The company focuses on disease-specific 3D protein conformations, which exposes new binding sites (epitopes) not present on the same protein in healthy cells. This allows for highly selective drugs that avoid the toxicity common with targets defined by genetic sequence alone.

Contrary to the popular belief that antibody development is a bespoke craft, modern methods enable a reproducible, systematic engineering process. This allows for predictable creation of antibodies with specific properties, such as matching affinity for human and animal targets, a feat once considered a "flight of fancy."

Recludix succeeded in drugging SH2 domains, a target class abandoned in the 90s, by integrating five modern technologies. This platform includes proprietary DNA-encoded libraries, machine learning, a selectivity tool, novel crystallography methods, and a pro-drug approach to ensure cell permeability, demonstrating the complex approach needed for modern drug discovery breakthroughs.

The technology's main constraints are reaching proteins outside the intracellular space (membrane-bound or secreted) and the limited chemical libraries explored so far. These are viewed as engineering challenges that will be overcome with time and new ligases, not as permanent roadblocks.

Unlike traditional small molecules that need a pocket on a target protein, molecular glues work by changing the surface of an E3 ligase. This modified surface then perfectly matches and binds the target protein, enabling its degradation without requiring a direct drug-to-target binding site.

A-muto suggests many drug programs fail due to toxicity from hitting the wrong epitope, not a flawed biological concept. By identifying and targeting a structural epitope unique to the diseased state of the same protein, these previously abandoned but promising therapies could be salvaged.

For a modest 100-amino-acid protein, there are 10^130 possible sequences, while all life on Earth has only explored ~10^43. This vast, unexplored space means we can now design binders for "undruggable" targets that evolution never needed to create.

Instead of screening billions of nature's existing proteins (a search problem), AI-powered de novo design creates entirely new proteins for specific functions from scratch. This moves the paradigm from hoping to find a match to intentionally engineering the desired molecule.

Targeting the MYC cancer protein presents a dual challenge. Biologically, it's vital for healthy cells, creating a high risk of toxicity. Biophysically, its disordered, 'floppy' structure lacks the defined pockets that traditional drugs need to bind to, making it a 'holy grail' target.