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市场调查报告书
商品编码
1371911

到 2030 年药物研发市场中的人工智慧 (AI) 预测：按成分、治疗领域、技术、应用、最终用户和地区进行的全球分析

Artificial Intelligence in Drug Discovery Market Forecasts to 2030 - Global Analysis By Component, By Therapeutic Area, By Technology, Application, 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个工作天内

价格

根据 Stratistics MRC 的数据，到 2023 年，药物研发发现领域的人工智慧(AI) 全球市场将达到14 亿美元，预计在预测期内复合年增长率为31.6%，到2030 年将达到98 亿美元。 。

药物研发中的人工智慧（AI）是应用人工智慧和机器学习技术来简化和增强药物开发过程。利用演算法分析大型资料、预测潜在的候选药物、最佳化临床试验设计并识别新的药物标靶。人工智慧透过降低成本、提高研究效率和增加识别成功候选药物的可能性来加速药物研发。

根据国际糖尿病联盟（IDF）的报告，2021年全球约有5.37亿成年人（20岁至79岁）患有糖尿病。预计2030年糖尿病患者总数将增加至6.43亿，2045年将增加至7.83亿。

慢性病和感染疾病的盛行率上升

人工智慧技术提供了无与伦比的分析复杂生物资料的能力，加速了药物开发进程。随着癌症、糖尿病和抗生素抗药性感染疾病等疾病负担的日益加重，人工智慧可以帮助快速识别潜在的候选药物、标靶蛋白和治疗策略。这不仅加速了药物研发，还增加了临床试验成功的可能性并降低了开发成本。此外，人工智慧驱动的方法将能够重新利用现有药物，加速新治疗方法的发现，并最终满足世界对更有效治疗方法的迫切需求。

药物研发领域缺乏资料

人工智慧严重依赖大量且多样化的资料来源来进行准确的分析和预测，但由于隐私、资料共用和资料标准化等问题，取得此类资料往往很困难，尤其是在医疗保健领域。对相关且註释良好的资料的存取有限会阻碍人工智慧模型的训练和检验，从而导致结果不佳并错失药物研发的机会。这是有可能的。解决这些资料限制对于释放人工智慧的全部潜力、加速药物研发发现和开发以及改善医疗保健结果至关重要。

慢性疾病和感染疾病增加

人工智慧主导的解决方案非常适合透过加速创新疗法的开发来解决日益严重的全球健康危机。随着癌症和糖尿病等慢性疾病变得越来越普遍，以及抗生素抗药性感染疾病的出现，人工智慧资料主导的分析可以有效地识别潜在的候选药物、发现新的目标并改善临床结果，从而简化您的研究设计。透过利用人工智慧的力量，研究人员可以加速药物研发过程，优化个体化治疗策略，并最终采取更多措施来应对这些疾病日益增长的全球负担。我们可以开创有效且可及的治疗方法的新时代。

理解和专业知识有限

人工智慧的有效应用需要涵盖生物学、化学、资料科学和人工智慧技术的跨学科知识。缺乏能够弥合这些领域的专家可能会阻碍人工智慧主导的药物研发解决方案的开发和部署。此外，对人工智慧的能力和限制的误解可能会导致不切实际的期望。理解不足也可能导致糟糕的实验设计和对人工智慧生成见解的误解，可能会浪费资源并减慢药物研发工作。解决这些知识差距并促进专家之间的合作对于充分发挥人工智慧的潜力至关重要。

COVID-19 的影响：

COVID-19 的爆发对药物研发的人工智慧 (AI) 市场产生了重大影响。一方面，随着研究人员迫切寻求药物再利用和疫苗开发的解决方案，人工智慧主导方法的采用加速。人工智慧在识别潜在候选药物和最佳化临床试验设计方面发挥了关键作用，显着缩短了开发时间。然而，疫情也扰乱了研究工作，推迟了临床试验，转移了资源，并使基于人工智慧的药物研发计画遭受挫折。此外，对人工智慧专业知识和资料资源的需求不断增长，导致该领域的能力紧张，并凸显了基础设施改进和资料共用倡议的必要性。

