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2032 年人工智慧藥物研發平台市場預測：按組件、治療領域、藥物類型、部署模式、最終用戶和地區進行的全球分析

AI-Powered Drug Discovery Platforms Market Forecasts to 2032 - Global Analysis By Component (Solutions and Services), Therapeutic Area, Drug Type, Deployment Model, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3個工作天內

價格

根據 Stratistics MRC 的數據，全球 AI 藥物研發平台市場規模預計在 2025 年達到 24.629 億美元，到 2032 年將達到 157.059 億美元，預測期內的複合年成長率為 30.3%。

AI藥物研發平台是先進的運算系統，利用人工智慧加速和最佳化新藥化合物的識別、設計和測試流程。這些平台分析海量生物醫學資料集，包括基因組學、蛋白質組學和臨床記錄，以預測藥物-標靶相互作用、評估毒性並模擬分子行為。將傳統上耗時的任務自動化，可降低研發成本並縮短開發週期。機器學習演算法能夠持續改進模型，提高準確性和成功率。 AI平台廣泛應用於精準醫療、腫瘤學和罕見疾病研究，正在將藥物研發轉變為更快、數據主導、更有效率的過程。

縮短藥物研發時間

加快藥物開發進度是人工智慧藥物發現平台的關鍵驅動力。這些系統簡化了化合物篩檢、標靶識別和毒性預測，顯著縮短了臨床前和臨床階段所需的時間。透過自動化數據分析和分子相互作用模擬，人工智慧能夠加快決策速度並更早檢驗。這種效率對於製藥公司至關重要，尤其是那些正在應對新興疾病和競爭壓力的公司，因為他們力求更快地將治療方法推向市場。

數據品質和整合問題

數據品質和整合問題是人工智慧驅動的藥物研發市場的主要限制因素。不一致、不完整或孤立的生物醫學數據會降低模型的準確性和可靠性。整合基因組學、蛋白質組學和臨床記錄等多樣化資料集需要先進的基礎設施和標準化。如果沒有清晰、可互通的數據，人工智慧演算法就難以產生有意義的洞察，從而限制了其有效性。應對這些挑戰對於充分發揮人工智慧在藥物研發領域的潛力至關重要。

製藥業研發成本上升

製藥業不斷上漲的研發成本為人工智慧藥物研發平台帶來了巨大的機會。傳統的藥物研發成本高且耗時，通常需要數十億美元的投資。人工智慧透過自動化早期研究、改進候選化合物的選擇以及最大限度地減少試驗失敗來降低成本。隨著企業尋求經濟高效的解決方案以保持創新和盈利，人工智慧平台提供了一種可擴展的數據主導方法，可以簡化整個藥物研發生命週期的運作並提高生產力。

初期投資高

高昂的初始投資是人工智慧藥物研發平台應用面臨的一大挑戰。建構強大的人工智慧基礎設施需要大量資金用於數據採集、運算資源和技術人員。規模較小的公司可能無法承擔此類技術，從而限制了其市場滲透。此外，漫長的開發週期和不明確的投資報酬率也阻礙了相關人員的參與。缺乏財務獎勵或合作模式，領先成本障礙可能會減緩從傳統方法轉向人工智慧主導的藥物研發的轉變。

COVID-19的影響：

新冠疫情凸顯了快速藥物開發的迫切性，並加速了人們對人工智慧平台的興趣。這些系統透過分析大量資料集並預測分子交互作用，支持了疫苗和治療方法的研究。然而，供應鏈中斷和資源重新分配暫時減緩了採用速度。疫情過後，該產業將數位轉型和韌性放在了優先位置，而人工智慧在未來的應對中發揮核心作用。最終，這場疫情強化了人工智慧在實現更快、數據主導的醫藥創新方面的價值。

預計腫瘤學將成為預測期內最大的領域

由於癌症研究的複雜性和緊迫性，預計腫瘤學將在預測期內佔據最大的市場佔有率。人工智慧平台有助於識別新標靶、預測藥物反應並根據基因圖譜制定個人化治療方案。隨著癌症發病率的上升和精準醫療需求的不斷成長，製藥公司正在大力投資人工智慧工具，以加速抗癌藥物的研發。這些平台正在增強臨床試驗設計和生物標記發現，使腫瘤學成為最大、影響力最大的應用領域。

