To address this, we have trained the first LLM-based AI capable of forecasting the likelihood of approval for early-stage therapies at scale. It helps ensure the industry does not overlook treatments with the greatest potential for success.

Our initial focus is orphan drugs for rare diseases.

Rare diseases affect 350 million people globally, yet only 5% of the 7,000 known orphan conditions have treatments. At Explority, we’re bridging the gap between academic innovation and life-saving therapies to change that.

The goal is simple: earlier clarity, fewer dead ends, and faster progress in bringing rare disease therapies to patients.

How it works

We harness the power of specially trained large language models (LLMs) to predict the probability of approval for novel drug candidates using text-based data, enabling the identification of the most promising orphan drug opportunities years before they reach clinical trials. By automating the analysis of 100,000+ scientific papers annually, we uncover hidden signals — predicting FDA and EMA orphan drug designations with up to 5 years of lead time and accelerating research-to-clinic timelines.

Under validated conditions, Explority AI outperformed the average success rates of the pharmaceutical industry in precision across the entire orphan drug discovery landscape, demonstrating a significantly higher ability to identify candidates that ultimately achieve regulatory designation and clinical advancement.

Impact

• Boost early-stage success rate

• Filter irreproducible research

• Bridge academia-industry gap

• Increase R&D ROI

• Shorten research-to-approval time

• Gain competitive edge

• Expand discovery scope