Drug Discovery용 GPU : 시장 점유율 분석, 업계 동향 및 통계, 성장 예측(2026-2031년)

According to Mordor Intelligence, the drug discovery GPU market size is projected to be USD 0.85 billion in 2025, USD 1.06 billion in 2026, and reach USD 2.52 billion by 2031, growing at a CAGR of 18.90% from 2026 to 2031.

This report is Segmented by Component (GPU Software and Development Platforms, GPU Cloud and Infrastructure Services, and More), Workload Type (Molecular Dynamics Simulation, Virtual Screening and Docking, and More), Deployment Model (Cloud-Based, Hybrid, and More), End User (Pharmaceutical and Biotechnology Companies, and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Global Drug Discovery GPU Market Trends and Insights

Rising Demand for GPU-Accelerated Molecular Simulation

GPU-accelerated molecular simulation has moved from a helpful research option to a necessary operating layer in the drug discovery GPU market because medicinal chemistry teams need faster iteration across larger experimental spaces. Classical molecular dynamics engines such as Desmond and related Schrodinger workflows run far faster on GPU architecture than on comparable CPU-only environments, which compresses turnaround time for conformational analysis and simulation-led prioritization. NVIDIA extended this advantage in 2026 through the ALCHEMI Toolkit, which was built around GPU-native molecular dynamics workflows in PyTorch and reduced the host-to-device transfer bottleneck that had limited machine-learned interatomic potential simulations. AMD also improved its position by optimizing GROMACS on its compute platforms with AstraZeneca and Orion Pharma, showing that faster design-make-test-analyze loops can be achieved outside a single-vendor ecosystem. As more discovery groups depend on simulation to narrow candidate sets before synthesis, the drug discovery GPU market is increasingly shaped by how many validated molecular workloads an organization can run per week rather than by how much raw hardware it owns. That shift also strengthens the role of software-hardware compatibility because throughput gains matter only when research teams can move simulation output directly into model training, structure analysis, and next-step compound selection.

Shift Toward Generative AI for Hit Discovery

Generative molecule design is changing workload economics in the drug discovery GPU market because these models keep GPUs engaged through repeated inference, sampling, ranking, and refinement cycles rather than through isolated screening jobs. NVIDIA expanded BioNeMo in January 2026 into a broader open development platform that included RNA structure prediction, molecular synthesis, toxicity prediction, and de novo molecule generation, which widened the practical scope of GPU-led pharmaceutical discovery work. Peer-reviewed literature published in 2025 also showed that generative AI is moving beyond narrow ligand design into broader protein design and molecular science applications, which increases the persistence of GPU demand across target classes and program stages. In the drug discovery GPU market, this matters because generative workflows do not replace downstream testing, they create larger and more diverse candidate pools that still require simulation, docking, safety prediction, and optimization. That chain effect increases total compute usage across multiple workload categories even when the first point of adoption appears to be limited to hit generation. It also explains why the fastest growth is emerging in speculative design workloads, where the value of compute is tied not only to speed but also to the ability to search chemical space that traditional screening methods cannot cover efficiently.

High GPU Infrastructure and Energy Costs

High infrastructure cost remains a meaningful restraint on the drug discovery GPU market because the capital needed for dense compute systems, cooling, power delivery, and facility design is far beyond the reach of many mid-sized research organizations. Industry reporting cited in the source draft showed very high power draw for leading accelerators and pointed to the need for advanced cooling environments, which underscores why private deployments remain concentrated among well-funded pharmaceutical and hyperscale operators. NVIDIA's January 2026 announcement on the live Lilly AI factory also showed that major deployments are now being built as purpose-designed environments rather than as standard server-room extensions, which illustrates the scale of commitment needed for frontier drug discovery workloads. Roche reinforced the same pattern in March 2026 by deploying more than 3,500 NVIDIA Blackwell GPUs across hybrid cloud and on-premise environments, which confirms that major therapeutic companies are still using scale and balance sheet strength as competitive tools in compute access. Sustainability adds another constraint because energy-intensive clusters must increasingly align with internal carbon goals and board-level oversight on infrastructure planning. The drug discovery GPU market therefore grows fastest where firms can either absorb these fixed costs directly or shift them into flexible cloud spending without losing scientific throughput.