预计肿瘤学将成为预测期内最大的领域

肿瘤学领域预计将出现良好的成长。人工智慧透过快速分析大量基因组、蛋白质组和临床资料，正在彻底改变肿瘤药物研发。机器学习演算法透过识别独特的基因突变、潜在的药物标靶和预测药物反应，促进针对个别癌症患者的精准药物的开发。此外，人工智慧允许将现有药物重新用于新的肿瘤学应用，从而降低开发成本和时间。随着全球癌症罹患率持续上升，利用人工智慧进行药物研发发现可以在充满挑战的癌症领域发现突破性治疗方法、优化治疗方法并改善患者的治疗结果，这提供了前所未有的改善机会。

预计临床前测试领域在预测期内年复合成长率最高

预计临床前测试领域在预测期内将以最快的年复合成长率成长。人工智慧透过分析大量资料集、预测化合物特性和评估安全性来帮助识别潜在的候选药物。透过虚拟筛选和预测建模，人工智慧加速了先导化合物的选择以进行进一步评估，并减少了与临床前研究相关的时间和成本。此外，人工智慧驱动的平台可以帮助设计更有针对性的实验，最佳化测试方案，并在药物开发的早期阶段预测潜在的毒性问题。这种创新方法提高了临床前测试的效率和成功率，最终促进更安全、更有效的药物进入市场。

比最大的地区

由于其先进的医疗基础设施、强大的研发能力和支援性的法规环境，北美在药物研发发现市场的人工智慧中占据了重要份额。随着医疗保健提供者寻求改善患者照护和治疗结果，该地区对物联网医疗设备（例如可穿戴健康追踪器和远端监控系统）的采用率很高。透过对远端医疗和资料主导的医疗保健的投资，以及对以患者为中心的护理模式的关注，北美将自己定位为利用物联网技术转变和增强医疗保健服务交付的领跑者。

复合年复合成长率最高的地区：

由于人口扩张、医疗保健需求不断增长以及数位技术的日益采用，预计亚太地区在预测期内将出现最高的年复合成长率。在政府倡议和不断增长的精通技术的消费者基础的支持下，药物研发中的人工智慧正在迅速获得接受。除了改善患者照护之外，人工智慧还正在解决农村地区远端患者监护等挑战。亚太地区巨大的市场潜力和对医疗保健创新的承诺使该地区成为全球药物研发发现人工智慧市场的关键参与者，推动医疗保健服务的变革性进步。

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  • 公司简介
    • 其他市场公司的综合分析（最多 3 家公司）
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  • 区域分割
    • 根据客户兴趣对主要国家的市场估计、预测和年复合成长率（註：基于可行性检查）
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    • 根据产品系列、地理分布和策略联盟对主要企业基准化分析

目录

第1章执行摘要

第2章前言

  • 概述
  • 利害关係人
  • 调查范围
  • 调查方法
    • 资料探勘
    • 资料分析
    • 资料检验
    • 研究途径
  • 调查来源
    • 主要调查来源
    • 二次调查来源
    • 先决条件

第3章市场趋势分析

  • 促进因素
  • 抑制因素
  • 机会
  • 威胁
  • 技术分析
  • 应用分析
  • 最终用户分析
  • 新兴市场
  • 新型冠状病毒感染疾病（COVID-19）的影响

