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市場調查報告書

商品編碼

1889439

人工智慧藥物發現市場預測至2032年：按藥物類型、治療領域、技術、應用、最終用戶和地區分類的全球分析

AI Drug Discovery Market Forecasts to 2032 - Global Analysis By Drug Type, Therapeutic Area, Technology, Application, End User, and By Geography

出版日期: 2025年12月12日 | 出版商:

Stratistics Market Research Consulting | 英文 | 商品交期: 2-3個工作天內

價格

簡介目錄圖表

根據 Stratistics MRC 的一項研究，預計到 2025 年，全球人工智慧藥物發現市場價值將達到 48 億美元，到 2032 年將達到 96 億美元，預測期內複合年成長率為 10.4%。

人工智慧藥物發現利用先進的演算法分析生物數據、預測分子交互作用，並加速潛在治療候選藥物的辨識。機器學習平台簡化了標靶選擇、先導化合物最佳化和毒性預測流程，顯著縮短了研發時間和成本。這些系統能夠快速篩檢龐大的化合物庫，並在實驗室檢驗前模擬化合物的生化行為。因此，製藥公司能夠更快實現創新，提高研發效率，並增加攻克複雜罕見疾病的成功率。

根據 Clinical Trials Arena 2025 年的分析，人工智慧與製藥公司之間的策略合作夥伴關係將從 2015 年的 4 個激增至 2024 年的 27 個，凸顯了旨在加速藥物開發和降低臨床前失敗率的合作創新。

快速藥物開發平臺的需求日益成長

隨著製藥公司尋求縮短藥物研發週期並降低研發風險，對更快藥物研發管線的需求日益成長，人工智慧的應用也隨之加速。為了高效識別先導化合物，人工智慧演算法正在輔助進行高通量篩檢、分子對接和預測建模。快速將療法商業化的壓力不斷增加，尤其是在複雜疾病領域，這進一步加劇了對自動化的依賴。隨著競爭加劇，開發人員越來越將人工智慧驅動的藥物發現引擎視為提高早期藥物開發流程效率和成功率的關鍵工具。

平台實施成本高昂

高昂的平台部署成本仍然是一大障礙，尤其對於資金有限的中小型生技公司更是如此。先進的人工智慧藥物發現引擎需要對雲端運算、生物資料集、模型訓練和專業人才進行大量投資。與現有實驗室系統的整合進一步增加了支出，並使擴充性更加複雜。此外，持續的演算法改進和資料收集需求也增加了長期營運成本。這些財務限制減緩了科技的普及，並在大型製藥企業和新興研究機構之間造成了鴻溝。

計算生物學整合進展

計算生物學的整合發展正透過加深對疾病機制的理解，創造巨大的成長機會。體學數據、分子模擬和人工智慧驅動的通路分析的融合，加速了標靶辨識和作用機制研究。隨著多模態資料集的日益豐富，人工智慧平台能夠更精準地預測治療反應。這種協同效應將顯著提升精準藥物研發水平，並拓展其在罕見疾病、免疫學和個人化醫療等眾多領域的應用。這些進展已確立了人工智慧在推動下一代藥物研發管線變革中的作用。

影響獨家調查的資料外洩事件

影響專有研究的資料外洩構成重大威脅，尤其是在大量分子資料儲存於雲端環境的情況下。未授權存取和模型篡改可能導致競爭策略洩露、監管申報延誤或機密化合物庫暴露。生物技術領域網路攻擊的增加加劇了脆弱性，並削弱了人們對數位化研究工作流程的信任。缺乏健全安全態勢的公司將面臨聲譽受損和經濟損失的風險，這凸顯了在人工智慧驅動的藥物發現生態系統中建立嚴格的網路安全通訊協定的必要性。

新冠疫情的影響：

新冠疫情加速了人工智慧藥物研發技術的應用，製藥公司急需快速找到抗病毒藥物和免疫調節劑。人工智慧工具輔助虛擬篩檢和藥物重定位，顯著縮短了初步研究的時間。疫情凸顯了傳統研發方法的低效，促使企業對機器學習平台進行長期投資。此外，全球合作提高了資料集的可用性，從而提升了模型的準確性。即使在疫情結束後，對快速治療反應和應對疫情的持續重視，也保持了人工智慧驅動的藥物研發框架的市場成長勢頭。

