CureRx: A Biology-First Generative AI Platform for Small-Molecule Drug Discovery

CureRx is a modular, AI-native platform that compresses discovery by combining generative chemistry, structure-based validation, and reinforcement-learning optimization—built to create repeatable, partner-grade assets in high-unmet-need indications.

CureRx runs a closed-loop discovery cycle:
Target triage → Generative design → Property prediction → Structure-based prioritization → Multi-objective optimization → Repeat.

Each iteration improves candidate quality across novelty, drug-likeness, synthesizability, safety and target-specific constraints—so we converge on high-confidence leads faster.

Drug discovery isn’t broken — it’s slow, serial, and capital-intensive. Traditional programs often take 3–5 years to reach proof-of-concept with $50–100M+ spent before meaningful de-risking. CureRx runs closed-loop learning before wet-lab spend, enabling rapid iteration and early elimination of false positives.