第4章波特五力分析

  • 供应商的议价能力
  • 买方议价能力
  • 替代的威胁
  • 新进入者的威胁
  • 竞争公司之间的敌对关係

第5章：按组成部分

  • 软体
  • 服务

第6章：按治疗领域

  • 肿瘤学
  • 神经退化性疾病
  • 发炎的
  • 感染疾病
  • 代谢性疾病
  • 罕见疾病
  • 心血管疾病
  • 其他治疗领域

第7章：按技术分类

  • 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8章：按应用分类

  • 分子库筛选
  • 目标识别
  • 药物最佳化和再利用
  • 新药设计
  • 临床前测试

第9章：按最终用户分类

  • 製药和生物技术公司
  • 委外研发机构（CRO）
  • 学术研究
  • 其他最终用户

第10章：按地区

  • 北美洲
    • 美国
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 义大利
    • 法国
    • 西班牙
    • 其他欧洲国家
  • 亚太地区
    • 日本
    • 中国
    • 印度
    • 澳洲
    • 纽西兰
    • 韩国
    • 其他亚太地区
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地区
  • 中东和非洲
    • 沙乌地阿拉伯
    • 阿拉伯聯合大公国
    • 卡达
    • 南非
    • 其他中东和非洲

第11章进展

  • 合约、伙伴关係、协作和合资企业
  • 收购和合併
  • 新产品发布
  • 业务扩展
  • 其他关键策略

第12章公司简介

  • Cyclica
  • Deep Genomics
  • Euretos
  • Alphabet
  • Atomwise
  • Benevolent AI
  • Berg Health
  • BioSymetrics
  • Exscientia
  • Insilico Medicine
  • GNS Healthcare
  • IBM
  • Insitro
  • Microsoft
  • Neumora
  • Notable
  • Nvidia Corporation
  • PathAI
  • Recursion
Product Code: SMRC23968

According to Stratistics MRC, the Global Artificial Intelligence in Drug Discovery Market is accounted for $1.4 billion in 2023 and is expected to reach $9.8 billion by 2030 growing at a CAGR of 31.6% during the forecast period. Artificial intelligence (AI) in the drug discovery market is the application of AI and machine learning techniques to streamline and enhance the drug development process. It utilizes algorithms to analyze vast datasets, predict potential drug candidates, optimize clinical trial designs, and identify novel drug targets. AI accelerates drug discovery by reducing costs, improving the efficiency of research, and increasing the likelihood of identifying successful drug candidates.

According to the International Diabetes Federation (IDF) report, in 2021, approximately 537 million adults (20-79 years) are living with diabetes across the globe. The total number of people living with diabetes is projected to rise to 643 million by 2030 and 783 million by 2045.

Market Dynamics:

Driver:

Rising prevalence of chronic and infectious diseases

AI technologies offer unparalleled capabilities to analyze complex biological data, accelerating drug development processes. With the increasing burden of diseases like cancer, diabetes, and antibiotic-resistant infections, AI aids in the rapid identification of potential drug candidates, target proteins, and treatment strategies. This not only expedites drug discovery but also improves the chances of success in clinical trials, reducing development costs. Furthermore, AI-driven approaches enable the repurposing of existing drugs and facilitate the discovery of novel therapies, ultimately addressing the urgent global healthcare need for more effective treatments.

Restraint:

Lack of data sets in the field of drug discovery

AI heavily relies on vast and diverse data sources for accurate analysis and prediction, but acquiring such data, especially in healthcare, is often challenging due to issues related to privacy, data sharing, and data standardization. Limited access to relevant and well-annotated datasets hinders the training and validation of AI models, potentially leading to suboptimal results and missed opportunities for drug discovery. Addressing these data limitations is crucial for unlocking AI's full potential in accelerating drug development and improving healthcare outcomes.

Opportunity:

Rising prevalence of chronic and infectious diseases

AI-driven solutions are well-suited to address the growing global health crisis by expediting the development of innovative therapeutics. With chronic diseases like cancer and diabetes reaching epidemic proportions and the emergence of antibiotic-resistant infections, AI's data-driven analytics can efficiently identify potential drug candidates, uncover novel targets, and streamline clinical trial designs. By harnessing the power of AI, researchers can accelerate drug discovery processes, optimize personalized treatment strategies, and ultimately, usher in a new era of more effective and accessible therapies to combat the rising burden of these diseases on a global scale.

