De-Risk Discovery
Pharma's low
early-stage success
Pharma's low
early-stage success
rate
rate
rigour.
solved by AI.
solved by AI.
The first AI that improves success rates of early-stage R&D and portfolio decisions across 5,846 rare diseases.
The first AI that improves success rates of early-stage R&D and portfolio decisions across 5,846 rare diseases.
One solution for all early-stage rare disease decisions
Automated deal sourcing for Big Pharma
Automated deal sourcing for Big Pharma
Automated deal sourcing for Big Pharma
De-risked pipeline for Biotech companies
De-risked pipeline for Biotech
De-risked pipeline for Biotech companies
The first AI that solved pharma's low success rate
Explority — an LLM-based AI system trained on 1M+ outcome-linked papers that forecasts the likelihood of approval for early-stage drug candidates, outperforming traditional preclinical success rates and identifying the highest-potential orphan therapies for drug development, investment, and partnering.
Boost early-stage success rate
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 while exceeding the industry’s average early-stage success rate.
Gain competitive edge in the pipeline
5-year competitive edge in portfolio
By continuously analyzing 100,000+ of drug discovery papers 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.
Expand drug discovery scope
5,846 rare diseases covered
Comprehensive coverage of drug discovery across 5,846 rare diseases, performing the work equivalent to over 200 full-time scientific advisors by evaluating publication landscapes that would take 14.4 years to read annually, bridging the gap between academia and industry.
Boost early-stage success rate
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 while exceeding the industry’s average early-stage success rate.
Gain competitive edge in the pipeline
5-year competitive edge in portfolio
By continuously analyzing 100,000+ of drug discovery papers 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.
Expand drug discovery scope
5,846 rare diseases covered
Comprehensive coverage of drug discovery across 5,846 rare diseases, performing the work equivalent to over 200 full-time scientific advisors by evaluating publication landscapes that would take 14.4 years to read annually, bridging the gap between academia and industry.
Boost early-stage success rate
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 while exceeding the industry’s average early-stage success rate.
Gain competitive edge in the pipeline
5-year competitive edge in portfolio
By continuously analyzing 100,000+ of drug discovery papers 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.
Expand drug discovery scope
5,846 rare diseases covered
Comprehensive coverage of drug discovery across 5,846 rare diseases, performing the work equivalent to over 200 full-time scientific advisors by evaluating publication landscapes that would take 14.4 years to read annually, bridging the gap between academia and industry.
our ai system
our ai system
our ai system
How it works
Our AI is not a ChatBot. It is a 1) state-of-the-art algorithm that structures published research and links it to clinical outcomes 2) LLMs trained as classifiers on this data to predict the likelihood of approval for novel therapy candidates.
1M drug discovery publications across 5,846 rare diseases, matched with outcomes as a training dataset.
1M drug discovery publications across 5,846 rare diseases, matched with outcomes as a training dataset.
1M drug discovery publications across 5,846 rare diseases, matched with outcomes as a training dataset.
10,000+ orphan designations and 1,200+ approvals as positive outcomes.
10,000+ orphan designations and 1,200+ approvals as positive outcomes.
10,000+ orphan designations and 1,200+ approvals as positive outcomes.
LLMs incorporate learnings from all previous drug discovery results into every new decision.
LLMs incorporate learnings from all previous drug discovery results into every new decision.
LLMs incorporate learnings from all previous drug discovery results into every new decision.
Understanding patterns that surpass human reasoning.
Understanding patterns that surpass human reasoning.
Understanding patterns that surpass human reasoning.
Drive higher drug discovery success with outcome-trained AI
Drive higher success with
Drive higher success with
outcome-trained AI.
outcome-trained AI.
Solutions to empower Big Pharma, Biotech and VCs decisions with published research landscapes, paired with data-driven probability of success forecasts.
Automated deal sourcing
Continuous monitoring of emerging novel therapies with the highest approval potential, as well as the startups and scientists developing them, delivered as investment memorandums personalized to specific therapeutic focus areas.
Automated deal sourcing
Continuous monitoring of emerging novel therapies with the highest approval potential, as well as the startups and scientists developing them, delivered as investment memorandums personalized to specific therapeutic focus areas.
Automated deal sourcing
Continuous monitoring of emerging novel therapies with the highest approval potential, as well as the startups and scientists developing them, delivered as investment memorandums personalized to specific therapeutic focus areas.
De-risked pipeline
Make confident early-stage investments and R&D choices with drug discovery landscapes for 5,846 rare diseases featuring likelihood of approval percentiles.
