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市場調查報告書

商品編碼

1577080

人工智慧在藥物發現市場、機會、成長動力、產業趨勢分析與預測，2024-2032

Artificial Intelligence in Drug Discovery Market, Opportunity, Growth Drivers, Industry Trend Analysis and Forecast, 2024-2032

出版日期: 2024年08月08日 | 出版商:

Global Market Insights Inc. | 英文 242 Pages | 商品交期: 2-3個工作天內

價格

簡介目錄

2023 年，全球人工智慧藥物發現市場價值為 19 億美元，預計在預測期內複合年成長率為 29.6%。這一成長是由創投公司、製藥公司和政府機構增加投資所推動的，反映出人們對人工智慧技術加快藥物發現和滿足醫療需求的信心。隨著公司利用互補的專業知識和資源，協作也不斷加強。基因組學和臨床試驗資料等醫療保健資料的可用性推動了人工智慧在藥物發現中的採用。慢性病的流行和對創新療法的需求促使製藥公司投資人工智慧技術。

藥物發現產業的整體人工智慧根據組成部分、技術、應用類型、治療領域、最終用途和地區進行分類。

按組件分類，市場分為軟體和服務。到 2023 年，軟體部門將佔據 68.3% 的收入佔有率，預計複合年成長率為 29.4%。製藥業對高階分析和機器學習工具的需求推動了這一成長。人工智慧軟體提高了效率並使藥物發現階段自動化，從而減少了時間和資源。雲端和高效能運算的進步也促進了人工智慧軟體的採用。

按技術分類，市場分為機器學習和其他技術。分析期間，機器學習領域預計將達到 159 億美元。機器學習透過分析大量資料集來識別候選藥物、預測療效和安全性以及最佳化臨床試驗，從而徹底改變藥物開發。運算能力、資料可用性和演算法開發的進步支持了成長。此外，機器學習能夠根據個別患者的基因圖譜進行個人化醫療和客製化治療，這進一步推動了其在製藥業的採用。

到 2023 年，北美將佔據 47.4% 的市場佔有率，並將大幅成長。該地區強大的製藥業、領先的人工智慧技術提供者以及支援性的監管環境推動了這一主導地位。學術界、工業界和技術供應商之間的合作進一步鞏固了北美在人工智慧藥物發現領域的地位。 2023 年美國市場價值為 8.236 億美元，預計複合年成長率為 29.1%。政府加強推動精準醫療、研究經費以及主要製藥公司在研發中採用人工智慧工具，推動了成長。美國先進的醫療保健基礎設施支援早期人工智慧技術的採用，確保了重要的市場佔有率。

產業生態系統分析
產業影響力
- 成長動力
  - 越來越多的跨產業合作和夥伴關係
  - 人工智慧減少了藥物發現和開發過程中使用的成本和時間
  - 慢性病和傳染病率上升
- 產業陷阱與挑戰
  - 藥物發現領域缺乏資料集
  - 理解和專業知識有限
成長潛力分析
監管環境
藥物發現中的人工智慧 - 按階段和治療領域分類的藥物
2018-2020 年人工智慧在藥物發現領域獲得的資金
波特的分析
PESTEL分析

第 4 章：競爭格局

介紹
投資及合作版圖
- 投資格局
- 合作格局
公司矩陣分析
主要市場參與者的競爭分析
競爭定位矩陣
戰略儀表板

第 5 章：市場估計與預測：按組成部分，2018 年 - 2032 年

主要趨勢
軟體
服務

第 6 章：市場估計與預測：按技術分類，2018 年 - 2032 年

主要趨勢
機器學習
- 深度學習
- 監督學習
- 無監督學習
- 其他機器學習技術
其他技術

第 7 章：市場估計與預測：按應用類型，2018 年 - 2032 年

主要趨勢
分子庫篩選
目標識別
藥物最佳化和再利用
從頭藥物設計
臨床前測試

第 8 章：市場估計與預測：按治療領域，2018 年 - 2032 年

主要趨勢
腫瘤學
神經退化性疾病
發炎性疾病
傳染病
代謝性疾病
罕見疾病
心血管疾病
其他治療領域

第 9 章：市場估計與預測：依最終用途，2018 年 - 2032 年

主要趨勢
製藥和生物技術公司
合約研究組織 (CRO)
其他最終用戶

第 10 章：市場估計與預測：按地區，2018 - 2032

主要趨勢
北美洲
- 美國
- 加拿大
歐洲
- 德國
- 英國
- 法國
- 西班牙
- 義大利
- 歐洲其他地區
亞太地區
- 中國
- 日本
- 印度
- 澳洲
- 韓國
- 亞太地區其他地區
拉丁美洲
- 巴西
- 墨西哥
- 拉丁美洲其他地區
中東和非洲
- 南非
- 沙烏地阿拉伯
- 中東和非洲其他地區

第 11 章：公司簡介

Alphabet Inc. (DeepMind)
Atomwise Inc.
BenevolentAI
Cyclica
Deep Genomic
Deargen Inc.
Exscientia
International Business Machines Corporation
Microsoft Corporation
NVIDIA Corporation

簡介目錄

Product Code: 6361

The Global Artificial Intelligence In Drug Discovery Market was valued at USD 1.9 billion in 2023 and is projected to grow at a CAGR of 29.6% during the forecast period. This growth is driven by increased investments from venture capital firms, pharmaceutical companies, and government agencies, reflecting confidence in AI technologies to expedite drug discovery and address medical needs. Collaboration is rising as companies leverage complementary expertise and resources. The availability of healthcare data, such as genomics and clinical trial data, fuels AI adoption in drug discovery. The prevalence of chronic diseases and the need for innovative therapies push pharmaceutical companies to invest in AI technologies.

