Novel Efficient Multistage Lead Optimization Pipeline Experimentally Validated for DYRK1B Selective Inhibitors

Area of Science:

• Biochemistry
• Drug Discovery
• Computational Chemistry

Background:

• Developing targeted cancer therapies requires specific inhibitors for molecular targets like DYRK1B kinase.
• Existing inhibitors lack selectivity, inhibiting both DYRK1B and the related DYRK1A kinase, which is not therapeutically beneficial.
• The absence of a DYRK1B crystal structure complicates the design of selective inhibitors.

Purpose of the Study:

• To develop a novel computational pipeline for identifying potent and selective small-molecule inhibitors of DYRK1B.
• To address the challenge of discriminating between closely related kinases in drug discovery.
• To discover lead and runner-up compounds targeting DYRK1B for potential cancer chemotherapy.

Main Methods:

• A multi-stage compound discovery pipeline was designed for *in silico* identification.
• Structure-based docking was employed to screen a large set of initial candidates.
• Ligand-based quantitative structure-activity relationship (QSAR) modeling was utilized to refine selectivity and potency.

Main Results:

• The computational pipeline successfully identified small-molecule compounds with high efficiency and specificity for DYRK1B.
• Lead and runner-up compounds targeting DYRK1B were discovered through the proposed methodology.
• The approach demonstrated the feasibility of *in silico* identification of selective kinase inhibitors.

Conclusions:

• The developed multi-stage computational pipeline is effective for discovering potent and selective DYRK1B inhibitors.
• This strategy offers a promising solution for overcoming challenges in developing targeted cancer therapies.
• The identified compounds represent potential candidates for further development in DYRK1B-targeted cancer chemotherapy.