預計在預測期內，小分子藥物研發將佔據最大的市場規模。

由於小分子藥物適應症廣泛且研發路徑成熟，預計在預測期內，小分子藥物研發領域將佔據最大的市場佔有率。人工智慧平台在最佳化分子結構、預測ADMET特性以及加速先導藥物最適化週期方面表現出色。製藥公司持續優先研發小分子藥物，因為它們具有可擴展性強、生產製程複雜度低以及商業性化成功率高等優點。與其他藥物類別相比，這些因素促使人工智慧技術在小分子藥物研發管線中得到主導的應用。

預計在預測期內，腫瘤治療領域將達到最高的複合年成長率。

在預測期內，腫瘤學領域預計將保持最高的成長率，這主要得益於精準醫療需求的不斷成長以及複雜標靶識別技術的進步。癌症的異質性生物學特徵需要廣泛的數據建模，這使得人工智慧在生物標記發現、通路映射和個人化治療方案設計方面具有特別重要的價值。對免疫腫瘤學和標靶抑制劑領域投資的增加將進一步推動對人工智慧驅動洞察的依賴。隨著全球癌症發生率的上升，開發商正在加速採用先進的分析技術，從而支撐該領域的強勁成長動能。

佔比最大的地區：

預計亞太地區將在預測期內佔據最大的市場佔有率，這主要得益於中國、印度、韓國和日本等國醫藥研發基地的擴張。政府對生物技術創新的大力支持、臨床試驗活動的增加以及人工智慧研究能力的提升，都推動了市場需求。該地區的成本優勢正促使全球企業將藥物研發活動外包。此外，快速發展的醫療保健生態系統以及對電腦輔助藥物研發領域不斷成長的投資，也進一步鞏固了亞太地區的主導地位。

預計年複合成長率最高的地區：

在預測期內，北美預計將實現最高的複合年成長率，這主要得益於其強大的人工智慧基礎設施、強勁的藥物創新能力以及對先進發現工具的早期應用。領先的生物技術公司、人工智慧Start-Ups和研究機構正在加速將機器學習融入藥物開發平臺。有利於數位化研發工具的監管環境將進一步促進其廣泛應用。精心整理的資料集、創業投資資金以及豐富的多學科人才儲備，正鞏固北美作為人工智慧驅動藥物發現領域成長最快市場的地位。

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公司概況
- 對其他市場參與者（最多 3 家公司）進行全面分析
- 主要參與者（最多3家公司）的SWOT分析
區域細分
- 根據客戶要求，提供主要國家的市場估算、預測和複合年成長率（註：可行性需確認）。
競爭基準化分析
- 根據主要參與者的產品系列、地理覆蓋範圍和策略聯盟基準化分析

北美洲
- 美國
- 加拿大
- 墨西哥
歐洲
- 德國
- 英國
- 義大利
- 法國
- 西班牙
- 其他歐洲
亞太地區
- 日本
- 中國
- 印度
- 澳洲
- 紐西蘭
- 韓國
- 亞太其他地區
南美洲
- 阿根廷
- 巴西
- 智利
- 其他南美國家
中東和非洲
- 沙烏地阿拉伯
- 阿拉伯聯合大公國
- 卡達
- 南非
- 其他中東和非洲地區

第11章重大進展

協議、夥伴關係、合作和合資企業
併購
新產品發布
業務拓展
其他關鍵策略

第12章企業概況

Pfizer
Roche
AstraZeneca
Moderna
Sanofi
Novartis
Johnson &Johnson
GSK
Eli Lilly
Bayer
Boehringer Ingelheim
Merck &Co.
AbbVie
Schrodinger
Exscientia
Atomwise
Insilico Medicine

簡介目錄圖表

Product Code: SMRC32795

According to Stratistics MRC, the Global AI Drug Discovery Market is accounted for $4.8 billion in 2025 and is expected to reach $9.6 billion by 2032 growing at a CAGR of 10.4% during the forecast period. AI Drug Discovery involves deploying advanced algorithms to analyze biological data, predict molecular interactions, and accelerate identification of potential therapeutic candidates. Machine-learning platforms streamline target selection, lead optimization, and toxicity prediction, significantly reducing development time and costs. These systems enable rapid screening of vast compound libraries and simulate biochemical behavior before laboratory validation. As a result, pharmaceutical companies gain faster pathways to innovation, improved R&D productivity, and a higher probability of success in addressing complex and rare diseases.

