What is the real market size of the AI in drug discovery market?
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In our AI in drug discovery market deck, you will find everything you need to understand the market
The AI in drug discovery market has entered a rapid growth phase in 2026.
Major pharmaceutical companies are now allocating meaningful budgets to AI-powered platforms that accelerate molecule identification and reduce preclinical failures.
And if you want to better understand this new industry, you can download our pitch covering the AI in drug discovery market.
Insights
- The AI in drug discovery market represents approximately 1.5% of the $194 billion global pharma R&D spend, demonstrating how software captures a small but high-value slice of discovery and preclinical workflows.
- North America holds 45% of AI in drug discovery revenue in 2026, but Asia is growing fastest and will reach 38% market share by 2036 as China, Japan, and Singapore accelerate adoption.
- Platform vendors offering end-to-end solutions capture 30% of 2026 revenue and will grow to 42% by 2036, as pharma buyers consolidate point solutions into unified discovery stacks.
- Preclinical prediction tools for ADMET and toxicity account for 12% of revenue in 2026 but will expand to 16% by 2036 because late-stage preclinical failures are extremely costly to fix.
- The market will grow from $2.9 billion in 2026 to $14.1 billion by 2036 under realistic assumptions, achieving a 14% compound annual growth rate driven by proven ROI in reducing wet-lab failures.
- Target identification and biology inference represent 18% of revenue in 2026, reflecting high willingness to pay for AI that reduces risk at the earliest and most uncertain phase of drug discovery.
- Drug repurposing generates 10% of AI discovery revenue in 2026 but will decline to just 2% by 2036, limited by the small number of approved drugs available for repositioning and lack of strong clinical anchors.
How do we define the AI in drug discovery market?
We define the AI in drug discovery market as revenue from AI-enabled software and related services that materially support the identification and design of therapeutic candidates before human trials.
We include AI used for target identification and validation, hit and lead generation, lead optimization, de novo design, repurposing, and preclinical prediction such as ADMET and toxicity assessment.
We exclude AI primarily used for clinical trial operations, regulatory submissions, manufacturing, pharmacovigilance, or commercial and enterprise analytics not directly tied to discovery and preclinical candidate selection.
We also use this definition when we make and update our pitch covering everything there is to know about the AI in drug discovery market
In our AI in drug discovery market deck, we will give you useful market maps and grids
What is the size of the AI in drug discovery market in 2026?
What results can we find on the internet?
As you probably know already, many firms regularly publish (sometimes conflicting) estimates of the AI in drug discovery market size, using different definitions, scopes, and years.
We have consolidated their results here. We will use it, among other things, to derive a single, reasonable estimate of the market size.
| Research Firm | Market Size | Year | Market Definition & Fit |
|---|---|---|---|
| MarketsandMarkets | $1.86B | 2024 | Covers AI in drug discovery market. Their scope is slightly broader than ours and may include some clinical decision support tools beyond candidate selection. |
| Grand View Research | $2.35B | 2025 | Measures AI in drug discovery market. This is a close match to our definition and includes discovery plus parts of preclinical testing. |
| Fortune Business Insights | $3.54B | 2023 | Analyzes AI in drug discovery market. Their definition is likely broader than ours and may include AI tools beyond candidate selection workflows. |
| Mordor Intelligence | $2.58B | 2025 | Reports on AI in drug discovery market. Close match to our scope but may include enabling infrastructure and some additional services. |
| Arizton | $1.71B | 2024 | Focuses on AI in drug discovery market. Close match with discovery-focused segmentation by application that aligns well with our definition. |
| Global Market Insights | $3.6B | 2024 | Covers AI in drug discovery market. Their definition is likely broader and could include more adjacent drug development tools beyond our scope. |
| Precedence Research | $6.93B | 2025 | Measures AI in drug discovery market. This estimate is very high compared to others and likely uses a broad definition with aggressive inclusion of services. |
What can we conclude, then?
Most credible estimates cluster between $1.7 billion and $2.6 billion for 2024 and 2025, with a few outliers that appear to use broader definitions.
For 2026, we estimate the AI in drug discovery market at approximately $2.9 billion, applying realistic single-year growth of around 18 to 25 percent from the 2025 midpoint of $2.45 billion. This is our first estimate, and we will refine it further using bottom-up calculations.
