查看購物車
  • Simplified Chinese
  • English
  • Japanese
  • Korean
封面
市場調查報告書
商品編碼
1370948

藥物發現市場中的人工智慧 - 2018-2028 年全球產業規模、佔有率、趨勢、機會和預測，按組件類型、藥物類型、按應用類型、治療領域、地區和競爭細分

AI in Drug Discovery Market - Global Industry Size, Share, Trends, Opportunity, and Forecast, 2018-2028 Segmented By Component Type, By Drug Type, By Application Type, By Therapeutic Area, By Region and Competition
出版日期: | 出版商: TechSci Research | 英文 183 Pages | 商品交期: 2-3個工作天內

價格

We offer 8 hour analyst time for an additional research. Please contact us for the details.

簡介目錄

2022 年，全球人工智慧藥物發現市場價值為 7.5004 億美元，預計在整個預測期內將出現大幅成長，預計年複合成長率 (CAGR) 為 10.18%，預計到 2028 年將達到 13.2765 億美元。智慧（ AI）是電腦科學中的一門學科，專注於模擬智慧行為。它使電腦能夠模擬人類和動物的思維和任務執行，同時從錯誤中學習。人工智慧主要採用旨在以最小錯誤高效完成任務的演算法。透過利用深度學習和機器學習演算法，人工智慧應用個人化知識來執行各種任務。人工智慧在藥物發現中的應用具有巨大的意義，有助於疾病追蹤、促進治療方法的開發，甚至預測突變動物病毒的出現。人工智慧徹底改變了藥物發現的研究和開發，在慢性病治療方面取得了突破。

主要市場促進因素

減少藥物研究時間

市場概況
預測期 2024-2028
2022 年市場規模 75004萬美元
2028 年市場規模 132765萬美元
2023-2028 年年複合成長率 10.18%
成長最快的細分市場 腫瘤學
最大的市場 北美洲

加速藥物發現過程的推動刺激了藥物研究中對人工智慧（AI）的需求，從而推動了市場成長。傳統方法通常需要數年時間來最佳化用於人類評估的化合物，而人工智慧驅動的新創公司可能在幾天或幾個月內完成相同的任務。醫療保健預算的增加和醫療保健基礎設施的進步進一步促進了市場擴張。人工智慧與高效藥物活性探索的整合也推動了藥物開發領域的需求。人工智慧驅動的方法簡化了藥物發現階段，最大限度地減少了成本和耗時的失敗。人工智慧演算法能夠快速分析化合物庫、精確的候選物優先排序和準確的特性預測，最終加快有效的藥物開發。

科技巨頭與製藥公司的合作

微軟等科技巨頭與諾華等製藥公司之間的策略協議為人工智慧演算法融入製藥領域鋪平了道路。 Nvidia 與 Schrodinger 合作增強分子預測的預測能力等合作夥伴關係對藥物發現市場中的人工智慧產生了重大影響。像 Exscientia 這樣的新興企業專注於基於人工智慧的方法，吸引了大量投資。 Recursion Pharmaceuticals 等公司正在開發工具，利用人工智慧加速識別潛在候選藥物。此外，IBM、微軟和谷歌等 IT 公司正在投資製藥公司並與製藥公司合作，以推動人工智慧在藥物發現市場的進步。

慢性病增加

糖尿病、慢性阻塞性肺病、冠狀動脈疾病、關節炎、氣喘、肝炎和癌症等慢性疾病的盛行率在全球範圍內激增。這是由於老年人口不斷成長、生活方式不斷變化和城市化。國際糖尿病聯盟報告稱，2021 年全球將有5.37 億人受到糖尿病的影響。預計到2030 年，每年新增癌症病例約為6.43 億。例如，中國的肺癌病例佔亞太地區所有肺癌病例的50%以上。人工智慧正在透過患者資料整合改變個人化醫療，實現精準醫療保健並提高治療效果。它徹底改變了疾病的診斷、監測和治療，帶來更有效、更有針對性的治療介入。

技術進步

機器學習、深度學習、自然語言處理等人工智慧技術的進步，顯著增強了人工智慧分析複雜生物資料的能力。這些進步使得基因組學、蛋白質組學和臨床資料等不同資料源的整合成為可能，從而在藥物發現中獲得全面的見解和快速決策。生物資料（包括基因組序列、蛋白質結構和藥物與標靶相互作用）的指數成長為人工智慧驅動的分析和建模提供了充足的機會。大規模資料集使人工智慧演算法能夠識別模式、預測化合物特性並產生創新假設，從而在藥物發現中做出明智的數據驅動決策。

