Receptor.AI is advancing the study of cryptic pockets in drug discovery, an area focused on targeting previously inaccessible regions of proteins. These hidden binding sites become accessible through specific conformational changes, presenting new possibilities for selectivity and therapeutic intervention.

The process begins with AI-driven virtual screening in the "bootstrapping" phase to identify molecules that reveal cryptic pockets by inducing protein conformational changes. This is followed by employing quantum simulations and molecular dynamics to generate accurate protein conformations, facilitating the identification of potential drug targets.

Subsequently, techniques such as cryo-electron microscopy (CryoEM) are used to analyze protein complexes and examine cryptic site characteristics. AI-driven models help prioritize candidate targets, and virtual screening is employed to identify potential compounds. The process concludes with compound validation to assess their ability to engage the identified pockets.

Receptor.AI's approach efficiently bridges computational prediction and experimental validation, offering a pragmatic solution to drug discovery challenges. This streamlined process not only accelerates the discovery of new therapeutic agents but also optimizes resource use, marking a significant advancement in the pursuit of novel drug targets within the hidden recesses of protein structures.

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