In our AI in drug discovery market deck, we have collected signals proving this market is hot right now
What if we try to make our own estimate?
We don't have to rely only on external analyses to estimate market size.
We will try to build a first-principles, bottom-up calculation, then run a few sanity checks to see whether we can reliably estimate the size of the AI in drug discovery market.
Useful data about the AI in drug discovery market
Here is some useful and reliable data we have collected, they will help us estimate the size of the AI in drug discovery market:
- Top 45 pharma companies reported $194.3 billion in R&D spending in 2024 (Hardman & Co)
- Pharma R&D spending grew by 1.5% in 2024 despite economic headwinds (BioSpace)
- FDA approved 50 new molecular entities in 2024 (Chemical & Engineering News)
- Global preclinical CRO market was valued at $6.19 billion in 2024 (Grand View Research)
- Eli Lilly and Nvidia announced a $1 billion investment over five years for joint AI research lab (Reuters)
- Illumina launched Billion Cell Atlas dataset to accelerate AI drug discovery in January 2026 (Reuters)
Method and calculation to get the size of the AI in drug discovery market
We start with the total pharma R&D budget pool that AI tools can realistically tap into. The top 45 pharmaceutical companies spent $194.3 billion on R&D in 2024.
AI in drug discovery does not capture all R&D spending. It mainly touches discovery work for finding molecules and preclinical testing decisions for predicting ADMET and toxicity risk.
We estimate that approximately 25% of pharma R&D relates to discovery and preclinical phases before clinical trials. This gives us an addressable activity spend of roughly $49 billion.
However, most of that spend goes to people, laboratories, wet experiments, and external CRO work. Only a small portion is dedicated to software and analytics tools.
If we assume that roughly 5 to 7% of discovery and preclinical activity spend could be software-like tools and services, we arrive at approximately $2.9 billion.
This makes intuitive sense. AI software and services can deliver tremendous value, but they still represent a small slice of total discovery and preclinical work in 2026.
Sanity checks
Let's verify this estimate makes sense (we always double-check everything, as you will see in our pitch deck covering the AI in drug discovery market).
Our estimate of $2.9 billion for 2026 fits naturally between the credible 2024 estimates of $1.7 to $1.9 billion and the 2025 estimates of $2.35 to $2.58 billion.
The preclinical CRO market was $6.19 billion in 2024, so if AI discovery were $2.9 billion, it would be about half of global preclinical CRO spending. This seems reasonable because AI is used both inside pharma companies and inside CROs, plus AI also covers discovery work beyond just preclinical outsourcing.
Major strategic investments like the $1 billion Eli Lilly and Nvidia partnership over five years support the conclusion that the AI in drug discovery market is in the low single-digit billions rather than hundreds of millions or tens of billions.
What's our final guess then?
Based on all available data and our bottom-up calculations, we estimate the AI in drug discovery market at approximately $2.9 billion in 2026.
To put this in perspective, the AI in drug discovery market is roughly half the size of the global preclinical CRO market, which was $6.19 billion in 2024.
The AI in drug discovery market is also about 1.5% of the total $194.3 billion pharma R&D spend across major companies. This ratio makes sense because AI tools are valuable but still represent a small fraction of overall discovery and preclinical activities.
Our estimate aligns with credible third-party research from firms like MarketsandMarkets, Grand View Research, and Mordor Intelligence, all of which report figures in the $1.7 billion to $2.6 billion range for 2024 and 2025.
The AI in drug discovery market is growing much faster than traditional pharmaceutical services markets but slower than pure software markets, reflecting the hybrid nature of this industry where cutting-edge technology meets heavily regulated drug development timelines.
In our AI in drug discovery market deck, we provide the data and the context to understand it
Is the AI in drug discovery market mature, competitive, fragmented?
The maturity score of the AI in drug discovery market in 2026 is 35/100
The AI in drug discovery market is still in its early stages despite rapid adoption by major pharmaceutical companies. Workflows are evolving quickly, and many organizations are still running pilot programs rather than fully integrated production systems.
Trust and validation cycles remain long in pharma R&D because companies need extensive evidence that AI predictions actually reduce expensive wet-lab failures. This means the AI in drug discovery market has significant room to mature as more success stories emerge and best practices become standardized across the industry.