Threat:

Limited understanding and expertise

The effective application of AI requires interdisciplinary knowledge spanning biology, chemistry, data science, and AI technologies. The shortage of experts who can bridge these domains can hinder the development and deployment of AI-driven solutions for drug discovery. Moreover, misconceptions about the capabilities and limitations of AI may lead to unrealistic expectations. Inadequate understanding can also result in poorly designed experiments or misinterpretation of AI-generated insights, potentially wasting resources and delaying drug development efforts. To harness the full potential of AI, addressing these knowledge gaps and fostering collaboration among experts is essential.

COVID-19 Impact:

The COVID-19 pandemic has had a profound impact on the artificial intelligence in drug discovery market. On one hand, it accelerated the adoption of AI-driven approaches, as researchers urgently sought solutions for drug repurposing and vaccine development. AI played a critical role in identifying potential drug candidates and optimizing clinical trial designs, significantly shortening development timelines. However, the pandemic also disrupted research efforts, delayed clinical trials, and redirected resources, causing setbacks in AI-based drug discovery projects. Moreover, the increased demand for AI expertise and data resources strained the field's capacity, highlighting the need for infrastructure improvements and data sharing initiatives.

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

The oncology segment is expected to have lucrative growth. AI is revolutionizing oncology drug discovery by rapidly analyzing extensive genomic, proteomic, and clinical data. Machine learning algorithms identify unique genetic mutations, potential drug targets, and predict drug responses, facilitating the development of precision medicines tailored to individual cancer patients. Furthermore, AI enables the repurposing of existing drugs for novel oncology applications, reducing development costs and timelines. With the ever-growing cancer burden worldwide, AI-powered drug discovery offers unprecedented opportunities to uncover groundbreaking therapies, optimize treatment regimens, and improve patient outcomes in the challenging realm of oncology.

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

The preclinical testing segment is anticipated to witness the fastest CAGR growth during the forecast period. AI aids in the identification of potential drug candidates by analyzing vast datasets, predicting compound properties, and assessing their safety profiles. Through virtual screening and predictive modelling, AI accelerates the selection of lead compounds for further evaluation, reducing the time and cost associated with preclinical research. Additionally, AI-powered platforms assist in designing more targeted experiments, optimizing study protocols, and predicting potential toxicity issues early in drug development. This innovative approach enhances the efficiency and success rates of preclinical testing, ultimately expediting the delivery of safer and more effective drugs to market.

Region with largest share:

North America holds a significant share in the Artificial Intelligence in Drug Discovery Market, driven by its advanced healthcare infrastructure, strong research and development capabilities, and supportive regulatory environment. The region boasts a high adoption rate of IoT-enabled medical devices, including wearable health trackers and remote monitoring systems, as healthcare providers seek to improve patient care and outcomes. North America's investment in telemedicine and data-driven healthcare, along with its focus on patient-centric care models, positions it as a frontrunner in leveraging IoT technology to transform and enhance the delivery of healthcare services.

Region with highest CAGR:

Asia Pacific is projected to have the highest CAGR over the forecast period, fuelled by its expanding population, increasing healthcare needs, and growing adoption of digital technologies. With the support of government initiatives and a growing tech-savvy consumer base, Artificial Intelligence in Drug Discovery are rapidly gaining acceptance. In addition to improving patient care, they address challenges like remote patient monitoring in rural areas. Asia Pacific's vast market potential, coupled with its commitment to healthcare innovation, positions it as a significant player in the global Artificial Intelligence in Drug Discovery Market, fostering transformative advancements in healthcare delivery.

Key players in the market:

Some of the key players in Artificial Intelligence in Drug Discovery market include: Cyclica, Deep Genomics, Euretos, Alphabet, Atomwise, Benevolent AI, Berg Health, BioSymetrics, Exscientia, Insilico Medicine, GNS Healthcare, IBM, Insitro, Microsoft, Neumora, Notable, Nvidia Corporation, PathAI and Recursion.

