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

Area of Science:

• Medicinal Chemistry
• Computational Chemistry
• Drug Discovery

Background:

• Multitarget drugs offer advantages over single-target therapies for improved efficacy and safety.
• Designing effective multitarget drugs remains a significant challenge in pharmaceutical research.

Purpose of the Study:

• To apply a novel Group based QSAR (GQSAR) method for lead optimization and scaffold hopping of multitarget inhibitors.
• To identify key fragment-based features for designing potent multitarget compounds.

Main Methods:

• Collected and analyzed diverse datasets of multikinase (PDGFR-beta, FGFR-1, SRC) and multiserotonin (serotonin receptor 1A, serotonin transporter) inhibitors.
• Utilized a novel Group based QSAR (GQSAR) approach, dividing molecules into fragments and calculating 2D descriptors.
• Developed multiresponse regression GQSAR models for both inhibitor datasets.

Main Results:

• The developed GQSAR models proved effective for scaffold hopping and lead optimization of multitarget inhibitors.
• Identified crucial fragment-based features essential for constructing potent multitarget inhibitors.
• The method facilitates the design of combinatorial libraries for discovering optimal multitarget drug candidates.

Conclusions:

• The novel GQSAR method is a valuable tool for accelerating the development of multitarget inhibitors.
• Fragment-based insights from GQSAR can guide the rational design of next-generation therapeutics.
• This approach enhances the efficiency and success rate of multitarget drug discovery efforts.