Other drivers and restraints analyzed in the detailed report include:

1. Expansion of Cloud-Based High Performance Compute Access
2. Rising Multi-Omics and Protein Structure Data Volumes
3. Data Fragmentation Across Biological and Chemical Silos

For complete list of drivers and restraints, kindly check the Table Of Contents.

Segment Analysis

GPU Hardware held 52.22% of the drug discovery GPU market share in 2025, which reflected the heavy first wave of infrastructure buildout by pharmaceutical companies and major compute providers. The live deployment of LillyPod and the January 2026 NVIDIA and Eli Lilly partnership showed how large-cap drugmakers are treating dedicated AI compute as a long-duration research asset rather than as a temporary experiment. Roche extended the same investment pattern in March 2026 by launching an AI factory with more than 3,500 NVIDIA Blackwell GPUs across hybrid cloud and on-premise environments, which reinforced hardware's leading revenue role in this phase of the drug discovery GPU market. Hardware leadership also reflects practical buying behavior because companies need cluster density, memory bandwidth, and validated infrastructure before they can capture value from advanced discovery software. In that sense, hardware remains the operational foundation on which the rest of the drug discovery GPU market is being built.

GPU Cloud and Infrastructure Services are projected to expand at a 19.45% CAGR through 2031, which makes them the fastest-growing component even though owned hardware still leads current revenue. AWS and Microsoft Azure both expanded access to NVIDIA BioNeMo-enabled workflows, which supports a wider customer base that values speed of deployment more than physical ownership of GPUs. GPU software and development platforms are also gaining ground because vendors such as Schrodinger and NVIDIA are tying discovery productivity more closely to subscription, platform, and throughput models rather than to hardware sales alone. Integration and support services remain smaller by revenue, but they are becoming more important as customers manage hybrid cloud, regulated research workflows, and multi-tool deployment across chemistry and biology teams. The drug discovery GPU industry within this component view is therefore moving from hardware-first purchasing toward a more layered stack where access, orchestration, and deployment simplicity drive a larger share of incremental growth.

Molecular Dynamics Simulation accounted for 27.56% of the drug discovery GPU market size in 2025, which kept it as the largest workload because physics-based validation remains central to structure-aware discovery programs. Schrodinger's GPU-powered simulation environment still represents one of the most established commercial examples of this category, and its continuing relevance shows that simulation is not being displaced even as AI-native methods spread. Simulation remains the grounding layer for candidate refinement because it helps teams understand conformational behavior, binding stability, and downstream feasibility before synthesis and wet-lab testing scale further. The drug discovery GPU market still depends on this workload for core scientific confidence, especially in settings where generated compounds must be filtered through robust physical validation. That is why the largest share remains with a mature and trusted workload rather than with the newest class of models.

Generative Molecule Design is projected to record the fastest 19.67% CAGR through 2031, which reflects the much higher persistence of compute demand once generative systems become embedded in hit identification and lead exploration. NVIDIA's broader BioNeMo platform push in 2026 and the emerging literature on generative AI for molecular science both support the view that these models are widening from niche experimentation into repeatable discovery workflows. Protein Structure Prediction, Virtual Screening and Docking, and Multi-Omics Analytics continue to build a broader demand mix, especially as folding, ranking, and biological context generation become more tightly linked in single research programs. The practical outcome for the drug discovery GPU market is that one discovery program can now trigger repeated GPU use across design, screening, validation, and systems-level interpretation instead of within one isolated analytic stage. The drug discovery GPU industry is therefore seeing growth not only because more tasks use GPUs, but because each successful task increasingly creates follow-on compute demand across adjacent workloads.

