-based CORAL software was used for QSAR model development. Firstly, the dataset was divided into three random splits and secondly each split was divided into training, calibration, test and validation sets. A training set was used for model development whereas the rest of the sets were used to assess the quality of the developed models. QSAR models were developed with and without considering the influence of cyclic rings toward the inhibitory activity. Statistical quality of QSAR models developed from all splits was very good and fulfilled the criteria. The values of R2, Q2, s, R2 pred and r2 m explained that selected models are robust in nature and efficient enough to predict the inhibitory activity of the molecules outside of the training set. Statistical parameters also suggested that the presence of cyclic rings have a crucial impact on inhibitory activity. The molecular fragmentswere found to be important for the increase or decrease of the inhibitory activity which explained that models have mechanistic interpretation. This ligandbased QSAR study can provide clear directions to design and modulate potential HIV-protease inhibitors.en© 2016 Elsevier B.V. All rights reserved. Notice : this is the author’s version of a work that was accepted for publication in Chemometrics and Intelligent Laboratory Systems. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Chemometrics and Intelligent Laboratory Systems, vol. 153, pp. 67-74, 2016. doi : 10.1016/j.chemolab.2016.02.008.HIV-proteaseDescriptorsMonte Carlo methodCORAL softwareSimplified molecular input line entry system (SMILES)Quantitative structure–activity relationship (QSAR)Health sciences articles SDG-03SDG-03: Good health and well-beingHealth sciences articles SDG-17SDG-17: Partnerships for the goalsPostprint Article