預計生技公司板塊在預測期內將以最高的複合年成長率成長

生技公司預計將在預測期內實現最高成長率，因為這些敏捷、創新主導的公司正在迅速採用人工智慧來增強其藥物發現管道並降低開發成本。憑藉最尖端科技和專業資料集，生物技術公司正在利用人工智慧進行靶點識別、分子設計和預測建模。它們的靈活性以及對利基療法的專注是人工智慧藥物發現市場成長的關鍵驅動力。

佔比最大的地區：

在預測期內，由於製藥業的擴張、人工智慧投資的增加以及政府的支持政策，預計亞太地區將佔據最大的市場佔有率。中國、印度和日本等國家正在推動其數位醫療基礎設施建設，並促進高科技和生物技術領域之間的合作。該地區龐大的患者群體和豐富的生物醫學數據資源將進一步增強人工智慧模型的訓練和部署。這些因素共同使亞太地區成為全球市場的主導力量。

複合年成長率最高的地區：

在預測期內，北美預計將憑藉其強大的研發能力、先進的人工智慧基礎設施以及科技巨頭與製藥公司之間的戰略夥伴關係，實現最高的複合年成長率。美國在人工智慧創新和監管支援方面處於領先地位，這促進了其在醫療保健和生物技術領域的快速應用。精準醫療需求的不斷成長以及對人工智慧新興企業的強勁投資正在推動這一成長。北美在數位轉型和藥物開發方面的領先地位，使其成為該市場成長最快的地區。

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目錄

第1章執行摘要

第2章 前言

  • 概述
  • 相關利益者
  • 調查範圍
  • 調查方法
    • 資料探勘
    • 數據分析
    • 數據檢驗
    • 研究途徑
  • 研究材料
    • 主要研究資料
    • 次級研究資訊來源
    • 先決條件

第3章市場走勢分析

  • 驅動程式
  • 抑制因素
  • 機會
  • 威脅
  • 最終用戶分析
  • 新興市場
  • COVID-19的影響

第4章 波特五力分析

  • 供應商的議價能力
  • 買方的議價能力
  • 替代品的威脅
  • 新進入者的威脅
  • 競爭對手之間的競爭

5. 全球人工智慧藥物研發平台市場（按組件）

  • 軟體
  • 服務

6. 全球人工智慧藥物發現平台市場（按治療領域）

  • 腫瘤學
  • 神經病學
  • 心血管疾病
  • 感染疾病
  • 免疫學和炎症
  • 代謝紊亂
  • 罕見疾病
  • 其他

7. 全球人工智慧藥物發現平台市場（按藥物類型）

  • 小分子
  • 生物製藥
    • 單株抗體
    • 胜肽和蛋白質
    • 基於RNA的療法
  • 細胞和基因治療候選藥物

第8章全球人工智慧藥物發現平台市場（按部署模式）

  • 雲端基礎的平台
  • 本地解決方案
  • 混合模式

第9章全球人工智慧藥物發現平台市場（按最終用戶）

  • 製藥公司
  • 生技公司
  • 合約開發組織（CRO）
  • 學術研究機構
  • 醫療保健提供者

第 10 章全球 AI 藥物發現平台市場（按地區）

  • 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
    • 其他歐洲國家
  • 亞太地區
    • 日本
    • 中國
    • 印度
    • 澳洲
    • 紐西蘭
    • 韓國
    • 其他亞太地區
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 其他南美
  • 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
  • 收購與合併
  • 新產品發布
  • 業務擴展
  • 其他關鍵策略

第 12 章：公司概況

  • Atomwise
  • BenevolentAI
  • Insilico Medicine
  • Recursion Pharmaceuticals
  • Schrodinger
  • Exscientia
  • Healx
  • Cyclica
  • LabGenius
  • Numerate
  • Owkin
  • Relay Therapeutics
  • Generate Biomedicines
  • Cloud Pharmaceuticals
  • NVIDIA Corporation
Product Code: SMRC31510

According to Stratistics MRC, the Global AI-Powered Drug Discovery Platforms Market is accounted for $2,462.9 million in 2025 and is expected to reach $15,705.9 million by 2032 growing at a CAGR of 30.3% during the forecast period. AI-powered drug discovery platforms are advanced computational systems that leverage artificial intelligence to accelerate and optimize the process of identifying, designing, and testing new pharmaceutical compounds. These platforms analyze vast biomedical datasets-including genomics, proteomics, and clinical records-to predict drug-target interactions, assess toxicity, and simulate molecular behavior. By automating traditionally time-consuming tasks, they reduce R&D costs and shorten development timelines. Machine learning algorithms enable continuous refinement of models, improving accuracy and success rates. Widely used in precision medicine, oncology, and rare disease research, AI-powered platforms are transforming drug discovery into a faster, data-driven, and more efficient process.