Complete Report Scope:

• By Component
  • GPU Hardware
  • GPU Software and Development Platforms
  • GPU Cloud and Infrastructure Services
  • GPU Integration and Support Services
• By Workload Type
  • Molecular Dynamics Simulation
  • Virtual Screening and Docking
  • Protein Structure Prediction
  • Generative Molecule Design
  • Multi-Omics Analytics
  • Other Workload Types (ADMET, Toxicity Prediction and Lead Optimization, Multi-Omics and Biomarker Analytics)
• By Deployment Model
  • Cloud-Based
  • On-Premise
  • Hybrid
• By End User
  • Pharmaceutical and Biotechnology Companies
  • Contract Research Organizations
  • Academic and Research Institutes
  • Government and Nonprofit Research Institutes
• By Geography
  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • Germany
    • United Kingdom
    • France
    • Italy
    • Rest of Europe
  • Asia-Pacific
    • China
    • Japan
    • South Korea
    • India
    • Southeast Asia
    • Rest of Asia-Pacific
  • South America
  • Middle East and Africa

Geography Analysis

North America held 42.46% of the drug discovery GPU market size in 2025, which made it the largest regional contributor because it combines leading pharmaceutical balance sheets, cloud infrastructure depth, and dense biotech clusters. NVIDIA and Eli Lilly's January 2026 partnership, together with LillyPod's live deployment, showed the scale of compute investment that North American firms are now willing to treat as core discovery infrastructure. The United States remains the center of the drug discovery GPU market because major research corridors in Boston-Cambridge, the San Francisco Bay Area, and San Diego pull hardware, software, and model innovation into the same commercial network. Canada also adds momentum through academic and research-intensive ecosystems that support life sciences computing, even though its commercial footprint remains smaller than that of the United States. The regional advantage is therefore not only about spending power, it is also about the closeness between drug developers, platform vendors, and high-end compute availability.

Asia-Pacific is projected to expand at a 19.33% CAGR through 2031, which makes it the fastest-growing regional segment in the drug discovery GPU market. NTT's February 2025 launch at Shonan iPark showed that Japanese stakeholders are building secure remote compute models tailored to pharmaceutical research needs rather than simply copying Western cloud deployment patterns. China remains central to the region's scale because AI-driven discovery activity, service platform growth, and strong biotech clustering continue to support higher GPU intensity across research workflows. South Korea is also emerging as a focused policy market for AI-enabled drug discovery, while Japan continues to strengthen practical infrastructure suited to collaborative pharmaceutical environments. The regional growth story in the drug discovery GPU market comes from capacity expansion, rising domestic R&D sophistication, and a willingness to build local compute pathways that match national data and research preferences.

Europe remains the second-largest regional position in the drug discovery GPU market, supported by large pharmaceutical groups, strong translational science, and a continuing need for secure compute in regulated development settings. Roche's March 2026 AI factory deployment across the United States and Europe demonstrated that European-headquartered companies are investing at the same strategic level as their North American peers. Germany, the United Kingdom, and France provide the broadest commercial base, while Italy and France add incremental opportunity through established manufacturing strength and expanding biotech activity. South America and Middle East and Africa remain earlier-stage regions within the drug discovery GPU market because infrastructure readiness, energy constraints, and supply limitations still weigh on adoption. Even so, their long-term relevance should improve as pharmaceutical manufacturing investment and digital research capacity expand after the current infrastructure bottlenecks ease.

1. NVIDIA Corporation
2. Microsoft Corporation
3. Amazon Web Services, Inc.
4. Alphabet Inc.
5. International Business Machines Corporation
6. Oracle Corporation
7. Advanced Micro Devices, Inc.
8. Intel Corporation
9. Dell Technologies Inc.
10. Hewlett Packard Enterprise Company
11. Super Micro Computer, Inc.
12. Schrodinger, Inc.
13. Recursion Pharmaceuticals, Inc.
14. Insilico Medicine, Inc.
15. Exscientia plc
16. BioAge Labs, Inc.
17. Healx Limited
18. Atomwise, Inc.
19. BenevolentAI Limited
20. Cyclica Inc.

Additional Benefits:

• The market estimate (ME) sheet in Excel format
• 3 months of analyst support

TABLE OF CONTENTS

1 INTRODUCTION

• 1.1 Study Assumptions and Market Definition
• 1.2 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET LANDSCAPE

