Solutions
Solutions
Solutions
We partner with pharmaceutical and biotech companies to offer a paradigm shift in success rates with our AI platform.
We partner with pharmaceutical and biotech companies to offer a paradigm shift in success rates with our AI platform.
We partner with pharmaceutical and biotech companies to offer a paradigm shift in success rates with our AI platform.
See how Explority AI can power early-stage pipelines and portfolios.
See how Explority AI can power early-stage pipelines and portfolios.
Highlighting next successful therapies at scale.
7,000+ early-stage therapies with above-average success likelihood, identified across 5,846 rare diseases — delivered as disease landscapes and personalized investment memorandums.
De-risking discovery
The first AI system capable of forecasting the likelihood of approval for early-stage therapies, outperforming the industry’s precision, with a focus on orphan therapies for rare diseases.
De-risking discovery
The first AI system capable of forecasting the likelihood of approval for early-stage therapies, outperforming the industry’s precision, with a focus on orphan therapies for rare diseases.
De-risking discovery
The first AI system capable of forecasting the likelihood of approval for early-stage therapies, outperforming the industry’s precision, with a focus on orphan therapies for rare diseases.
Big Pharma portfolios
Big Pharma portfolios
Big Pharma portfolios
Biotech pipelines
Biotech pipelines
Biotech pipelines
BioVCs investments
BioVCs investments
BioVCs investments
Automated deal sourcing
Receive high-potential novel therapies on autopilot, tailored to your therapeutic focus areas. View emerging therapy landscapes with likelihood of approval percentiles for strategic decision-making.
Personalized newsletters with highest-potential therapies
Memorandums covering population, disease burden, current treatments
Partnering and investment ready insights soursed from published research
31%
Higher success rate of sourced early-stage therapies.
5-year
Earlier detection to shorter research-to-approval timelines.
5,846
Rare diseases covered with automated deal sourcing.
Automated deal sourcing
Receive high-potential novel therapies on autopilot, tailored to your therapeutic focus areas. View emerging therapy landscapes with likelihood of approval percentiles for strategic decision-making.
Personalized newsletters with highest-potential therapies
Memorandums covering population, disease burden, current treatments
Partnering and investment ready insights soursed from published research
Automated deal sourcing
Receive high-potential novel therapies on autopilot, tailored to your therapeutic focus areas. View emerging therapy landscapes with likelihood of approval percentiles for strategic decision-making.
Personalized newsletters with highest-potential therapies
Memorandums covering population, disease burden, current treatments
Partnering and investment ready insights soursed from published research
31%
Higher success rate of sourced early-stage therapies.
5-year
Earlier detection to shorter research-to-approval timelines.
5,846
Rare diseases covered with automated deal sourcing.
De-risked pipeline
De-risk early-stage R&D decisions using comprehensive drug discovery landscapes across rare diseases, annotated with likelihood of approval percentiles for hundreds of thousands of drug candidates.
Find opportunities with the greatest success potential
Drug discovery landscapes for 5,846 rare diseases
Confident pipeline prioritization across unique targets
5-year
Competitive edge in early-stage pipeline.
31%
Lower failure rate in therapies selected by Explority AI.
5,846
Rare diseases covered for de-risked pipeline decisions.
De-risked pipeline
De-risk early-stage R&D decisions using comprehensive drug discovery landscapes across rare diseases, annotated with likelihood of approval percentiles for hundreds of thousands of drug candidates.
Find opportunities with the greatest success potential
Drug discovery landscapes for 5,846 rare diseases
Confident pipeline prioritization across unique targets
De-risked pipeline
De-risk early-stage R&D decisions using comprehensive drug discovery landscapes across rare diseases, annotated with likelihood of approval percentiles for hundreds of thousands of drug candidates.
Find opportunities with the greatest success potential
Drug discovery landscapes for 5,846 rare diseases
Confident pipeline prioritization across unique targets
5-year
Competitive edge in early-stage pipeline.
31%
Lower failure rate in therapies selected by Explority AI.
5,846
Rare diseases covered for de-risked pipeline decisions.
Drug repurposing
Identify and rank high-potential drug repurposing opportunities by analyzing targets and mechanisms across more than 1M biomedical publications.
Target and mechanism driven repurposing with highest success
Evidence mined from 1M+ drug discovery publications
Opportunities with the highest success rate for drug discovery platforms
5,846
Rare diseases are covered for repurposing.
5-year
Competitive edge in early-stage pipeline.
31%
Lower failure rate in therapies selected by Explority AI.
Drug repurposing
Identify and rank high-potential drug repurposing opportunities by analyzing targets and mechanisms across more than 1M biomedical publications.
Target and mechanism driven repurposing with highest success
Evidence mined from 1M+ drug discovery publications
Opportunities with the highest success rate for drug discovery platforms
Drug repurposing
Identify and rank high-potential drug repurposing opportunities by analyzing targets and mechanisms across more than 1M biomedical publications.
Target and mechanism driven repurposing with highest success
Evidence mined from 1M+ drug discovery publications
Opportunities with the highest success rate for drug discovery platforms
5,846
Rare diseases are covered for repurposing.
5-year
Competitive edge in early-stage pipeline.
31%
Lower failure rate in therapies selected by Explority AI.
From insights to
impact.
impact.
Let's accelerate therapies for rare diseases affecting over 350 million people worldwide.
From insights to
impact.
impact.
Let's accelerate therapies for rare diseases affecting over 350 million people worldwide.
From insights to
impact.
impact.
Let's accelerate therapies for rare diseases affecting over 350 million people worldwide.
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, drug discovery 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, drug discovery 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.
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, drug discovery 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.