Market Dynamics:

Driver:

Accelerated Drug Development Timelines

Accelerated drug development timelines are a key driver for AI-powered drug discovery platforms. These systems streamline compound screening, target identification, and toxicity prediction, significantly reducing the time required for preclinical and clinical phases. By automating data analysis and simulating molecular interactions, AI enables faster decision-making and early-stage validation. This efficiency is crucial for pharmaceutical companies aiming to bring therapies to market quickly, especially in response to emerging diseases and competitive pressures.

Restraint:

Data Quality and Integration Issues

Data quality and integration issues pose a major restraint to the AI-powered drug discovery market. Inconsistent, incomplete, or siloed biomedical data can impair model accuracy and reliability. Integrating diverse datasets-such as genomics, proteomics, and clinical records-requires advanced infrastructure and standardization. Without clean, interoperable data, AI algorithms struggle to generate meaningful insights, limiting their effectiveness. Addressing these challenges is essential to unlock the full potential of AI in pharmaceutical research and development.

Opportunity:

Rising R&D Costs in Pharma

Rising R&D costs in the pharmaceutical industry present a significant opportunity for AI-powered drug discovery platforms. Traditional drug development is expensive and time-consuming, often requiring billions in investment. AI reduces costs by automating early-stage research, improving candidate selection, and minimizing trial failures. As companies seek cost-effective solutions to maintain innovation and profitability, AI platforms offer a scalable, data-driven approach to streamline operations and enhance productivity across the drug development lifecycle.

Threat:

High Initial Investment

High initial investment is a notable threat to the adoption of AI-powered drug discovery platforms. Building robust AI infrastructure requires substantial funding for data acquisition, computing resources, and skilled personnel. Smaller firms may struggle to afford these technologies, limiting market penetration. Additionally, long development cycles and uncertain ROI can deter stakeholders. Without financial incentives or collaborative models, the upfront cost barrier may slow the transition from traditional methods to AI-driven drug discovery.

Covid-19 Impact:

The COVID-19 pandemic highlighted the urgency of rapid drug development, accelerating interest in AI-powered platforms. These systems supported vaccine and therapeutic research by analyzing vast datasets and predicting molecular interactions. However, supply chain disruptions and resource reallocation temporarily slowed adoption. Post-pandemic, the industry is prioritizing digital transformation and resilience, with AI playing a central role in future preparedness. The pandemic ultimately reinforced the value of AI in enabling faster, data-driven pharmaceutical innovation.

The oncology segment is expected to be the largest during the forecast period

The oncology segment is expected to account for the largest market share during the forecast period due to the complexity and urgency of cancer research. AI-powered platforms help identify novel targets, predict drug responses, and personalize treatments based on genetic profiles. With rising cancer incidence and demand for precision medicine, pharmaceutical companies are investing heavily in AI tools to accelerate oncology drug development. These platforms enhance clinical trial design and biomarker discovery, making oncology the largest and most impactful application area.

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

Over the forecast period, the biotechnology firms segment is predicted to witness the highest growth rate because these agile, innovation-driven companies are rapidly adopting AI to enhance drug discovery pipelines and reduce development costs. With access to cutting-edge technologies and specialized datasets, biotech firms leverage AI for target identification, molecule design, and predictive modeling. Their flexibility and focus on niche therapies position them as key drivers of growth in the AI-powered drug discovery market.

Region with largest share:

During the forecast period, the Asia Pacific region is expected to hold the largest market share owing to its expanding pharmaceutical industry, growing investments in AI, and supportive government initiatives. Countries like China, India, and Japan are advancing digital healthcare infrastructure and fostering collaborations between tech and biotech sectors. The region's large patient population and rich biomedical data resources further enhance AI model training and deployment. These factors collectively position Asia Pacific as a dominant force in the global market.

