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市場調查報告書
商品編碼
1371911

到 2030 年藥物研發市場中的人工智慧 (AI) 預測：按成分、治療領域、技術、應用、最終用戶和地區進行的全球分析

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個工作天內

價格

根據 Stratistics MRC 的數據，到 2023 年，藥物研發發現領域的人工智慧(AI) 全球市場將達到14 億美元，預計在預測期內年複合成長率為31.6%，到2030 年將達到98 億美元。 。

藥物研發中的人工智慧（AI）是應用人工智慧和機器學習技術來簡化和增強藥物開發過程。利用演算法分析大型資料、預測潛在的候選藥物、最佳化臨床試驗設計並識別新的藥物標靶。人工智慧透過降低成本、提高研究效率和增加識別成功候選藥物的可能性來加速藥物研發。

根據國際糖尿病聯盟（IDF）的報告，2021年全球約有5.37億成年人（20歲至79歲）患有糖尿病。預計2030年糖尿病患者總數將增加至6.43億，2045年將增加至7.83億。

慢性病和感染疾病的盛行率上升

人工智慧技術提供了無與倫比的分析複雜生物資料的能力，加速了藥物開發進程。隨著癌症、糖尿病和抗生素抗藥性感染疾病等疾病負擔的日益加重，人工智慧可以幫助快速識別潛在的候選藥物、標靶蛋白和治療策略。這不僅加速了藥物研發，還增加了臨床試驗成功的可能性並降低了開發成本。此外，人工智慧驅動的方法將能夠重新利用現有藥物，加速新治療方法的發現，並最終滿足世界對更有效治療方法的迫切需求。

藥物研發領域缺乏資料

人工智慧嚴重依賴大量且多樣化的資料來源來進行準確的分析和預測，但由於隱私、資料共用和資料標準化等問題，取得此類資料往往很困難，尤其是在醫療保健領域。對相關且註釋良好的資料的存取有限會阻礙人工智慧模型的訓練和檢驗，從而導致結果不佳並錯失藥物研發的機會。這是有可能的。解決這些資料限制對於釋放人工智慧的全部潛力、加速藥物研發發現和開發以及改善醫療保健結果至關重要。

慢性疾病和感染疾病增加

人工智慧主導的解決方案非常適合透過加速創新療法的開發來解決日益嚴重的全球健康危機。隨著癌症和糖尿病等慢性疾病變得越來越普遍，以及抗生素抗藥性感染疾病的出現，人工智慧資料主導的分析可以有效地識別潛在的候選藥物、發現新的目標並改善臨床結果，從而簡化您的研究設計。透過利用人工智慧的力量，研究人員可以加速藥物研發過程，最佳化個體化治療策略，並最終採取更多措施來應對這些疾病日益成長的全球負擔。我們可以開創有效且可及的治療方法的新時代。

理解和專業知識有限

人工智慧的有效應用需要涵蓋生物學、化學、資料科學和人工智慧技術的跨學科知識。缺乏能夠彌合這些領域的專家可能會阻礙人工智慧主導的藥物研發解決方案的開發和部署。此外，對人工智慧的能力和限制的誤解可能會導致不切實際的期望。理解不足也可能導致糟糕的實驗設計和對人工智慧生成見解的誤解，可能會浪費資源並減慢藥物研發工作。解決這些知識差距並促進專家之間的合作對於充分發揮人工智慧的潛力至關重要。

COVID-19 的影響：

COVID-19 的爆發對藥物研發的人工智慧 (AI) 市場產生了重大影響。一方面，隨著研究人員迫切尋求藥物再利用和疫苗開發的解決方案，人工智慧主導方法的採用加速。人工智慧在識別潛在候選藥物和最佳化臨床試驗設計方面發揮了關鍵作用，顯著縮短了開發時間。然而，疫情也擾亂了研究工作，推遲了臨床試驗，轉移了資源，並使基於人工智慧的藥物研發計畫遭受挫折。此外，對人工智慧專業知識和資料資源的需求不斷成長，導致該領域的能力緊張，並凸顯了基礎設施改進和資料共用舉措的必要性。