The competitiveness score of the AI in drug discovery market in 2026 is 80/100
The AI in drug discovery market is highly competitive with many vendors offering overlapping discovery platforms for target identification, lead optimization, and preclinical prediction. New entrants continuously emerge with novel approaches to molecular design and ADMET prediction.
Large pharmaceutical companies can build in-house AI models using their proprietary data, which increases competitive pressure on external vendors. Additionally, major tech companies like Nvidia are partnering directly with pharma to create custom AI solutions, further intensifying competition in the AI in drug discovery market.
The fragmentation score of the AI in drug discovery market in 2026 is 75/100
The AI in drug discovery market is highly fragmented with many niche tools that specialize in specific areas like ADMET prediction, de novo molecular design, or target discovery. Buyers often use multiple vendors simultaneously instead of adopting one end-to-end platform.
This fragmentation reflects the complexity of drug discovery workflows where different AI approaches excel at different stages. However, platform vendors that offer comprehensive solutions are beginning to consolidate market share, which may reduce fragmentation over the next decade in the AI in drug discovery market.
How much bigger will the AI in drug discovery market be in 10 years?
What are the different forecasts for the growth rate of the AI in drug discovery market?
One more time, let's check what other market research firms have to say.
| Research Firm | Growth Rate | Until Year | Commentary and Adjustments |
|---|---|---|---|
| MarketsandMarkets | 29.9% CAGR | 2029 | This growth rate is strong but plausible for the AI in drug discovery market. Their scope may include adjacent drug development tooling beyond our definition. We should moderate this estimate slightly to account for our narrower scope focused on discovery and preclinical candidate selection. |
| Grand View Research | 24.8% CAGR | 2033 | This provides a good baseline estimate for the AI in drug discovery market. Their scope includes preclinical testing, which closely aligns with our definition. This is one of the more reliable forecasts we can use with minimal adjustments. |
| Mordor Intelligence | 25.94% CAGR | 2030 | This is a solid directional estimate for the AI in drug discovery market growth. We should slightly reduce this if their definition includes broader services beyond our core scope. The timeline to 2030 makes this particularly useful for near-term projections. |
| Fortune Business Insights | 12.2% CAGR | 2030 | This represents a conservative scenario for the AI in drug discovery market. It likely assumes slower enterprise adoption and longer validation cycles. This is useful as a lower bound for our projections. |
| Arizton | 30.58% CAGR | 2030 | This is a very aggressive growth forecast for the AI in drug discovery market. It probably assumes fast platform scaling and strong services growth. We should treat this as an optimistic upper bound rather than a base case. |
| Global Market Insights | 30.1% CAGR | 2034 | This is another very aggressive estimate for the AI in drug discovery market. Their market scope seems broader than our definition. We should significantly moderate this forecast to reflect our more focused definition on discovery and preclinical stages. |
What can we conclude about the growth rate of the AI in drug discovery market?
Based on all available forecasts and our analysis of adoption dynamics, we estimate the AI in drug discovery market will grow at approximately 22% CAGR from 2026 to 2030, then moderate to around 14% CAGR from 2030 to 2036 as the market matures.
This means the AI in drug discovery market should be approximately 2.2 times larger in 2030, reaching around $6.4 billion. By 2036, the market should be roughly 4.9 times larger than 2026, reaching approximately $14.1 billion.
Our growth projections are more conservative than the most aggressive forecasts because drug discovery faces inherent constraints around data quality, biological uncertainty, and long experimental feedback loops that prevent software-like scaling speeds.
However, the AI in drug discovery market will still grow much faster than traditional pharmaceutical services markets because AI demonstrably reduces expensive wet-lab failures and accelerates time to market for valuable therapeutic candidates.
And if you're curious about what's happening in this (really interesting) market, we publish a quarterly update on the activity in the AI in drug discovery market here. We also have a monthly update here.
In our AI in drug discovery market deck, we dentify risks investors and builders need to be aware of
What is the projected CAGR for the AI in drug discovery market?