Region with highest CAGR:

Over the forecast period, the North America region is anticipated to exhibit the highest CAGR due to strong R&D capabilities, advanced AI infrastructure, and strategic partnerships between tech giants and pharmaceutical companies. The U.S. leads in AI innovation and regulatory support, fostering rapid adoption across healthcare and biotech sectors. Increasing demand for precision medicine, coupled with robust investment in AI startups, is fueling growth. North America's leadership in digital transformation and drug development makes it the fastest-growing region in this market.

Key players in the market

Some of the key players in AI-Powered Drug Discovery Platforms Market include Atomwise, BenevolentAI, Insilico Medicine, Recursion Pharmaceuticals, Schrodinger, Exscientia, Healx, Cyclica, LabGenius, Numerate, Owkin, Relay Therapeutics, Generate Biomedicines, Cloud Pharmaceuticals and NVIDIA Corporation.

Key Developments:

In September 2025, NVIDIA and Intel have joined forces to co-develop custom AI infrastructure and personal computing products. This strategic partnership aims to seamlessly integrate NVIDIA's accelerated computing capabilities with Intel's leading CPU technologies, utilizing NVIDIA's NVLink to deliver cutting-edge solutions across hyperscale, enterprise, and consumer markets.

In September 2025, OpenAI and NVIDIA have embarked on a strategic partnership to deploy at least 10 gigawatts of NVIDIA systems, marking a significant leap in AI infrastructure development. This collaboration aims to establish a robust foundation for training and operating next-generation AI models, propelling both companies toward the realization of superintelligence.

Components Covered:

  • Software
  • Services

Therapeutic Areas Covered:

  • Oncology
  • Neurology
  • Cardiovascular Diseases
  • Infectious Diseases
  • Immunology & Inflammation
  • Metabolic Disorders
  • Rare & Orphan Diseases
  • Other Therapeutic Areas

Drug Types Covered:

  • Small Molecules
  • Biologics
  • Cell & Gene Therapy Candidates

Deployment Models Covered:

  • Cloud-Based Platforms
  • On-Premises Solutions
  • Hybrid Models

End Users Covered:

  • Pharmaceutical Companies
  • Biotechnology Firms
  • Contract Research Organizations (CROs)
  • Academic & Research Institutes
  • Healthcare Providers

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 2022, 2023, 2024, 2026, and 2030
  • 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

Table of Contents

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 End User Analysis
  • 3.7 Emerging Markets
  • 3.8 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-Powered Drug Discovery Platforms Market, By Component

  • 5.1 Introduction
  • 5.2 Software
  • 5.3 Services

6 Global AI-Powered Drug Discovery Platforms Market, By Therapeutic Area

  • 6.1 Introduction
  • 6.2 Oncology
  • 6.3 Neurology
  • 6.4 Cardiovascular Diseases
  • 6.5 Infectious Diseases
  • 6.6 Immunology & Inflammation
  • 6.7 Metabolic Disorders
  • 6.8 Rare & Orphan Diseases
  • 6.9 Other Therapeutic Areas

7 Global AI-Powered Drug Discovery Platforms Market, By Drug Type

  • 7.1 Introduction
  • 7.2 Small Molecules
  • 7.3 Biologics
    • 7.3.1 Monoclonal Antibodies
    • 7.3.2 Peptides & Proteins
    • 7.3.3 RNA-based Therapeutics
  • 7.4 Cell & Gene Therapy Candidates

8 Global AI-Powered Drug Discovery Platforms Market, By Deployment Model

  • 8.1 Introduction
  • 8.2 Cloud-Based Platforms
  • 8.3 On-Premises Solutions
  • 8.4 Hybrid Models

9 Global AI-Powered Drug Discovery Platforms Market, By End User

  • 9.1 Introduction
  • 9.2 Pharmaceutical Companies
  • 9.3 Biotechnology Firms
  • 9.4 Contract Research Organizations (CROs)
  • 9.5 Academic & Research Institutes
  • 9.6 Healthcare Providers

10 Global AI-Powered Drug Discovery Platforms 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 Atomwise
  • 12.2 BenevolentAI
  • 12.3 Insilico Medicine
  • 12.4 Recursion Pharmaceuticals
  • 12.5 Schrodinger
  • 12.6 Exscientia
  • 12.7 Healx
  • 12.8 Cyclica
  • 12.9 LabGenius
  • 12.10 Numerate
  • 12.11 Owkin
  • 12.12 Relay Therapeutics
  • 12.13 Generate Biomedicines
  • 12.14 Cloud Pharmaceuticals
  • 12.15 NVIDIA Corporation