According to Clinical Trials Arena's 2025 analysis, strategic partnerships between AI firms and pharmaceutical companies surged to 27 in 2024 from 4 in 2015, highlighting collaborative innovation in accelerating drug development and reducing preclinical failure rates.

Market Dynamics:

Driver:

Rising demand for faster drug pipelines

Rising demand for faster drug pipelines is accelerating AI adoption as pharma companies strive to shorten discovery timelines and reduce R&D risks. Propelled by the need to identify lead compounds more efficiently, AI algorithms support high-throughput screening, molecular docking, and predictive modeling. Increasing pressure to commercialize therapeutics rapidly especially for complex diseases further boosts reliance on automation. As competitive intensity heightens, developers increasingly view AI-driven discovery engines as essential tools to enhance productivity and improve success rates across early-stage drug workflows.

Restraint:

High deployment costs for platforms

High deployment costs for platforms remain a significant barrier, especially for small and mid-sized biotech firms with limited capital. Advanced AI discovery engines require substantial investments in cloud computing, biological datasets, model training, and skilled personnel. Integration with legacy laboratory systems further increases expenditures, complicating scalability. Additionally, the need for ongoing algorithm refinement and data acquisition adds long-term operational costs. These financial constraints slow adoption and create disparities between large pharmaceutical companies and emerging research organizations.

Opportunity:

Advances in computational biology integration

Advances in computational biology integration create substantial growth opportunities by enabling deeper understanding of disease mechanisms. The fusion of omics data, molecular simulations, and AI-driven pathway analysis accelerates target identification and mechanism-of-action studies. As multi-modal datasets become more accessible, AI platforms gain the ability to predict therapeutic responses with higher accuracy. This synergy significantly enhances precision-drug development and broadens applicability across rare diseases, immunology, and personalized medicine. These advancements position AI as a transformative enabler of next-generation drug pipelines.

Threat:

Data breaches affecting proprietary research

Data breaches affecting proprietary research pose a major threat, particularly as vast volumes of molecular data reside in cloud environments. Unauthorized access or model manipulation could compromise competitive strategies, delay regulatory submissions, or reveal confidential compound libraries. Increasing cyberattacks in the biotech sector amplify vulnerabilities, undermining trust in digitalized research workflows. Companies lacking robust security frameworks risk reputational damage and financial losses, emphasizing the necessity for stringent cybersecurity protocols across AI-driven discovery ecosystems.

Covid-19 Impact:

COVID-19 accelerated AI drug discovery adoption as pharma companies sought rapid solutions for antiviral and immunomodulatory candidates. AI tools supported virtual screening and repurposing efforts, significantly compressing early research timelines. The pandemic highlighted inefficiencies in traditional R&D approaches, prompting long-term investments in machine learning platforms. Additionally, global collaboration increased dataset availability, improving model accuracy. Post-pandemic, continued emphasis on rapid therapeutic response and preparedness sustains market momentum for AI-enabled discovery frameworks.

The small molecule drug discovery segment is expected to be the largest during the forecast period

The small molecule drug discovery segment is expected to account for the largest market share during the forecast period, resulting from its broad therapeutic applicability and well-established development pathways. AI platforms excel at optimizing molecular structures, predicting ADMET profiles, and accelerating lead optimization cycles. Pharmaceutical companies continue prioritizing small molecules due to their scalability, lower manufacturing complexity, and strong commercial success rates. These factors reinforce dominant adoption of AI technologies across small molecule pipelines compared to other drug classes.

