compare outcomes
Move from
research
to results.
to results.
We help teams find successful rare disease therapies with clarity and speed.
compare outcomes
Move from
research
to results.
to results.
We help teams find successful rare disease therapies with clarity and speed.
compare outcomes
Move from
research
to results.
to results.
We help teams find successful rare disease therapies with clarity and speed.
First-of-its-kind AI system, trained on 1M+ outcome-linked papers, for confident R&D and partnership decisions across rare diseases.
Explority AI
ChatBots
Technology
Technology
Trained to Forecast Likelihood of Approval
Trained to Forecast Likelihood of Approval
LLMs as Outcome-Trained Classifiers
LLMs as Outcome-Trained Classifiers
Beyond Human Pattern Recognition
Beyond Human Pattern Recognition
Validated Impact
Validated Impact
Explority AI
Average pharma outcomes
Average pharma outcomes
Deal-sourcing & partnerships
Deal-sourcing & partnerships
Deal Sourcing Process
Automated, data-driven
Manual, prone to missed opportunities
Deal Sourcing Process
Automated, data-driven
Manual, prone to missed opportunities
Avg. Preclinical Success Rate
3.3% (+31%)
2.5%
Avg. Preclinical Success Rate
3.3% (+31%)
2.5%
First-Mover Advantage
up to 5 years
First-Mover Advantage
up to 5 years
Deal Sourcing Coverage
5,846 rare diseases
Limited
Deal Sourcing Coverage
5,846 rare diseases
Limited
Pipeline and R&D
Pipeline and R&D
Candidates Prioritization
De-risked, diverse
Limeted coverage
Candidates Prioritization
De-risked, diverse
Limeted coverage
Avg. Preclinical Success Rate
3.3% (+31%)
2.5%
Avg. Preclinical Success Rate
3.3% (+31%)
2.5%
Novel targets
Novel targets
Full Drug Discovery Landscape
Full Drug Discovery Landscape
Pipeline Lead-Time
up to 5 years
Pipeline Lead-Time
up to 5 years
Repurposing Opportunities
Repurposing Opportunities
Explority AI impact
Results
Industry-Level Impact
Improvement of industry-wide success rates by transforming fragmented, irreproducible research into predictive insight, bridging academia and industry, and accelerating the translation of early-stage research into approved therapies.
Company-Level Impact
Improvement of R&D ROI by prioritizing high-potential therapies early, shortening research-to-clinic timelines, reducing wasted effort, and enabling teams to focus on the most promising opportunities for development and investment.
Frequently Asked Questions
How AI works
Impact analysis
Orphan drugs
How is your model different from ChatGPT or other LLMs?
ChatGPT generates human-like text. Our models are classifiers trained with reinforcement learning to discover patterns in scientific papers based on real-world outcomes, allowing them to detect signals beyond human reasoning—similar to how AlphaFold learned protein folding from real word outcomes, not text imitation.
What differentiates you from other AI in pharma?
We built the first AI system that outperforms the pharmaceutical industry in identifying successful therapies at the discovery and preclinical stages. It identifies 50.7% of future approved orphan therapies earlier and with higher precision by linking scientific research papers to their real-world outcomes at unprecedented scale and using this data as a dedicated training dataset.
Much scientific research is irreproducible—how do you handle this?
Our model learns which research findings actually lead to therapies and which do not, effectively distinguishing reproducible science from noise. This makes the system a practical solution to the irreproducibility problem in biomedical research.
How interpretable is your model?
Because our system is a classifier, we can quantify how every token, word and paper influences each prediction. We provide influence scores and can visualize which parts of the text drove the model’s conclusions.
How can your results be evaluated?
We offer full transparency of our results. They can be evaluated through review of current predictions, independent validation on a quasi-prospective benchmark, and full retraining and validation of our quasi-prospective models.
How do you handle publication copyright and licensing?
We source data from PubMed and OpenAlex and only display content when licenses allow. Investment memorandums for deal soursing include only titles and abstracts from publications available under open licenses.
Drug decisions require more than scientific papers—how do you address this?
Each prediction includes a full generated investment memorandum covering population size, disease burden, current treatments, development timelines, and relevant companies and scientists.
Who owns the IP for therapies identified by your forecasts?
