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市场调查报告书

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1577080

人工智慧在药物发现市场、机会、成长动力、产业趋势分析与预测，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个工作天内

价格

简介目录

2023 年，全球人工智慧药物发现市场价值为 19 亿美元，预计在预测期内复合年增长率为 29.6%。这一成长是由创投公司、製药公司和政府机构增加投资所推动的，反映出人们对人工智慧技术加快药物发现和满足医疗需求的信心。随着公司利用互补的专业知识和资源，协作也不断加强。基因组学和临床试验资料等医疗保健资料的可用性推动了人工智慧在药物发现中的采用。慢性病的流行和对创新疗法的需求促使製药公司投资人工智慧技术。

药物发现产业的整体人工智慧根据组成部分、技术、应用类型、治疗领域、最终用途和地区进行分类。

按组件划分，市场分为软体和服务。到 2023 年，软体部门将占据 68.3% 的收入份额，预计复合年增长率为 29.4%。製药业对高阶分析和机器学习工具的需求推动了这一成长。人工智慧软体提高了效率并使药物发现阶段自动化，从而减少了时间和资源。云端和高效能运算的进步也促进了人工智慧软体的采用。

按技术划分，市场分为机器学习和其他技术。分析期间，机器学习领域预计将达到 159 亿美元。机器学习透过分析大量资料集来识别候选药物、预测疗效和安全性以及优化临床试验，从而彻底改变药物开发。运算能力、资料可用性和演算法开发的进步支持了成长。此外，机器学习能够根据个别患者的基因图谱进行个人化医疗和客製化治疗，这进一步推动了其在製药业的采用。

到 2023 年，北美将占据 47.4% 的市场份额，并将大幅成长。该地区强大的製药业、领先的人工智慧技术提供者以及支援性的监管环境推动了这一主导地位。学术界、工业界和技术供应商之间的合作进一步巩固了北美在人工智慧药物发现领域的地位。 2023 年美国市场价值为 8.236 亿美元，预计复合年增长率为 29.1%。政府加强推动精准医疗、研究经费以及主要製药公司在研发中采用人工智慧工具，推动了成长。美国先进的医疗保健基础设施支援早期人工智慧技术的采用，确保了重要的市场占有率。

产业生态系统分析
产业影响力
- 成长动力
  - 越来越多的跨产业合作和伙伴关係
  - 人工智慧减少了药物发现和开发过程中使用的成本和时间
  - 慢性病和传染病率上升
- 产业陷阱与挑战
  - 药物发现领域缺乏资料集
  - 理解和专业知识有限
成长潜力分析
监管环境
药物发现中的人工智慧 - 按阶段和治疗领域分類的药物
2018-2020 年人工智慧在药物发现领域获得的资金
波特的分析
PESTEL分析

第 4 章：竞争格局

介绍
投资及合作版图
- 投资格局
- 合作格局
公司矩阵分析
主要市场参与者的竞争分析
竞争定位矩阵
战略仪表板

第 5 章：市场估计与预测：按组成部分，2018 年 - 2032 年

主要趋势
软体
服务

第 6 章：市场估计与预测：按技术划分，2018 年 - 2032 年

主要趋势
机器学习
- 深度学习
- 监督学习
- 无监督学习
- 其他机器学习技术
其他技术

第 7 章：市场估计与预测：按应用类型，2018 年 - 2032 年

主要趋势
分子库筛选
目标识别
药物优化和再利用
从头药物设计
临床前测试

第 8 章：市场估计与预测：按治疗领域，2018 年 - 2032 年

主要趋势
肿瘤学
神经退化性疾病
发炎性疾病
传染病
代谢性疾病
罕见疾病
心血管疾病
其他治疗领域

第 9 章：市场估计与预测：依最终用途，2018 年 - 2032 年

主要趋势
製药和生物技术公司
合约研究组织 (CRO)
其他最终用户

第 10 章：市场估计与预测：按地区，2018 - 2032

主要趋势
北美洲
- 我们
- 加拿大
欧洲
- 德国
- 英国
- 法国
- 西班牙
- 义大利
- 欧洲其他地区
亚太地区
- 中国
- 日本
- 印度
- 澳洲
- 韩国
- 亚太地区其他地区
拉丁美洲
- 巴西
- 墨西哥
- 拉丁美洲其他地区
中东和非洲
- 南非
- 沙乌地阿拉伯
- 中东和非洲其他地区

第 11 章：公司简介

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

简介目录

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