• 4.1 Market Overview
• 4.2 Market Drivers
  • 4.2.1 Rising Demand for GPU-Accelerated Molecular Simulation
  • 4.2.2 Shift Toward Generative AI for Hit Discovery
  • 4.2.3 Expansion of Cloud-Based High Performance Compute Access
  • 4.2.4 Rising Multi-Omics and Protein Structure Data Volumes
  • 4.2.5 Pharma Digital Twin and Virtual Screening Adoption
  • 4.2.6 Public And Private Investment in AI-Native Drug Pipelines
• 4.3 Market Restraints
  • 4.3.1 High GPU Infrastructure and Energy Costs
  • 4.3.2 Data Fragmentation Across Biological And Chemical Silos
  • 4.3.3 Explainability and Validation Burden for AI-Designed Molecules
  • 4.3.4 Shortage of Cross-Functional AI, Chemistry, And Biology Talent
• 4.4 Impact of Macroeconomic Factors on the Market
• 4.5 Market Positioning Analysis
• 4.6 Regulatory Landscape
• 4.7 Technological Outlook
• 4.8 Porter's Five Forces Analysis
  • 4.8.1 Bargaining Power of Suppliers
  • 4.8.2 Bargaining Power of Buyers
  • 4.8.3 Threat of New Entrants
  • 4.8.4 Threat of Substitutes
  • 4.8.5 Intensity of Competitive Rivalry

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

• 5.1 By Component
  • 5.1.1 GPU Hardware
  • 5.1.2 GPU Software and Development Platforms
  • 5.1.3 GPU Cloud and Infrastructure Services
  • 5.1.4 GPU Integration and Support Services
• 5.2 By Workload Type
  • 5.2.1 Molecular Dynamics Simulation
  • 5.2.2 Virtual Screening and Docking
  • 5.2.3 Protein Structure Prediction
  • 5.2.4 Generative Molecule Design
  • 5.2.5 Multi-Omics Analytics
  • 5.2.6 Other Workload Types (ADMET, Toxicity Prediction and Lead Optimization, Multi-Omics and Biomarker Analytics)
• 5.3 By Deployment Model
  • 5.3.1 Cloud-Based
  • 5.3.2 On-Premise
  • 5.3.3 Hybrid
• 5.4 By End User
  • 5.4.1 Pharmaceutical and Biotechnology Companies
  • 5.4.2 Contract Research Organizations
  • 5.4.3 Academic and Research Institutes
  • 5.4.4 Government and Nonprofit Research Institutes
• 5.5 By Geography
  • 5.5.1 North America
    • 5.5.1.1 United States
    • 5.5.1.2 Canada
    • 5.5.1.3 Mexico
  • 5.5.2 Europe
    • 5.5.2.1 Germany
    • 5.5.2.2 United Kingdom
    • 5.5.2.3 France
    • 5.5.2.4 Italy
    • 5.5.2.5 Rest of Europe
  • 5.5.3 Asia-Pacific
    • 5.5.3.1 China
    • 5.5.3.2 Japan
    • 5.5.3.3 South Korea
    • 5.5.3.4 India
    • 5.5.3.5 Southeast Asia
    • 5.5.3.6 Rest of Asia-Pacific
  • 5.5.4 South America
  • 5.5.5 Middle East and Africa

6 COMPETITIVE LANDSCAPE

• 6.1 Market Concentration
• 6.2 Strategic Moves
• 6.3 Market Positioning Analysis
• 6.4 Company Profiles (includes Global Level Overview, Market Level Overview, Core Segments, Financials as available, Strategic Information, Market Rank/Share, Products and Services, Recent Developments)
  • 6.4.1 NVIDIA Corporation
  • 6.4.2 Microsoft Corporation
  • 6.4.3 Amazon Web Services, Inc.
  • 6.4.4 Alphabet Inc.
  • 6.4.5 International Business Machines Corporation
  • 6.4.6 Oracle Corporation
  • 6.4.7 Advanced Micro Devices, Inc.
  • 6.4.8 Intel Corporation
  • 6.4.9 Dell Technologies Inc.
  • 6.4.10 Hewlett Packard Enterprise Company
  • 6.4.11 Super Micro Computer, Inc.
  • 6.4.12 Schrodinger, Inc.
  • 6.4.13 Recursion Pharmaceuticals, Inc.
  • 6.4.14 Insilico Medicine, Inc.
  • 6.4.15 Exscientia plc
  • 6.4.16 BioAge Labs, Inc.
  • 6.4.17 Healx Limited
  • 6.4.18 Atomwise, Inc.
  • 6.4.19 BenevolentAI Limited
  • 6.4.20 Cyclica Inc.

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

• 7.1 White-Space And Unmet-Need Assessment