The overall artificial intelligence in drug discovery industry is classified based on the component, technology, application type, therapeutic area, end use , and region.

By component, the market is segmented into software and services. The software segment held a 68.3% revenue share in 2023 and is expected to grow at a 29.4% CAGR. The demand for advanced analytics and machine learning tools in the pharmaceutical industry drives this growth. AI software enhances efficiency and automates drug discovery stages, reducing time and resources. Advancements in cloud and high-performance computing also boost AI software adoption.

By technology, the market is classified into machine learning and other technologies. The machine learning segment is expected to reach USD 15.9 billion during the analysis period. Machine learning revolutionizes drug development by analyzing vast datasets to identify drug candidates, predict efficacy and safety, and optimize clinical trials. Growth is supported by advancements in computational power, data availability, and algorithm development. Additionally, machine learning's ability to personalize medicine and tailor treatments to individual patients' genetic profiles further drives its adoption in the pharmaceutical industry.

North America held a 47.4% market share in 2023 and is set for substantial growth. The region's strong pharmaceutical industry, leading AI technology providers, and supportive regulatory environment drive this dominance. Collaborations between academia, industry, and tech providers further bolster North America's position in AI drug discovery. The U.S. market was valued at USD 823.6 million in 2023 and is projected to grow at a 29.1% CAGR. Increased government initiatives promoting precision medicine, research funding, and the adoption of AI tools in R&D by major pharmaceutical companies propel growth. The advanced healthcare infrastructure in the U.S. supports early AI technology adoption, ensuring a significant market presence.

Chapter 1 Methodology and Scope

1.1 Market scope and definitions
1.2 Research design
- 1.2.1 Research approach
- 1.2.2 Data collection methods
1.3 Base estimates and calculations
- 1.3.1 Base year calculation
- 1.3.2 Key trends for market estimation
1.4 Forecast model
1.5 Primary research and validation
- 1.5.1 Primary sources
- 1.5.2 Data mining sources

Chapter 2 Executive Summary

2.1 Industry 360° synopsis

Chapter 3 Industry Insights

3.1 Industry ecosystem analysis
3.2 Industry impact forces
- 3.2.1 Growth drivers
  - 3.2.1.1 Growing number of cross industry collaboration and partnership
  - 3.2.1.2 Artificial intelligence reduces cost and time utilized in the drug discovery and development process
  - 3.2.1.3 Rising prevalence of chronic and infectious diseases
- 3.2.2 Industry pitfalls and challenges
  - 3.2.2.1 Lack of data sets in the field of drug discovery
  - 3.2.2.2 Limited understanding and expertise
3.3 Growth potential analysis
3.4 Regulatory landscape
3.5 AI in drug discovery - drugs by stage and therapeutic area
3.6 Funding received for AI in drug discovery, 2018-2020
3.7 Porter's analysis
3.8 PESTEL analysis

Chapter 4 Competitive Landscape, 2023

4.1 Introduction
4.2 Investment and partnership landscape
- 4.2.1 Investment landscape
- 4.2.2 Partnership landscape
4.3 Company matrix analysis
4.4 Competitive analysis of major market players
4.5 Competitive positioning matrix
4.6 Strategy dashboard

Chapter 5 Market Estimates and Forecast, By Component, 2018 - 2032 ($ Mn)

5.1 Key trends
5.2 Software
5.3 Services

Chapter 6 Market Estimates and Forecast, By Technology, 2018 - 2032 ($ Mn)

6.1 Key trends
6.2 Machine learning
- 6.2.1 Deep learning
- 6.2.2 Supervised learning
- 6.2.3 Unsupervised learning
- 6.2.4 Other machine learning technologies
6.3 Other technologies

Chapter 7 Market Estimates and Forecast, By Application Type, 2018 - 2032 ($ Mn)

7.1 Key trends
7.2 Molecular library screening
7.3 Target identification
7.4 Drug optimization and repurposing
7.5 De novo drug designing
7.6 Preclinical testing

Chapter 8 Market Estimates and Forecast, By Therapeutic Area, 2018 - 2032 ($ Mn)

8.1 Key trends
8.2 Oncology
8.3 Neurodegenerative diseases
8.4 Inflammatory diseases
8.5 Infectious diseases
8.6 Metabolic diseases
8.7 Rare diseases
8.8 Cardiovascular diseases
8.9 Other therapeutic areas

Chapter 9 Market Estimates and Forecast, By End-Use, 2018 - 2032 ($ Mn)

9.1 Key trends
9.2 Pharmaceutical and biotechnology companies
9.3 Contract research organization (CROs)
9.4 Other end-users

Chapter 10 Market Estimates and Forecast, By Region, 2018 - 2032 ($ Mn)

10.1 Key trends
10.2 North America
- 10.2.1 U.S.
- 10.2.2 Canada
10.3 Europe
- 10.3.1 Germany
- 10.3.2 UK
- 10.3.3 France
- 10.3.4 Spain
- 10.3.5 Italy
- 10.3.6 Rest of Europe
10.4 Asia Pacific
- 10.4.1 China
- 10.4.2 Japan
- 10.4.3 India
- 10.4.4 Australia
- 10.4.5 South Korea
- 10.4.6 Rest of Asia Pacific
10.5 Latin America
- 10.5.1 Brazil
- 10.5.2 Mexico
- 10.5.3 Rest of Latin America
10.6 Middle East and Africa
- 10.6.1 South Africa
- 10.6.2 Saudi Arabia
- 10.6.3 Rest of Middle East and Africa