We do not claim or request any IP rights. Our work is purely predictive, and all intellectual property remains with the original inventors or organizations.
Do you specialize in any particular areas?
Absolutely. In fact, many of our most successful projects are built on close collaboration with internal R&D, data science, or innovation units. We integrate seamlessly, offering fresh perspectives while respecting existing knowledge and workflows. Our role is to complement, not replace.
Why do you focus only on orphan (rare) diseases?
We plan to expand to all diseases as we scale, but orphan diseases are the strongest starting point. Over half of new FDA approvals are orphan drugs, they deliver much higher investment returns, and an increasing share of blockbusters are orphan therapies.
What data did you use to train your models on orphan drugs?
We used 10,000+ orphan drug designations, including 1,200+ approvals as positive outcomes. As a source of information for training, we used the texts of 15M research articles about rare diseases, filtered down to 1M articles relevant to drug discovery, and linked them to orphan designations and approvals as outcomes to train our models.
Why use orphan drug designation as a training target?
Orphan designation is a strong early signal of future success and typically occurs after Phase I or II. Moving from a paper idea (<1% likelihood of approval) to orphan designation (25%) marks a major validation milestone, making it an ideal target for training predictive models.
How significant is the burden of 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.
Our AI
Impact
Orphan drugs
How is your model different from ChatGPT or other LLMs?
ChatGPT generates human-like text. Our models are classifiers trained with reinforcement learning to discover patterns in scientific papers based on real-world outcomes, allowing them to detect signals beyond human reasoning—similar to how AlphaFold learned protein folding from real word outcomes, not text imitation.
What differentiates you from other AI in pharma?
We built the first AI system that outperforms the pharmaceutical industry in identifying successful therapies at the discovery and preclinical stages. It identifies 50.7% of future approved orphan therapies earlier and with higher precision by linking scientific research papers to their real-world outcomes at unprecedented scale and using this data as a dedicated training dataset.
Much scientific research is irreproducible—how do you handle this?
Our model learns which research findings actually lead to therapies and which do not, effectively distinguishing reproducible science from noise. This makes the system a practical solution to the irreproducibility problem in biomedical research.
How interpretable is your model?
Because our system is a classifier, we can quantify how every token, word and paper influences each prediction. We provide influence scores and can visualize which parts of the text drove the model’s conclusions.
How can your results be evaluated?
We offer full transparency of our results. They can be evaluated through review of current predictions, independent validation on a quasi-prospective benchmark, and full retraining and validation of our quasi-prospective models.
How do you handle publication copyright and licensing?
We source data from PubMed and OpenAlex and only display content when licenses allow. Investment memorandums for deal soursing include only titles and abstracts from publications available under open licenses.
Drug decisions require more than scientific papers—how do you address this?
Each prediction includes a full generated investment memorandum covering population size, disease burden, current treatments, development timelines, and relevant companies and scientists.
Who owns the IP for therapies identified by your forecasts?
We do not claim or request any IP rights. Our work is purely predictive, and all intellectual property remains with the original inventors or organizations.
Do you specialize in any particular areas?
Absolutely. In fact, many of our most successful projects are built on close collaboration with internal R&D, data science, or innovation units. We integrate seamlessly, offering fresh perspectives while respecting existing knowledge and workflows. Our role is to complement, not replace.
Why do you focus only on orphan (rare) diseases?
We plan to expand to all diseases as we scale, but orphan diseases are the strongest starting point. Over half of new FDA approvals are orphan drugs, they deliver much higher investment returns, and an increasing share of blockbusters are orphan therapies.
What data did you use to train your models on orphan drugs?
We used 10,000+ orphan drug designations, including 1,200+ approvals as positive outcomes. As a source of information for training, we used the texts of 15M research articles about rare diseases, filtered down to 1M articles relevant to drug discovery, and linked them to orphan designations and approvals as outcomes to train our models.
Why use orphan drug designation as a training target?
Orphan designation is a strong early signal of future success and typically occurs after Phase I or II. Moving from a paper idea (<1% likelihood of approval) to orphan designation (25%) marks a major validation milestone, making it an ideal target for training predictive models.
How significant is the burden of 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.
Still have questions? Get in touch with our team and we'll discuss your unique requirements.