First of a kind AI system: LLM classifiers trained via reinforcement learning to uncover outcome-based patterns in drug discovery research beyond human comprehension.
An AI system that solves the root-node problem in the pharmaceutical industry.
31% higher success rates over average assets
Validated on 83,501 novel drug candidates reported in the literature in a quasi-prospective test — Explority AI sources 50.7% of approved orphan therapies from the published research with greater precision than the industry’s average early-stage success rate.
5-year competitive edge in portfolio
By continuously analyzing 100,000+ of drug discovery papers across 5,846 rare diseases annually, Explority AI identifies the next successful first- and best-in-class therapies up to five years before they receive orphan designation, across small molecules, antibodies, therapeutic proteins, RNAs, gene and cell therapies.
De-Risking Decisions
Validation
Assessing
Explority AI
precision
Quasi-prospective setup
Trained on literature published up to 2019, we validated our LLMs on all 83,501 new drug candidates mentioned in these texts and used new orphan drug designations from 2019 to 2025 as positive outcomes.
Results across 5,846 rare diseases
Explority AI system identified 50.7% of orphan therapies that would reach approval – on average 2.5 years earlier and with greater precision than the industry’s average success rate.
Our solutions
How we help accelerate research and decision-making.
Solutions for all early-stage rare disease decisions with data-driven preclinical probability of success forecasts.
Welcome to Explority