Key Developments:

In November 2022, Exscientia collaborated with the University of Texas MD Anderson Cancer Center to use its patient-centric artificial intelligence technology for novel small molecule drug discovery and development using the expertise of MD Anderson. This strategy helped the company to expand and grow.

In August 2022, GNS Healthcare collaborated with Servier, a global pharmaceutical group to advance drug discovery, translational, and clinical development efforts in multiple myeloma (MM). This strategy helped the company to expand its service offering.

Components Covered:

  • Software
  • Services

Therapeutic Areas Covered:

  • Oncology
  • Neurodegenerative Diseases
  • Inflammatory
  • Infectious Diseases
  • Metabolic Diseases
  • Rare Diseases
  • Cardiovascular Diseases
  • Other Therapeutic Areas

Technologies Covered:

  • Machine Learning
  • Other Technologies

Applications Covered:

  • Molecular Library Screening
  • Target Identification
  • Drug Optimization and Repurposing
  • De novo Drug Designing
  • Preclinical Testing

End Users Covered:

  • Pharmaceutical and Biotechnology Companies
  • Contract Research Organization (CROs)
  • Academics & Research
  • 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 2021, 2022, 2023, 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 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 Artificial Intelligence in Drug Discovery Market, By Component

  • 5.1 Introduction
  • 5.2 Software
  • 5.3 Services

6 Global Artificial Intelligence in Drug Discovery Market, By Therapeutic Area

  • 6.1 Introduction
  • 6.2 Oncology
  • 6.3 Neurodegenerative Diseases
  • 6.4 Inflammatory
  • 6.5 Infectious Diseases
  • 6.6 Metabolic Diseases
  • 6.7 Rare Diseases
  • 6.8 Cardiovascular Diseases
  • 6.9 Other Therapeutic Areas

7 Global Artificial Intelligence in Drug Discovery Market, By Technology

  • 7.1 Introduction
  • 7.2 Machine Learning
    • 7.2.1 Deep Learning
    • 7.2.2 Supervised Learning
    • 7.2.3 Unsupervised Learning
    • 7.2.4 Other Machine Learning Technologies
  • 7.3 Other Technologies

8 Global Artificial Intelligence in Drug Discovery Market, By Application

  • 8.1 Introduction
  • 8.2 Molecular Library Screening
  • 8.3 Target Identification
  • 8.4 Drug Optimization and Repurposing
  • 8.5 De novo Drug Designing
  • 8.6 Preclinical Testing

9 Global Artificial Intelligence in Drug Discovery Market, By End User

  • 9.1 Introduction
  • 9.2 Pharmaceutical and Biotechnology Companies
  • 9.3 Contract Research Organization (CROs)
  • 9.4 Academics & Research
  • 9.5 Other End Users

10 Global Artificial Intelligence in 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 Cyclica
  • 12.2 Deep Genomics
  • 12.3 Euretos
  • 12.4 Alphabet
  • 12.5 Atomwise
  • 12.6 Benevolent AI
  • 12.7 Berg Health
  • 12.8 BioSymetrics
  • 12.9 Exscientia
  • 12.10 Insilico Medicine
  • 12.11 GNS Healthcare
  • 12.12 IBM
  • 12.13 Insitro
  • 12.14 Microsoft
  • 12.15 Neumora
  • 12.16 Notable
  • 12.17 Nvidia Corporation
  • 12.18 PathAI
  • 12.19 Recursion

