Integrated Covalent Drug Design Workflow Using Site Identification by Ligand Competitive Saturation.

Area of Science:

• Computational chemistry
• Drug discovery
• Biochemistry

Background:

• Traditional drugs form reversible bonds, limiting interaction duration.
• Irreversible covalent drugs offer enhanced therapeutic potential by forming stable bonds with target residues.
• Computational methods are crucial for identifying suitable protein targets and warhead functionalities for covalent drug design.

Purpose of the Study:

• To extend computational approaches for designing irreversible covalent drugs.
• To integrate protein flexibility, functional group interactions, and desolvation effects into drug design.
• To develop a comprehensive workflow for guiding covalent drug discovery.

Main Methods:

• Utilized site identification by ligand competitive saturation (SILCS) with explicit solvent all-atom molecular simulations.
• Employed SILCS-Monte Carlo (SILCS-MC) docking to identify reactive cysteine residues.
• Developed a machine learning model incorporating SILCS-MC metrics and experimental data to assess warhead reactivity.
• Evaluated the ranking of covalent binders using SILCS ligand grid free energy (LGFE).

Main Results:

• Successfully identified reactive cysteines on target proteins using SILCS-MC.
• Quantified the effectiveness of various warhead groups through a machine learning model.
• Demonstrated the ability to rank covalent binders with similar warheads using LGFE.
• Validated the integrated SILCS-Covalent workflow for informing covalent drug discovery.

Conclusions:

• The developed SILCS-Covalent workflow effectively identifies reactive sites and quantifies warhead potential for covalent drug design.
• This integrated approach enhances the prediction of drug-target interactions and binding specificity.
• SILCS-Covalent provides a powerful tool for both qualitative and quantitative guidance in irreversible covalent drug discovery.