Modern small-molecule drug discovery is constrained by practical screening limits, both experimental and computational. Identifying hits for novel targets typically requires extensive screening, which in practice is restricted to relatively small and chemically biased libraries. Even the most advanced computational approaches, including co-folding, do not scale to broad chemical exploration and therefore operate on libraries far below the few million compounds that represent a practical upper limit. As a result, discovery efforts stay close to familiar chemistry to control cost and timelines, limiting chemical diversity, reinforcing historical bias, and leaving many potentially better drugs undiscovered.
ProPhet addresses this limitation with an AI-driven discovery platform capable of screening billions of molecules at once, without relying on structural information or large-scale experimental screening. By making broad and diverse chemical exploration practical instead of risky, ProPhet enables teams to search far beyond standard chemical libraries without slowing development or increasing cost, opening new possibilities for targets previously considered out of reach.
In addition to scale, ProPhet provides early insight into selectivity and off-target binding, helping reduce late-stage toxicity risk and avoid unnecessary experimental investment. Together, these capabilities make a wider range of historically “undruggable†targets accessible in practice.
Here we will present the principles behind ProPhet’s screening approach, discuss its application across challenging targets, and share early validation results from ongoing collaborations with pharma, biotech, and academic partners.