List of Tables

  • Table 1 Global AI-Powered Drug Discovery Platforms Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global AI-Powered Drug Discovery Platforms Market Outlook, By Component (2024-2032) ($MN)
  • Table 3 Global AI-Powered Drug Discovery Platforms Market Outlook, By Software (2024-2032) ($MN)
  • Table 4 Global AI-Powered Drug Discovery Platforms Market Outlook, By Services (2024-2032) ($MN)
  • Table 5 Global AI-Powered Drug Discovery Platforms Market Outlook, By Therapeutic Area (2024-2032) ($MN)
  • Table 6 Global AI-Powered Drug Discovery Platforms Market Outlook, By Oncology (2024-2032) ($MN)
  • Table 7 Global AI-Powered Drug Discovery Platforms Market Outlook, By Neurology (2024-2032) ($MN)
  • Table 8 Global AI-Powered Drug Discovery Platforms Market Outlook, By Cardiovascular Diseases (2024-2032) ($MN)
  • Table 9 Global AI-Powered Drug Discovery Platforms Market Outlook, By Infectious Diseases (2024-2032) ($MN)
  • Table 10 Global AI-Powered Drug Discovery Platforms Market Outlook, By Immunology & Inflammation (2024-2032) ($MN)
  • Table 11 Global AI-Powered Drug Discovery Platforms Market Outlook, By Metabolic Disorders (2024-2032) ($MN)
  • Table 12 Global AI-Powered Drug Discovery Platforms Market Outlook, By Rare & Orphan Diseases (2024-2032) ($MN)
  • Table 13 Global AI-Powered Drug Discovery Platforms Market Outlook, By Other Therapeutic Areas (2024-2032) ($MN)
  • Table 14 Global AI-Powered Drug Discovery Platforms Market Outlook, By Drug Type (2024-2032) ($MN)
  • Table 15 Global AI-Powered Drug Discovery Platforms Market Outlook, By Small Molecules (2024-2032) ($MN)
  • Table 16 Global AI-Powered Drug Discovery Platforms Market Outlook, By Biologics (2024-2032) ($MN)
  • Table 17 Global AI-Powered Drug Discovery Platforms Market Outlook, By Monoclonal Antibodies (2024-2032) ($MN)
  • Table 18 Global AI-Powered Drug Discovery Platforms Market Outlook, By Peptides & Proteins (2024-2032) ($MN)
  • Table 19 Global AI-Powered Drug Discovery Platforms Market Outlook, By RNA-based Therapeutics (2024-2032) ($MN)
  • Table 20 Global AI-Powered Drug Discovery Platforms Market Outlook, By Cell & Gene Therapy Candidates (2024-2032) ($MN)
  • Table 21 Global AI-Powered Drug Discovery Platforms Market Outlook, By Deployment Model (2024-2032) ($MN)
  • Table 22 Global AI-Powered Drug Discovery Platforms Market Outlook, By Cloud-Based Platforms (2024-2032) ($MN)
  • Table 23 Global AI-Powered Drug Discovery Platforms Market Outlook, By On-Premises Solutions (2024-2032) ($MN)
  • Table 24 Global AI-Powered Drug Discovery Platforms Market Outlook, By Hybrid Models (2024-2032) ($MN)
  • Table 25 Global AI-Powered Drug Discovery Platforms Market Outlook, By End User (2024-2032) ($MN)
  • Table 26 Global AI-Powered Drug Discovery Platforms Market Outlook, By Pharmaceutical Companies (2024-2032) ($MN)
  • Table 27 Global AI-Powered Drug Discovery Platforms Market Outlook, By Biotechnology Firms (2024-2032) ($MN)
  • Table 28 Global AI-Powered Drug Discovery Platforms Market Outlook, By Contract Research Organizations (CROs) (2024-2032) ($MN)
  • Table 29 Global AI-Powered Drug Discovery Platforms Market Outlook, By Academic & Research Institutes (2024-2032) ($MN)
  • Table 30 Global AI-Powered Drug Discovery Platforms Market Outlook, By Healthcare Providers (2024-2032) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.