FATHOM®: The Experimental Truth Layer For AI-Native Drug Discovery

AI is accelerating molecular design, but computational speed cannot resolve every uncertainty surrounding protein behavior. Dynamic targets may occupy multiple conformational states, and overlooking those states can lead researchers to prioritize compounds that bind effectively yet produce the wrong biological outcome. Examples involving HSP90, MDM2 and MDMX, and PD-L1 show how gaps between structural predictions and physiological behavior can create costly downstream risks. FATHOM® uses electron paramagnetic resonance spectroscopy to measure which protein conformations exist in solution, their relative populations, and how ligand binding shifts the ensemble. By adding experimental feedback to structure prediction, hit validation, and lead optimization, the approach can help teams test AI-generated hypotheses before committing greater resources.

Explore how conformational measurements can refine computational models, support candidate selection, and reduce uncertainty across AI-enabled drug discovery.