Artificial Intelligence (AI) in Drug Discovery Market Industry Growth and Forecast 2030
Artificial Intelligence (AI) in Drug Discovery Market Industry Growth and Forecast 2030
The AI in drug discovery market centers on the use of artificial intelligence technologies—such as machine learning, deep learning, and generative AI—to accelerate and enhance early-stage drug development. These tools help researchers analyze vast biomedical datasets, identify promising drug targets, optimize candidate compounds, and improve clinical trial predictions. By reducing traditional processes that often span over a decade and cost billions, AI enables faster, more cost-effective discoveries—especially in small molecule research where data are abundant. North America currently leads the market thanks to strong pharma infrastructure and investment, while Asia-Pacific is emerging rapidly due to digital health adoption and supportive policies. Key tech players and startups in the field include names like IBM Watson Health, Google DeepMind (through Isomorphic Labs), Nvidia, Exscientia, Insilico Medicine, and XtalPi. However, widespread adoption faces hurdles such as data privacy, regulatory complexity, interdisciplinary collaboration, and talent shortages.
According to Fortune Business Insights, the global artificial intelligence (AI) in drug discovery market size was valued at USD 3.00 billion in 2022 and is projected to grow from USD 3.54 billion in 2023 to USD 7.94 billion by 2030, exhibiting a compound annual growth rate (CAGR) of 12.2% during the forecast period (2023–2030). In 2022, North America dominated the market with a 69.33% share.
Microsoft (U.S.)
Schrödinger, Inc. (U.S.)
Cresset (U.K.)
IBM (U.S.)
Atomwise Inc. (U.S.)
Insilico Medicine (U.S.)
Exscientia (U.K.)
BenevolentAI (U.K.)
Aria Pharmaceuticals, Inc. (U.S.)
Integral BioSciences (U.S.)
Alphabet Inc. (U.S.)
In a notable industry development, in November 2022, Cyclica received a USD 1.8 million grant from the Bill & Melinda Gates Foundation. The funding supports its artificial intelligence-enabled drug discovery platform to target novel non-hormonal contraceptives, leveraging low-data biological targets to push forward innovation in difficult therapeutic areas.
The AI in drug discovery market is propelled by the urgent need to accelerate drug development pipelines, reduce costs, and improve success rates. AI adoption enables enhanced target identification, predictive modeling, and streamlined optimization, which are especially critical for the pharmaceutical and biotechnology sectors.
However, the market's expansion may be dampened by challenges including high initial investment costs, integration complexities, and regulatory hurdles that vary across jurisdictions. While the Fortune Business Insights report does not explicitly enumerate these restraints, such concerns are inherent in the high-tech, regulated healthcare ecosystem.
The report from Fortune Business Insights provides a comprehensive outlook, detailing projections of global market size and growth trajectory from 2023 through 2030. It serves as a strategic resource for stakeholders seeking data-driven insights into AI applications transforming drug discovery.
Core coverage includes segmentation by:
Drug Type: Small molecules vs. large molecules
Offering: Software vs. services
Technology: Machine learning, natural language processing, others
Application: Therapeutic areas such as oncology, neurology, infectious diseases, and more
End-user: Pharmaceutical & biotech firms, academic & research institutions, among others
Regional Forecasts: Detailed geographic market analysis across regions
The competitive landscape is vibrant and highly diversified, featuring established tech giants like Microsoft, IBM, and Alphabet alongside specialized biotech innovators such as Schrödinger, Atomwise, Insilico Medicine, Exscientia, and BenevolentAI. Micro-players and emerging ventures enhance agility and niche expertise, while entrenched incumbents offer expansive infrastructure and capital.
This ecosystem fosters dynamic competition, alliances, and co-development models, ensuring rapid advancements in AI tools, drug discovery pipelines, and cross-sector synergies.
Fortune Business Insights delineates multiple segmentation axes:
By Drug Type: Small molecules are generally favored due to ease of synthesis and delivery, though large molecules are increasingly supported.
By Offering: Software platforms—including AI predictive systems—are complemented by services such as consulting, integration, and analytics support.
By Technology: AI approaches include machine learning (for pattern recognition and modeling), natural language processing (for literature mining and data analysis), and other emerging techniques.
By Application: Therapeutic focus ranges from oncology and neurology to infectious diseases and other high-need areas.
By End-User: Pharmaceutical companies and biotech firms lead adoption, supported by academic and research institutions exploring AI deployment.
By Region: The report maps regional performance and growth expectations globally.
Explore the full research report with detailed insights and TOC:https://www.fortunebusinessinsights.com/artificial-intelligence-in-drug-discovery-market-105354
In 2022, North America dominated the market, capturing approximately 69.33 % of global share. Notably, North America’s AI in drug discovery segment was valued at around USD 2.08 billion in 2022, underscoring the region’s leadership in research infrastructure, investment, and technological adoption.
Other regions—including Europe, Asia Pacific, Latin America, and the Middle East & Africa—feature in the report’s forecast, with each expected to contribute to the market’s overall acceleration over the forecast horizon, though specific regional projections beyond North America are not quoted verbatim here.
With projected growth to USD 7.94 billion by 2030 and a solid 12.2 % CAGR, the AI in drug discovery market is entering a phase of mainstream acceleration. Future opportunities lie in:
Advanced machine learning models tailored to personalized medicine.
Combinatorial AI tools integrating multi-omics, real-world evidence, and clinical data.
Collaborative public-private models accelerating translational research and licensing.
Cross-industry integration spanning diagnostics, digital health, and AI-augmented clinical trials.
As AI continues to embed itself in drug discovery pipelines, stakeholders—from big pharma and startups to academia and regulators—must align strategies to harness innovation while navigating ethical, regulatory, and operational complexities.
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