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Qikprop version 4

Manufactured by Schrödinger

QikProp version 4.3 is a computational tool developed by Schrödinger that can predict the physicochemical properties of small organic molecules. It provides estimates for a range of pharmaceutically relevant properties, such as solubility, permeability, and other ADME (Absorption, Distribution, Metabolism, and Excretion) characteristics.

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2 protocols using qikprop version 4

1

Screening and Optimization of Drug-like Compounds

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The IBScreen database containing 211432 compounds was selected. The drug-likeness behavior of the database compounds was predicted by using QikProp version 4.3 (Schrödinger). The compounds were employed for the calculation of pharmacokinetic parameters by QikProp v4.3. Physiochemical descriptors and pharmaceutically relevant properties of compounds were evaluated to analyze druggable properties [27 (link), 38 (link)]. Lipinski's rule of five was used to filter the compounds with drug-like properties [39 (link)]. To identify the best match molecules, the AAARRR.1061 hypothesis was applied for screening the IBScreen database with drug-like properties. Finally, we selected the compounds with the pIC50 value of more than 4 which were considered as the most active compounds, and then, further screening of these compounds is done by ADMET (absorption, distribution, metabolism, elimination, and toxicity) properties using QikProp version 4.3. The compounds complied with Lipinski's rule, with good predicted activity and good ADMET properties, were selected for molecular docking studies [24 (link), 32 ].
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2

Molecular Descriptor Calculation for PLS Regression

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All structures were built using the Maestro 10.1 from Schrödinger Suite 2015-1 (Maestro, version 10.1, Schrödinger, LLC, New York, NY). QikProp version 4.3 (QikProp, version 4.3, Schrödinger, LLC, New York, NY) was used for the calculation of physically significant molecular descriptors and pharmaceutically relevant properties. Single-point calculations using the RM1 method32 (link) from Semiempirical NDDO module of Scrodinger Suite 2015-1 was used for semiempirical parameters. The calculated molecular descriptors which are used in final PLS regression analysis are given in Table S1 (Supplemental Information).
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