Artificial Intelligence (AI) in Drug Discovery Market Key Players & Collaborations | 2030 Forecast

The AI in drug discovery market refers to the use of artificial intelligence—especially machine learning, deep learning, and generative modeling—to speed up and enhance early-stage drug development. These technologies facilitate tasks like identifying new targets, designing molecules, optimizing drug candidates, and predicting clinical trial outcomes. AI-driven approaches significantly reduce the time and cost compared to traditional methods, helping address the extensive timelines and high failure rates typical in pharma R&D. The market is growing swiftly thanks to rising R&D investments, access to vast biomedical datasets, and strategic partnerships between pharma companies and AI startups. North America leads the market, with Asia-Pacific emerging rapidly due to expanding healthcare infrastructure and increasing digitization of research.

According to Fortune Business Insights, the global artificial intelligence (AI) in drug discovery market 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.

🧪 Top Companies in the Market

• 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.)

Key Industry Development

In November 2022, Cyclica secured a USD 1.8 million grant from the Bill & Melinda Gates Foundation. The funding is aimed at advancing Cyclica’s AI-powered drug discovery platform to identify novel non-hormonal contraceptives by leveraging multiple low-data biological targets. This milestone highlights the transformative role of AI in tackling critical global health challenges.

Market Drivers

The growing prevalence of chronic diseases—including cancer, cardiovascular disorders, and metabolic conditions—is driving demand for AI-enabled drug discovery solutions. Pharmaceutical companies are under increasing pressure to cut development costs and shorten time-to-market. AI addresses these needs by enabling rapid identification of lead compounds, accelerating target validation, and optimizing molecular design. As a result, it significantly enhances efficiency, accuracy, and success rates in drug development pipelines.

Market Restraints

Despite strong growth potential, several challenges hinder market expansion. Data quality and standardization remain major barriers, as biomedical datasets are often fragmented, small in scale, and heterogeneous, limiting AI models’ reliability. Additionally, the complexity of handling multi-format pharmaceutical data—ranging from imaging and multi-omics to clinical records—creates hurdles for seamless AI integration and deployment.

Market Report Coverage

According to Fortune Business Insights, the report provides comprehensive coverage of the AI in drug discovery market. It examines segmentation by drug type (small molecule vs. large molecule), technology (machine learning, deep learning, NLP, and others), applications (target discovery, screening, validation, optimization), and end-users (pharmaceutical companies, biotech firms, and CROs/CDMOs). The report also highlights key innovations, product launches, and strategic collaborations shaping the industry’s growth trajectory.

Competitive Landscape

The market is characterized by strong collaboration between leading pharmaceutical firms and AI technology innovators. Prominent players include Alphabet Inc. (Isomorphic Labs, a DeepMind spin-off), IBM Watson Health, Insilico Medicine, Exscientia, BenevolentAI, Atomwise, and Schrödinger. These companies are heavily investing in platform development and forging partnerships to accelerate discovery timelines, reduce attrition rates, and enhance therapeutic productivity.

Market Segments

• By Drug Type:

  • Small molecule segment dominated in 2022, benefiting from abundant clinical datasets and more predictable structures; small molecules make up over 90% of the pharma market and comprised 182 of 293 FDA‑approved entities in 2017–2022.

  • Large molecule segment held a lower share due to R&D complexity and limited data availability, but is projected to grow as investments increase in biologics discovery.

• By Offering: Software and services offerings support distinct stages of the drug discovery pipeline; software (platforms) is expected to lead, while services grow more slowly due to a talent shortage in AI deployment.

• By Technology: Machine learning and deep learning are key drivers; NLP and computer vision also enable drug target interpretation, multi‑omics integration, and predictive modelling.

• By End‑User: Includes pharmaceutical companies, biotechnology firms, contract research organizations (CROs) and contract development and manufacturing organizations (CDMOs), among others

Market Regional Insights

North America led the market in 2022 with a dominant share of 69.33%, driven by a strong presence of major pharma players, high R&D investment, and robust AI adoption. 

Europe held the second‑largest share in 2022, supported by growing adoption of affordable AI‑based drug discovery interventions. The Asia-Pacific region is anticipated to register the fastest CAGR during the forecast period, thanks to rising chronic disease prevalence and the expanding presence of drug manufacturers in markets like China, India, and South Korea. Latin America and Middle East & Africa are projected to grow more slowly due to lower healthcare expenditures and limited local pharmaceutical R&D ecosystems.

Explore the full research report with detailed insights and TOC:https://www.fortunebusinessinsights.com/artificial-intelligence-in-drug-discovery-market-105354 

Future Market Scope

The AI in drug discovery market is positioned for robust expansion beyond 2030, with pharmaceutical firms and AI technology providers further deepening collaboration. Ongoing advances in machine learning, deep learning, NLP, and generative AI are expected to enhance efficiency in target identification, molecule design and pre‑clinical validation pipelines.

Cloud‑based platforms and integrated multi‑omics data tools, backed by increased investment and strategic alliances, offer fertile ground for next‑generation therapeutic development. The successful deployment of AI tools—particularly in novel biologics, personalized medicine, and complex disease targets—is expected to expand the market’s potential well into the next decade.

Conclusion

With projected growth from USD 3.54 billion in 2023 to nearly USD 7.94 billion by 2030 at a CAGR of 12.2%, and North America commanding nearly 70% of the global market as of 2022, the AI‑powered drug discovery sector is reshaping pharmaceutical R&D. Despite challenges around data standardization and talent gaps, the synergy between cutting‑edge AI technologies and life sciences promises accelerated innovation, reduced costs, and faster delivery of novel therapies.

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