預計腫瘤學將成為預測期內最大的領域

腫瘤學領域預計將出現良好的成長。人工智慧透過快速分析大量基因組、蛋白質組和臨床資料，正在徹底改變腫瘤藥物研發。機器學習演算法透過識別獨特的基因突變、潛在的藥物標靶和預測藥物反應，促進針對個別癌症患者的精準藥物的開發。此外，人工智慧允許將現有藥物重新用於新的腫瘤學應用，從而降低開發成本和時間。隨著全球癌症罹患率持續上升，利用人工智慧進行藥物研發發現可以在充滿挑戰的癌症領域發現突破性治療方法、最佳化治療方法並改善患者的治療結果，這提供了前所未有的改善機會。

預計臨床前測試領域在預測期內年複合成長率最高

預計臨床前測試領域在預測期內將以最快的年複合成長率成長。人工智慧透過分析大量資料集、預測化合物特性和評估安全性來幫助識別潛在的候選藥物。透過虛擬篩選和預測建模，人工智慧加速了先導化合物的選擇以進行進一步評估，並減少了與臨床前研究相關的時間和成本。此外，人工智慧驅動的平台可以幫助設計更有針對性的實驗，最佳化測試方案，並在藥物開發的早期階段預測潛在的毒性問題。這種創新方法提高了臨床前測試的效率和成功率，最終促進更安全、更有效的藥物進入市場。

比最大的地區

由於其先進的醫療基礎設施、強大的研發能力和支援性的法規環境，北美在藥物研發發現市場的人工智慧中佔據了重要佔有率。隨著醫療保健提供者尋求改善患者照護和治療結果，該地區對物聯網醫療設備（例如穿戴式健康追蹤器和遠端監控系統）的採用率很高。透過對遠端醫療和資料主導的醫療保健的投資，以及對以患者為中心的護理模式的關注，北美將自己定位為利用物聯網技術轉變和增強醫療保健服務交付的領跑者。

複合年複合成長率最高的地區：

由於人口擴張、醫療保健需求不斷成長以及數位技術的日益採用，預計亞太地區在預測期內將出現最高的年複合成長率。在政府舉措和不斷成長的精通技術的消費者基礎的支持下，藥物研發中的人工智慧正在迅速獲得接受。除了改善患者照護之外，人工智慧還正在解決農村地區遠端患者監護等挑戰。亞太地區巨大的市場潛力和對醫療保健創新的承諾使該地區成為全球藥物研發發現人工智慧市場的關鍵參與者，推動醫療保健服務的變革性進步。

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目錄

第1章 執行摘要

第2章 前言

  • 概述
  • 利害關係人
  • 調查範圍
  • 調查方法
    • 資料探勘
    • 資料分析
    • 資料檢驗
    • 研究途徑
  • 調查來源
    • 主要調查來源
    • 二次調查來源
    • 先決條件

第3章 市場趨勢分析

  • 促進因素
  • 抑制因素
  • 機會
  • 威脅
  • 技術分析
  • 應用分析
  • 最終用戶分析
  • 新興市場
  • 新型冠狀病毒感染疾病（COVID-19）的影響

第4章 波特五力分析

  • 供應商的議價能力
  • 買方議價能力
  • 替代的威脅
  • 新進入者的威脅
  • 競爭公司之間的敵對關係

第5章 ：按組成部分

  • 軟體
  • 服務

第6章 ：按治療領域

  • 腫瘤學
  • 神經退化性疾病
  • 發炎的
  • 感染疾病
  • 代謝性疾病
  • 罕見疾病
  • 心血管疾病
  • 其他治療領域

第7章 ：按技術分類

  • 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8章 ：按應用分類

  • 分子庫篩選
  • 目標識別
  • 藥物最佳化和再利用
  • 新藥設計
  • 臨床前測試

第9章 ：按最終用戶分類

  • 製藥和生物技術公司
  • 委外研發機構（CRO）
  • 學術研究
  • 其他最終用戶

第10章 ：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
    • 南美洲其他地區
  • 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

第11章進展

  • 合約、夥伴關係、協作和合資企業
  • 收購和合併
  • 新產品發布
  • 業務擴展
  • 其他關鍵策略

第12章公司簡介

  • 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.