List of Tables

  • Table 1 Global Artificial Intelligence in Drug Discovery Market Outlook, By Region (2021-2030) ($MN)
  • Table 2 Global Artificial Intelligence in Drug Discovery Market Outlook, By Component (2021-2030) ($MN)
  • Table 3 Global Artificial Intelligence in Drug Discovery Market Outlook, By Software (2021-2030) ($MN)
  • Table 4 Global Artificial Intelligence in Drug Discovery Market Outlook, By Services (2021-2030) ($MN)
  • Table 5 Global Artificial Intelligence in Drug Discovery Market Outlook, By Therapeutic Area (2021-2030) ($MN)
  • Table 6 Global Artificial Intelligence in Drug Discovery Market Outlook, By Oncology (2021-2030) ($MN)
  • Table 7 Global Artificial Intelligence in Drug Discovery Market Outlook, By Neurodegenerative Diseases (2021-2030) ($MN)
  • Table 8 Global Artificial Intelligence in Drug Discovery Market Outlook, By Inflammatory (2021-2030) ($MN)
  • Table 9 Global Artificial Intelligence in Drug Discovery Market Outlook, By Infectious Diseases (2021-2030) ($MN)
  • Table 10 Global Artificial Intelligence in Drug Discovery Market Outlook, By Metabolic Diseases (2021-2030) ($MN)
  • Table 11 Global Artificial Intelligence in Drug Discovery Market Outlook, By Rare Diseases (2021-2030) ($MN)
  • Table 12 Global Artificial Intelligence in Drug Discovery Market Outlook, By Cardiovascular Diseases (2021-2030) ($MN)
  • Table 13 Global Artificial Intelligence in Drug Discovery Market Outlook, By Other Therapeutic Areas (2021-2030) ($MN)
  • Table 14 Global Artificial Intelligence in Drug Discovery Market Outlook, By Technology (2021-2030) ($MN)
  • Table 15 Global Artificial Intelligence in Drug Discovery Market Outlook, By Machine Learning (2021-2030) ($MN)
  • Table 16 Global Artificial Intelligence in Drug Discovery Market Outlook, By Deep Learning (2021-2030) ($MN)
  • Table 17 Global Artificial Intelligence in Drug Discovery Market Outlook, By Supervised Learning (2021-2030) ($MN)
  • Table 18 Global Artificial Intelligence in Drug Discovery Market Outlook, By Unsupervised Learning (2021-2030) ($MN)
  • Table 19 Global Artificial Intelligence in Drug Discovery Market Outlook, By Other Machine Learning Technologies (2021-2030) ($MN)
  • Table 20 Global Artificial Intelligence in Drug Discovery Market Outlook, By Other Technologies (2021-2030) ($MN)
  • Table 21 Global Artificial Intelligence in Drug Discovery Market Outlook, By Application (2021-2030) ($MN)
  • Table 22 Global Artificial Intelligence in Drug Discovery Market Outlook, By Molecular Library Screening (2021-2030) ($MN)
  • Table 23 Global Artificial Intelligence in Drug Discovery Market Outlook, By Target Identification (2021-2030) ($MN)
  • Table 24 Global Artificial Intelligence in Drug Discovery Market Outlook, By Drug Optimization and Repurposing (2021-2030) ($MN)
  • Table 25 Global Artificial Intelligence in Drug Discovery Market Outlook, By De novo Drug Designing (2021-2030) ($MN)
  • Table 26 Global Artificial Intelligence in Drug Discovery Market Outlook, By Preclinical Testing (2021-2030) ($MN)
  • Table 27 Global Artificial Intelligence in Drug Discovery Market Outlook, By End User (2021-2030) ($MN)
  • Table 28 Global Artificial Intelligence in Drug Discovery Market Outlook, By Pharmaceutical and Biotechnology Companies (2021-2030) ($MN)
  • Table 29 Global Artificial Intelligence in Drug Discovery Market Outlook, By Contract Research Organization (CROs) (2021-2030) ($MN)
  • Table 30 Global Artificial Intelligence in Drug Discovery Market Outlook, By Academics & Research (2021-2030) ($MN)
  • Table 31 Global Artificial Intelligence in Drug Discovery Market Outlook, By Other End Users (2021-2030) ($MN)

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