In-silico Studies of Heterocyclic Benzoxazole Derivatives as an Anticancer Agent: Molecular Docking, 2D and 3D QSAR
Smita J. Pawar, Dhanashri Zope, Amol P. Kale
Abstract
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Smita J. Pawar, Dhanashri Zope, Amol P. Kale
Abstract
Open-access reader
In-silico molecular docking studies and QSAR study of benzoxazole derivatives synthesized by Kakkar et al. was done. Comparative studies of docking of 5-flurouracil and 20 compounds revealed presence of considerable interactions which indicates the affinity of newly synthesized compounds for thymidylate synthase. The statistically significant 2D-QSAR models were developed using molecular design suite (VLifeMDS 4.6). The study was performed with 20 compounds (data set) using sphere exclusion (SE) algorithm, random selection and manual selection methods used for the division of the data set into training and test set. Multiple linear regression [MLR] methodology with stepwise (SW) forward-backward variable selection method was used for building the QSAR models. The results of the 2D-QSAR models were further compared with 3D-QSAR models generated by kNN-MFA, (k-Nearest Neighbor Molecular Field Analysis) investigating the substitutional requirements for the favourable anticancer activity against HCT 116 cell line and providing useful information in the characterization and differentiation of their binding sites. The results derived may be useful for further designing of benzoxazole derivatives as anticancer agents prior to synthesis.
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In-silico molecular docking studies and QSAR study of benzoxazole derivatives synthesized by Kakkar et al. was done. Comparative studies of docking of 5-flurouracil and 20 compounds revealed presence of considerable interactions which indicates the affinity of newly synthesized compounds for thymidylate synthase. The statistically significant 2D-QSAR models were developed using molecular design suite (VLifeMDS 4.6). The study was performed with 20 compounds (data set) using sphere exclusion (SE) algorithm, random selection and manual selection methods used for the division of the data set into training and test set. Multiple linear regression [MLR] methodology with stepwise (SW) forward-backward variable selection method was used for building the QSAR models. The results of the 2D-QSAR models were further compared with 3D-QSAR models generated by kNN-MFA, (k-Nearest Neighbor Molecular Field Analysis) investigating the substitutional requirements for the favourable anticancer activity against HCT 116 cell line and providing useful information in the characterization and differentiation of their binding sites. The results derived may be useful for further designing of benzoxazole derivatives as anticancer agents prior to synthesis.
Key concepts: Quantitative structure–activity relationship, Benzoxazole, In silico, Docking (animal), Chemistry, Molecular descriptor, Computational biology, Stereochemistry