The covalent docking software landscape: features and applications in drug design

Area of Science:

• Medicinal Chemistry
• Computational Drug Discovery
• Chemical Biology

Background:

• Covalent small-molecule ligands are increasingly vital in drug discovery for prolonged target engagement and modulating previously undruggable proteins.
• Rational design and virtual screening (VS) of covalent ligands are computationally challenging due to difficulties in modeling covalent bond formation energetics.
• Traditional docking tools often require advanced quantum mechanical (QM) calculations for accurate predictions, highlighting a need for improved computational methods.

Purpose of the Study:

• To review the fundamental principles and mechanisms of covalent inhibition.
• To provide a comprehensive overview of computational tools for covalent ligand design and VS.
• To discuss the role of AI and machine learning (ML) in advancing covalent drug discovery.

Main Methods:

• Review of computational tools including covalent docking, warhead placement algorithms, and pharmacophore modeling.
• Discussion of AI/ML tools for prioritizing covalent ligand candidates.
• Inclusion of case studies, curated databases, and tools for assessing nucleophilic residue reactivity.

Main Results:

• The computational landscape for covalent ligand discovery is rapidly evolving with new open-source, commercial, and web-based platforms.
• AI and ML tools are increasingly assisting in the prioritization of candidate molecules.
• Existing computational tools enable rational design, hypothesis refinement, and expansion of druggability.

Conclusions:

• Despite challenges, advancements in computational tools significantly impact early-stage drug discovery and chemical biology for covalent ligands.
• Current covalent docking and AI-driven approaches facilitate rational design and innovation in drug discovery.
• Further advances are needed, but existing tools already expand the boundaries of druggable targets.