De-risked pipeline
Make confident early-stage investments and R&D choices with drug discovery landscapes for 5,846 rare diseases featuring likelihood of approval percentiles.
De-risked pipeline
Make confident early-stage investments and R&D choices with drug discovery landscapes for 5,846 rare diseases featuring likelihood of approval percentiles.
Drug repurposing
Selecting and ranking successful opportunities for therapeutic repurposing by targets and mechanisms across more than 1 million drug discovery publications.
Drug repurposing
Selecting and ranking successful opportunities for therapeutic repurposing by targets and mechanisms across more than 1 million drug discovery publications.
Drug repurposing
Selecting and ranking successful opportunities for therapeutic repurposing by targets and mechanisms across more than 1 million drug discovery publications.
De-Risking Decisions
Frequently Asked Questions
How is your AI different from Gen AI or ChatBots?
Explority AI is not based on text generation. Our LLMs are outcome-trained classifiers that learn patterns in scientific paper results linked to real drug-development results – enabling likelihood of approval forecasts, not text imitation. Our LLMs learn directly from drug discovery outcomes, similar to how AlphaFold learned from nature’s outcomes how to fold proteins. In contrast, models like GPT-5 cannot learn protein folding or accurately forecast the likelihood of approval just by mimicking human writing.
How is your AI different from Gen AI or ChatBots?
Explority AI is not based on text generation. Our LLMs are outcome-trained classifiers that learn patterns in scientific paper results linked to real drug-development results – enabling likelihood of approval forecasts, not text imitation. Our LLMs learn directly from drug discovery outcomes, similar to how AlphaFold learned from nature’s outcomes how to fold proteins. In contrast, models like GPT-5 cannot learn protein folding or accurately forecast the likelihood of approval just by mimicking human writing.
How is your AI different from Gen AI or ChatBots?
Explority AI is not based on text generation. Our LLMs are outcome-trained classifiers that learn patterns in scientific paper results linked to real drug-development results – enabling approval forecasts, not text imitation. Our LLMs learn directly from drug discovery outcomes, similar to how AlphaFold learned from nature’s outcomes how to fold proteins. In contrast, models like GPT-5 cannot learn protein folding just by mimicking human writing or accurately forecast the likelihood of approval.
Do I need to enter prompts to get results?
No. Explority AI continuously analyzes 100,000+ new drug discovery papers on its own. You can access calculated probabilities of success by searching for a drug discovery landscape for a specific disease, or receive personalized memorandums highlighting the most promising therapies tailored to your focus areas.
Do I need to enter prompts to get results?
No. Explority AI continuously analyzes 100,000+ new drug discovery papers on its own. You can access calculated probabilities of success by searching for a drug discovery landscape for a specific disease, or receive personalized memorandums highlighting the most promising therapies tailored to your focus areas.
Do I need to enter prompts to get results?
No. Explority AI continuously analyzes all drug discovery research on its own. You can access this data-driven probability of success forecasts by searching for a specific disease, or receive personalized memorandums highlighting the most promising therapies tailored to your focus areas.
What problems Explority AI solves?
Industry-level problems: • Low success rates of early-stage therapies • Irreproducible research • Gap between academia and industry Company-level problems: • Declining ROI in drug discovery • Long research-to-clinic timelines • Difficulty staying ahead of the competition • Excessive number of new ideas to monitor Decision-maker problems: • Limited resources for in-depth literature analysis and validation of prior research • Low automation of deal sourcing and evaluation • Lack of aggregated information for decision making
What problems Explority AI solves?
Industry-level problems: • Low success rates of early-stage therapies • Irreproducible research • Gap between academia and industry Company-level problems: • Declining ROI in drug discovery • Long research-to-clinic timelines • Difficulty staying ahead of the competition • Excessive number of new ideas to monitor Decision-maker problems: • Limited resources for in-depth literature analysis and validation of prior research • Low automation of deal sourcing and evaluation • Lack of aggregated information for decision making
What problems Explority AI solves?
Industry-level problems: - Low success rates of early-stage therapies - Irreproducible research - Gap between academia and industry Company-level problems: - Declining ROI in drug discovery - Long research-to-clinic timelines - Difficulty staying ahead of the competition - An excessive amount of new ideas for monitoring Decision-maker problems: - Limited resources for in-depth literature analysis and validation of prior research - Low automation of deals sourcing and evaluation - A lack of aggregated information for decision making - A narrow scope of conferences and external submissions for discovering new assets
How are results validated?
