Decrypting cryptic pockets with physics-based simulations and artificial intelligence.

Area of Science:

• Drug discovery and computational chemistry.

Background:

• Cryptic pockets represent a largely untapped resource for drug discovery, offering potential to target proteins previously considered undruggable.
• The transient and hidden nature of these pockets makes them challenging to identify using traditional experimental methods.

Purpose of the Study:

• To review recent advancements in computational approaches for identifying and characterizing cryptic pockets.
• To highlight the integration of different computational strategies for enhanced discovery and functional interpretation.

Main Methods:

• Physics-based molecular dynamics simulations.
• Artificial intelligence (AI)-driven models.
• Hybrid strategies combining physics-based and AI approaches.

Main Results:

• Computational methodologies have significantly improved the ability to detect and analyze cryptic pockets.
• These advanced methods enable the characterization of previously inaccessible binding sites.
• The review synthesizes key developments in computational tools for drug discovery.

Conclusions:

• Computational approaches are essential for overcoming the challenges in discovering cryptic pockets.
• These methods expand the druggable proteome by revealing new therapeutic targets.
• Future drug discovery efforts can leverage these computational tools for novel therapeutic strategies.