Application of GQSAR for Scaffold Hopping and Lead Optimization in Multitarget Inhibitors

Authors

  • Subhash Ajmani,

    1. NovaLead Pharma Pvt. Ltd. Pride Purple Coronet, 1st floor, S No. 287, Baner Road, Pune 411 045, India tel/fax: +91-20-27291590
    2. Present address: Jubilant Biosys Ltd. #96, Industrial Suburb, 2nd Stage, Yeshwantpur, Bangalore – 560 022
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  • Sudhir A. Kulkarni

    Corresponding author
    1. NovaLead Pharma Pvt. Ltd. Pride Purple Coronet, 1st floor, S No. 287, Baner Road, Pune 411 045, India tel/fax: +91-20-27291590
    2. VLife Sciences Technologies Pvt. Ltd. Pride Purple Coronet, 1st floor, S No. 287, Baner Road, Pune 411 045, India
    • NovaLead Pharma Pvt. Ltd. Pride Purple Coronet, 1st floor, S No. 287, Baner Road, Pune 411 045, India tel/fax: +91-20-27291590
    Search for more papers by this author

Abstract

Many literature reports suggest that drugs against multiple targets may overcome many limitations of single targets and achieve a more effective and safer control of the disease. However, design of multitarget drugs presents a great challenge. The present study demonstrates application of a novel Group based QSAR (GQSAR) method to assist in lead optimization of multikinase (PDGFR-beta, FGFR-1 and SRC) and scaffold hopping of multiserotonin target (serotonin receptor 1A and serotonin transporter) inhibitors. For GQSAR analysis, a wide variety of structurally diverse multikinase inhibitors (225 molecules) and multiserotonin target inhibitors (162 molecules) were collected from various literature reports. Each molecule in the data set was divided into four fragments (kinase inhibitors) and three fragments (serotonin target inhibitors) and their corresponding two-dimensional fragment descriptors were calculated. The multiresponse regression GQSAR models were developed for both the datasets. The developed GQSAR models were found to be useful for scaffold hopping and lead optimization of multitarget inhibitors. In addition, the developed GQSAR models provide important fragment based features that can form the building blocks to guide combinatorial library design in the search for optimally potent multitarget inhibitors.

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