The oncology segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the oncology segment is predicted to witness the highest growth rate, propelled by rising demand for precision therapies and complex target identification. Cancer's heterogeneous biology requires extensive data modeling, making AI particularly valuable for biomarker discovery, pathway mapping, and personalized treatment design. Increasing investment in immuno-oncology and targeted inhibitors further boosts reliance on AI-driven insights. As cancer incidence climbs globally, developers accelerate adoption of advanced analytics, supporting this segment's exceptional growth trajectory.

Region with largest share:

During the forecast period, the Asia Pacific region is expected to hold the largest market share, attributed to expanding pharmaceutical R&D hubs across China, India, South Korea, and Japan. Strong government support for biotech innovation, increasing clinical trial activity, and growing AI research capabilities fuel demand. Regional cost advantages attract global companies to outsource discovery tasks. Additionally, rapidly developing health ecosystems and increasing investment in computational drug discovery strengthen Asia Pacific's leadership position.

Region with highest CAGR:

Over the forecast period, the North America region is anticipated to exhibit the highest CAGR associated with strong AI infrastructure, robust pharmaceutical innovation, and early adoption of advanced discovery tools. Leading biotech companies, AI start-ups, and research institutes accelerate integration of machine learning into drug pipelines. Favorable regulatory pathways for digital R&D tools further enhance uptake. High availability of curated datasets, venture funding, and interdisciplinary talent solidify North America as the fastest-expanding market for AI-driven drug discovery.

Key players in the market

Some of the key players in AI Drug Discovery Market include Pfizer, Roche, AstraZeneca, Moderna, Sanofi, Novartis, Johnson & Johnson, GSK, Eli Lilly, Bayer, Boehringer Ingelheim, Merck & Co., AbbVie, Schrodinger, Exscientia, Atomwise and Insilico Medicine.

Key Developments:

In November 2025, AstraZeneca launched an AI collaboration with BenevolentAI, applying predictive algorithms to respiratory and cardiovascular drug pipelines, aiming to shorten discovery timelines and improve patient-specific treatment outcomes.

In October 2025, Pfizer advanced its AI-driven oncology pipeline, integrating machine learning for target identification and biomarker discovery, accelerating clinical trial readiness and enhancing precision medicine strategies across multiple cancer indications.

In September 2025, Roche expanded its AI-enabled drug discovery platform, focusing on immunology and rare diseases, leveraging deep learning to optimize molecular design and reduce early-stage attrition rates in therapeutic development.

Drug Types Covered:

Small Molecule Drug Discovery
Biologics Discovery
Peptide & Protein-Based Drugs
Regenerative & Cell Therapies
Gene Therapy Candidates
Novel Therapeutic Modalities

Therapeutic Areas Covered:

Oncology
Neurology
Immunology
Infectious Diseases
Cardiology
Rare & Orphan Diseases

Technologies Covered:

Machine Learning Platforms
Deep Learning & Neural Networks
Generative AI for Molecule Design
Quantum AI Drug Modeling
Structure-Based Drug Design Tools
Omics Data Analysis Systems

Applications Covered:

Target Identification
Lead Generation & Optimization
Compound Screening
Preclinical Testing Automation
Biomarker Identification
Toxicity Prediction & Validation

End Users Covered:

Pharmaceutical Companies & Biotechnology Companies
Academic & Research Institutes
Contract Research Organizations (CROs)
Hospitals & Clinical Labs
Other End Users

Regions Covered:

North America
- US
- Canada
- Mexico
Europe
- Germany
- UK
- Italy
- France
- Spain
- Rest of Europe
Asia Pacific
- Japan
- China
- India
- Australia
- New Zealand
- South Korea
- Rest of Asia Pacific
South America
- Argentina
- Brazil
- Chile
- Rest of South America
Middle East & Africa
- Saudi Arabia
- UAE
- Qatar
- South Africa
- Rest of Middle East & Africa

What our report offers:

Market share assessments for the regional and country-level segments
Strategic recommendations for the new entrants
Covers Market data for the years 2024, 2025, 2026, 2028, and 2032
Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
Strategic recommendations in key business segments based on the market estimations
Competitive landscaping mapping the key common trends
Company profiling with detailed strategies, financials, and recent developments
Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

Company Profiling
- Comprehensive profiling of additional market players (up to 3)
- SWOT Analysis of key players (up to 3)
Regional Segmentation
- Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
Competitive Benchmarking
- Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