At New Market Pitch, we like it when the information is clear and easy to digest, as you will see in the pitch about the AI in drug discovery market. That's also why we have made this clear summary table.
| Year | Worst Case (8% annual growth) |
Realistic (14% annual growth) |
Best Case (22% annual growth) |
|---|---|---|---|
| 2027 | $3.1B | $3.3B | $3.5B |
| 2028 | $3.4B | $3.8B | $4.3B |
| 2029 | $3.6B | $4.3B | $5.2B |
| 2030 | $3.9B | $4.9B | $6.4B |
| 2031 | $4.2B | $5.6B | $7.8B |
| 2032 | $4.5B | $6.4B | $9.5B |
| 2033 | $4.9B | $7.3B | $11.6B |
| 2034 | $5.3B | $8.3B | $14.2B |
| 2035 | $5.7B | $9.5B | $17.3B |
| 2036 | $6.1B | $10.8B | $21.1B |
What would it take for the AI in drug discovery market to be worth $21.1 billion?
For the AI in drug discovery market to reach $21.1 billion by 2036, AI would need to become a default layer in discovery teams rather than optional pilot programs.
Pharmaceutical companies would need to routinely use AI predictions to make critical go and no-go decisions, not just to generate interesting molecule candidates for further exploration.
Vendors would need to successfully scale services alongside software by creating integrated platforms, managed discovery programs, and AI-first contract discovery partnerships that generate recurring revenue streams.
Preclinical prediction tools for ADMET and toxicity would need to achieve reliable enough accuracy that pharma companies trust them to eliminate expensive wet-lab experiments rather than merely supplementing them.
Large pharmaceutical companies would need to continue paying premium prices for speed because faster discovery translates directly to blockbuster advantage, especially in highly competitive therapeutic indications.
Platform consolidation would need to accelerate as buyers shift budgets away from maintaining ten separate point solutions toward comprehensive end-to-end systems that handle multiple discovery workflows.
The regulatory environment would need to evolve to accept AI-generated evidence in preclinical submissions, reducing the need for redundant traditional experiments that AI predictions have already addressed.
Data sharing and standardization across the pharmaceutical industry would need to improve significantly so that AI models can train on much larger and higher-quality datasets than are currently available within individual companies.
In our AI in drug discovery market deck, we answer all the common questions from investors and entrepreneurs
Where is the money in the AI in drug discovery market?
What are the categories and how much do they generate?
AI discovery platforms that offer end-to-end solutions account for approximately 30% of revenue in the AI in drug discovery market in 2026. These platforms bundle multiple modules and can expand seat count across discovery teams, making them attractive both to vendors seeking recurring revenue and buyers seeking procurement simplification.
Target identification and biology inference tools capture about 18% of revenue in 2026. This high share reflects the willingness to pay for AI that reduces risk at the earliest and most uncertain phase of drug discovery, especially as omics data and multimodal models become more sophisticated.
Hit and lead generation plus virtual screening tools represent roughly 15% of the AI in drug discovery market in 2026. These tools deliver clear return on investment by reducing the number of compounds that need to be synthesized and tested in expensive wet-lab experiments.
Lead optimization and de novo design capabilities also account for about 15% of revenue in the AI in drug discovery market. These tools are used heavily in small molecule programs and structure-based drug design where computational methods have proven particularly effective.
Preclinical prediction tools for ADMET and toxicity capture approximately 12% of revenue in 2026. Strong demand exists because these predictions can reduce late-stage preclinical failures, which are among the most expensive setbacks in drug development timelines.
Drug repurposing applications generate about 10% of the AI in drug discovery market revenue in 2026. While useful for finding new indications for existing drugs, this category is limited by the relatively small number of approved drugs available for repositioning and the lack of strong clinical anchors for many potential new uses.
Finally, if you really want to understand where is the money, you can check our ranking of the most funded startups in the AI in drug discovery market as well as our list of the most valued startups.
How will it evolve?
By 2030, AI discovery platforms will grow to approximately 36% of market revenue as buyers consolidate point solutions and vendors prove the value of integrated workflows. Preclinical prediction will expand to around 14% as more models demonstrate reliable accuracy, while repurposing will shrink to just 6% as the category matures.
By 2036, platforms will dominate with roughly 42% of the AI in drug discovery market, and preclinical prediction will reach 16% as regulatory acceptance increases. Target identification will moderate to 14%, hit and lead generation to 11%, and repurposing will decline to only 2% as low-hanging fruit opportunities are exhausted.