Explority AI was tested through a quasi-prospective validation of its LLM classifiers, which were trained on articles published up to the 2019 knowledge cutoff. The validation included 83,501 new drug candidates described in the literature and used new orphan drug designations (2019–2025) across 5,846 rare diseases as the outcome measure. As a result, Explority AI identified 50.7% of orphan therapies that would eventually reach approval – on average 2.5 years earlier and with greater precision than the industry’s average success rate, demonstrating the potential to increase early-stage pipeline success rates by 31%.
How are results validated?
Explority AI was tested through a quasi-prospective validation of its LLM classifiers, which were trained on articles published up to the 2019 knowledge cutoff. The validation included 83,501 new drug candidates described in the literature and used new orphan drug designations (2019–2025) across 5,846 rare diseases as the outcome measure. As a result, Explority AI identified 50.7% of orphan therapies that would eventually reach approval – on average 2.5 years earlier and with greater precision than the industry’s average success rate, demonstrating the potential to increase early-stage pipeline success rates by 31%.
How are results validated?
Explority AI was tested through a quasi-prospective validation of its LLM classifiers, which were trained on articles published up to the 2019 knowledge cutoff. The validation included 83,501 new drug candidates described in the literature and used new orphan drug designations (2019–2025) across 5,846 rare diseases as the outcome measure. As a result, Explority AI identified 50.7% of orphan therapies that would eventually reach approval – on average 2.5 years earlier and with greater precision than the industry’s average success rate, demonstrating the potential to increase early-stage pipeline success rates by 31%.
Why invest in orphan drugs?
54% of recent FDA approvals belong to orphan therapies. The average ROI of orphan drug investment is 3.8x higher than for non-orphan drugs.
Why invest in orphan drugs?
54% of recent FDA approvals belong to orphan therapies. The average ROI of orphan drug investment is 3.8x higher than for non-orphan drugs.
Why invest in orphan drugs?
54% of recent FDA approvals belong to orphan therapies. The average ROI of orphan drug investment is 3.8x higher than for non-orphan drugs.
How can I get access to the Explority AI platform?
To get access, please book a demo meeting with our team. During the session, we will walk you through the platform and showcase personalized, highest-potential therapy candidates tailored to your pipeline or partnership focus areas.
How can I get access to the Explority AI platform?
To get access, please book a demo meeting with our team. During the session, we will walk you through the platform and showcase personalized, highest-potential therapy candidates tailored to your pipeline or partnership focus areas.
How can I get access to the Explority AI platform?
To get access, please book a demo meeting with our team. During the session, we will walk you through the platform and showcase personalized, highest-potential therapy candidates tailored to your pipeline or partnership focus areas.
How Explority AI is validated to assess the accuracy of early-stage forecasts, delivering precision that consistently exceeds industry success rates:
50.7%
of approved orphan therapies came from the top 8% of highest-scored research
31%
lower failure rate in therapies selected by Explority AI
5-year
earlier predictions to shorten research-to-approval time
50.7%
of approved orphan therapies came from the top 8% of highest-scored research
31%
lower failure rate in therapies selected by Explority AI
5-year
earlier predictions to shorten research-to-approval time
50.7%
of approved orphan therapies came from the top 8% of highest-scored research
31%
lower failure rate in therapies selected by Explority AI
5-year
earlier predictions to shorten research-to-approval time
01
Quasi-prospective validation across 5,846 rare diseases
Trained on literature published up to 2019, we validated our LLMs on 83,501 new drug candidates mentioned in these texts and used new orphan drug designations (2019–2025) as positive outcomes.
01
Quasi-prospective validation across 5,846 rare diseases
Trained on literature published up to 2019, we validated our LLMs on 83,501 new drug candidates mentioned in these texts and used new orphan drug designations (2019–2025) as positive outcomes.
01
Quasi-prospective validation across 5,846 rare diseases
Trained on literature published up to 2019, we validated our LLMs on 83,501 new drug candidates mentioned in these texts and used new orphan drug designations (2019–2025) as positive outcomes.
02
Outperforming industry in precision
Explority AI 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.
02
Outperforming industry in precision
Explority AI 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.
02
Outperforming industry in precision
Explority AI 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.
03
Boosting early-stage success rates
With a validated +31% increase in average early-stage success rates, accelerating development timelines and improving decision-making across rare disease drug pipelines and partnerships.
03
Boosting early-stage success rates
With a validated +31% increase in average early-stage success rates, accelerating development timelines and improving decision-making across rare disease drug pipelines and partnerships.
03
Boosting early-stage success rates
With a validated +31% increase in average early-stage success rates, accelerating development timelines and improving decision-making across rare disease drug pipelines and partnerships.
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