1 Executive Summary

2 Preface

2.1 Abstract
2.2 Stake Holders
2.3 Research Scope
2.4 Research Methodology
- 2.4.1 Data Mining
- 2.4.2 Data Analysis
- 2.4.3 Data Validation
- 2.4.4 Research Approach
2.5 Research Sources
- 2.5.1 Primary Research Sources
- 2.5.2 Secondary Research Sources
- 2.5.3 Assumptions

3 Market Trend Analysis

3.1 Introduction
3.2 Drivers
3.3 Restraints
3.4 Opportunities
3.5 Threats
3.6 Technology Analysis
3.7 Application Analysis
3.8 End User Analysis
3.9 Emerging Markets
3.10 Impact of Covid-19

4 Porters Five Force Analysis

4.1 Bargaining power of suppliers
4.2 Bargaining power of buyers
4.3 Threat of substitutes
4.4 Threat of new entrants
4.5 Competitive rivalry

5 Global AI Drug Discovery Market, By Drug Type

5.1 Introduction
5.2 Small Molecule Drug Discovery
5.3 Biologics Discovery
5.4 Peptide & Protein-Based Drugs
5.5 Regenerative & Cell Therapies
5.6 Gene Therapy Candidates
5.7 Novel Therapeutic Modalities

6 Global AI Drug Discovery Market, By Therapeutic Area

6.1 Introduction
6.2 Oncology
6.3 Neurology
6.4 Immunology
6.5 Infectious Diseases
6.6 Cardiology
6.7 Rare & Orphan Diseases

7 Global AI Drug Discovery Market, By Technology

7.1 Introduction
7.2 Machine Learning Platforms
7.3 Deep Learning & Neural Networks
7.4 Generative AI for Molecule Design
7.5 Quantum AI Drug Modeling
7.6 Structure-Based Drug Design Tools
7.7 Omics Data Analysis Systems

8 Global AI Drug Discovery Market, By Application

8.1 Introduction
8.2 Target Identification
8.3 Lead Generation & Optimization
8.4 Compound Screening
8.5 Preclinical Testing Automation
8.6 Biomarker Identification
8.7 Toxicity Prediction & Validation

9 Global AI Drug Discovery Market, By End User

9.1 Introduction
9.2 Pharmaceutical Companies & Biotechnology Companies
9.3 Academic & Research Institutes
9.4 Contract Research Organizations (CROs)
9.5 Hospitals & Clinical Labs
9.6 Other End Users

10 Global AI Drug Discovery Market, By Geography

10.1 Introduction
10.2 North America
- 10.2.1 US
- 10.2.2 Canada
- 10.2.3 Mexico
10.3 Europe
- 10.3.1 Germany
- 10.3.2 UK
- 10.3.3 Italy
- 10.3.4 France
- 10.3.5 Spain
- 10.3.6 Rest of Europe
10.4 Asia Pacific
- 10.4.1 Japan
- 10.4.2 China
- 10.4.3 India
- 10.4.4 Australia
- 10.4.5 New Zealand
- 10.4.6 South Korea
- 10.4.7 Rest of Asia Pacific
10.5 South America
- 10.5.1 Argentina
- 10.5.2 Brazil
- 10.5.3 Chile
- 10.5.4 Rest of South America
10.6 Middle East & Africa
- 10.6.1 Saudi Arabia
- 10.6.2 UAE
- 10.6.3 Qatar
- 10.6.4 South Africa
- 10.6.5 Rest of Middle East & Africa

11 Key Developments

11.1 Agreements, Partnerships, Collaborations and Joint Ventures
11.2 Acquisitions & Mergers
11.3 New Product Launch
11.4 Expansions
11.5 Other Key Strategies

12 Company Profiling

12.1 Pfizer
12.2 Roche
12.3 AstraZeneca
12.4 Moderna
12.5 Sanofi
12.6 Novartis
12.7 Johnson & Johnson
12.8 GSK
12.9 Eli Lilly
12.10 Bayer
12.11 Boehringer Ingelheim
12.12 Merck & Co.
12.13 AbbVie
12.14 Schrodinger
12.15 Exscientia
12.16 Atomwise
12.17 Insilico Medicine