Where to spend your energy as an investor or a builder in the AI in drug discovery market then?
Investors and builders should focus on comprehensive platforms that replace multiple point solutions because these offerings enable easier procurement, higher customer retention, and more upsell opportunities in the AI in drug discovery market.
Preclinical prediction for ADMET and toxicity represents an extremely attractive opportunity because it addresses one of the most expensive failure points in drug development, offering clear and measurable return on investment that is easy for pharma buyers to justify.
Target discovery tools backed by proprietary data advantages are particularly compelling because data moats matter more than model architectures over the long term in the AI in drug discovery market, and competitors without access to unique datasets will struggle to catch up.
Services plus platform hybrid models can grow revenue faster than pure software plays, but they are significantly harder to scale operationally. Investors should carefully evaluate whether teams have the operational capabilities to manage this complexity in the AI in drug discovery market.
And if you're curious about where investors are putting their money right now, we publish a quarterly update on the fundraising activity in the AI in drug discovery market here. We also analyze long-term funding trends in the AI in drug discovery market here.
In our AI in drug discovery market deck, we track adoption trends and shifts in consumer behavior
What is the geographical revenue breakdown for the AI in drug discovery market?
North America
North America captures approximately 45% of the AI in drug discovery market revenue in 2026, driven by the highest concentration of AI-biotech funding and major pharmaceutical company headquarters. This share will decline to around 40% by 2030 and 36% by 2036 as other regions accelerate adoption and build local AI discovery capabilities.
The decline in North American share reflects not weakness but rather faster growth in Asia and continued strong performance in Europe. North America will remain the largest single regional market for the AI in drug discovery market through 2036, maintaining leadership in cutting-edge platform development and premium-priced discovery services.
Europe
Europe accounts for roughly 25% of the AI in drug discovery market in 2026, supported by a strong pharmaceutical base and growing AI adoption across both large companies and emerging biotech firms. This share will moderate to approximately 23% by 2030 and 21% by 2036 as Asian markets grow faster.
European pharmaceutical companies are investing heavily in AI discovery capabilities but face fragmented regulatory environments across different countries. The AI in drug discovery market in Europe will grow steadily but at a slower pace than Asia due to more conservative adoption timelines and budget constraints at some mid-sized pharma companies.
Asia
Asia represents approximately 25% of the AI in drug discovery market in 2026 and will surge to 32% by 2030 and 38% by 2036. Rapid scaling in China, Japan, South Korea, Singapore, and India drives this growth as these countries invest aggressively in both AI technology infrastructure and pharmaceutical R&D capabilities.
China in particular is building massive AI discovery platforms and data infrastructure that will make it a dominant force in the AI in drug discovery market by the mid-2030s. Japan and South Korea are focusing on integration between established pharma companies and cutting-edge AI startups, while Singapore and India are emerging as attractive hubs for contract discovery services.
Central and South America
Central and South America account for about 3% of the AI in drug discovery market in 2026 and will maintain this share through 2030 and 2036. The smaller pharmaceutical footprint and limited large-scale R&D infrastructure constrain growth, though Brazil and Argentina have growing research hubs.
These regions will benefit from AI discovery services offered remotely by North American and European vendors, but local adoption of sophisticated AI platforms will remain limited through 2036 in the AI in drug discovery market.
Middle East and Africa
The Middle East and Africa represent approximately 2% of the AI in drug discovery market in 2026 and will maintain this modest share through 2030 and 2036. Early-stage adoption and limited large-scale pharmaceutical R&D demand constrain market development in these regions.
Select Middle Eastern countries like Israel and United Arab Emirates are making targeted investments in AI discovery capabilities, but overall market size will remain small in the AI in drug discovery market through 2036.
Oceania
Oceania accounts for less than 1% of the AI in drug discovery market in 2026 and will maintain negligible share through 2030 and 2036. The small absolute research spend in Australia and New Zealand means most adoption happens through global vendor relationships rather than local market development.
Australian biotech firms increasingly use AI discovery tools from North American and European platforms, but this activity is typically counted within global vendor sales rather than as a distinct regional market in the AI in drug discovery market.
In our AI in drug discovery market deck, we have designed useful charts to give you full market clarity
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