簡介目錄圖表

List of Tables

Table 1 Global AI Drug Discovery Market Outlook, By Region (2024-2032) ($MN)
Table 2 Global AI Drug Discovery Market Outlook, By Drug Type (2024-2032) ($MN)
Table 3 Global AI Drug Discovery Market Outlook, By Small Molecule Drug Discovery (2024-2032) ($MN)
Table 4 Global AI Drug Discovery Market Outlook, By Biologics Discovery (2024-2032) ($MN)
Table 5 Global AI Drug Discovery Market Outlook, By Peptide & Protein-Based Drugs (2024-2032) ($MN)
Table 6 Global AI Drug Discovery Market Outlook, By Regenerative & Cell Therapies (2024-2032) ($MN)
Table 7 Global AI Drug Discovery Market Outlook, By Gene Therapy Candidates (2024-2032) ($MN)
Table 8 Global AI Drug Discovery Market Outlook, By Novel Therapeutic Modalities (2024-2032) ($MN)
Table 9 Global AI Drug Discovery Market Outlook, By Therapeutic Area (2024-2032) ($MN)
Table 10 Global AI Drug Discovery Market Outlook, By Oncology (2024-2032) ($MN)
Table 11 Global AI Drug Discovery Market Outlook, By Neurology (2024-2032) ($MN)
Table 12 Global AI Drug Discovery Market Outlook, By Immunology (2024-2032) ($MN)
Table 13 Global AI Drug Discovery Market Outlook, By Infectious Diseases (2024-2032) ($MN)
Table 14 Global AI Drug Discovery Market Outlook, By Cardiology (2024-2032) ($MN)
Table 15 Global AI Drug Discovery Market Outlook, By Rare & Orphan Diseases (2024-2032) ($MN)
Table 16 Global AI Drug Discovery Market Outlook, By Technology (2024-2032) ($MN)
Table 17 Global AI Drug Discovery Market Outlook, By Machine Learning Platforms (2024-2032) ($MN)
Table 18 Global AI Drug Discovery Market Outlook, By Deep Learning & Neural Networks (2024-2032) ($MN)
Table 19 Global AI Drug Discovery Market Outlook, By Generative AI for Molecule Design (2024-2032) ($MN)
Table 20 Global AI Drug Discovery Market Outlook, By Quantum AI Drug Modeling (2024-2032) ($MN)
Table 21 Global AI Drug Discovery Market Outlook, By Structure-Based Drug Design Tools (2024-2032) ($MN)
Table 22 Global AI Drug Discovery Market Outlook, By Omics Data Analysis Systems (2024-2032) ($MN)
Table 23 Global AI Drug Discovery Market Outlook, By Application (2024-2032) ($MN)
Table 24 Global AI Drug Discovery Market Outlook, By Target Identification (2024-2032) ($MN)
Table 25 Global AI Drug Discovery Market Outlook, By Lead Generation & Optimization (2024-2032) ($MN)
Table 26 Global AI Drug Discovery Market Outlook, By Compound Screening (2024-2032) ($MN)
Table 27 Global AI Drug Discovery Market Outlook, By Preclinical Testing Automation (2024-2032) ($MN)
Table 28 Global AI Drug Discovery Market Outlook, By Biomarker Identification (2024-2032) ($MN)
Table 29 Global AI Drug Discovery Market Outlook, By Toxicity Prediction & Validation (2024-2032) ($MN)
Table 30 Global AI Drug Discovery Market Outlook, By End User (2024-2032) ($MN)
Table 31 Global AI Drug Discovery Market Outlook, By Pharmaceutical Companies & Biotechnology Companies (2024-2032) ($MN)
Table 32 Global AI Drug Discovery Market Outlook, By Academic & Research Institutes (2024-2032) ($MN)
Table 33 Global AI Drug Discovery Market Outlook, By Contract Research Organizations (CROs) (2024-2032) ($MN)
Table 34 Global AI Drug Discovery Market Outlook, By Hospitals & Clinical Labs (2024-2032) ($MN)
Table 35 Global AI Drug Discovery Market Outlook, By Other End Users (2024-2032) ($MN)