主要市場挑戰

數據品質和可用性

人工智慧很大程度上依賴高品質、多樣化、全面的資料來進行模型開發。在藥物發現中，資料隱私、智慧財產權和監管考慮是重大挑戰。獲得可靠、精心整理的資料集，尤其是代表不同患者群體和疾病類型的資料集，為人工智慧驅動的藥物發現帶來了障礙。解決人工智慧模型（尤其是深度學習模型）的不透明性所帶來的透明度問題至關重要。監管機構、臨床醫生和患者尋求透明的決策，因此可解釋性至關重要。驗證人工智慧模型並確保合規性提出了挑戰。人工智慧模型必須滿足嚴格的標準並表現出強大的性能才能獲得監管部門的批准。制定一個適應人工智慧在藥物發現中獨特考慮的監管框架對於廣泛採用至關重要。

技術挑戰

儘管人工智慧取得了重大進展，但資料品質仍然是使用人工智慧方法進行藥物開發的重大障礙。解決與資料所有權和保密性相關的挑戰勢在必行。持續的努力旨在最佳化藥物發現中的當前人工智慧技術。

主要市場趨勢

研發擴張

研究和開發活動的增加，加上基於雲端的服務的使用，推動了藥物發現市場中人工智慧的成長。新興經濟體和生物技術的進步進一步加速了市場的發展。 COVID-19 大流行顯著促進了人工智慧在藥物開發中的使用，特別是在識別和篩選用於治療 COVID-19 的現有藥物方面。人工智慧在識別各種疾病的活性物質方面的有效性促進了其在大流行期間的成長。

個人化醫療和精準醫療

人工智慧對包括遺傳和臨床資訊在內的患者資料的整合有可能徹底改變個人化醫療。它可以預測個體對治療的反應並最佳化治療策略，從而實現更有效的疾病診斷、監測和治療。

細分市場洞察

組件類型

就組件類型而言，服務預計將在 2022 年主導藥物發現市場的人工智慧，並在 2028 年之前呈現出最高的年複合成長率。服務的成長是由其優勢和最終用戶的強勁需求推動的。軟體也發揮著重要作用，新興公司專注於深度學習解決方案和生成模型，促進創新分子設計。

治療領域

由於人工智慧在發現癌症藥物方面的採用以及製藥公司和人工智慧提供者之間的合作，預計腫瘤學領域在預測期內將經歷最高的年複合成長率。

區域洞察

北美洲

由於人工智慧的高採用率、先進的醫療基礎設施以及人工智慧和藥物發現方面積極的臨床研究，北美將引領市場。值得注意的研究機構和關鍵進展進一步促進了該地區在人工智慧驅動的藥物發現方面的主導地位。

目錄

第 1 章：服務類型概述

第 2 章：研究方法

第 3 章：執行摘要

第 4 章：客戶之聲

第 5 章：全球人工智慧藥物發現市場展望

  • 市場規模及預測
    • 按價值
  • 市佔率及預測
    • 依組件類型（軟體和服務）
    • 依藥物類型（小分子和大分子）
    • 按應用類型（臨床前測試、藥物最佳化和再利用、標靶識別、候選篩選等）
    • 依治療領域（腫瘤、神經退化性疾病、心血管疾病、罕見疾病等）
    • 按地區
    • 按公司分類 (2022)
  • 市場地圖

第 6 章：北美藥物發現中的人工智慧市場展望

  • 市場規模及預測
    • 按價值
  • 市佔率及預測
    • 依組件類型
    • 依藥物類型
    • 按應用類型
    • 按治療領域
    • 按國家/地區
  • 北美：國家分析
    • 美國
    • 加拿大
    • 墨西哥

第 7 章：歐洲人工智慧在藥物發現市場前景

  • 市場規模及預測
    • 按價值
  • 市佔率及預測
    • 依組件類型
    • 依藥物類型
    • 按應用類型
    • 按治療領域
  • 歐洲：國家分析
    • 德國
    • 英國
    • 義大利
    • 法國
    • 西班牙

第 8 章：亞太地區人工智慧在藥物發現市場的展望

  • 市場規模及預測
    • 按價值
  • 市佔率及預測
    • 依組件類型
    • 依藥物類型
    • 按應用類型
    • 按治療領域
  • 亞太地區：國家分析
    • 中國
    • 印度
    • 日本
    • 韓國
    • 澳洲

第 9 章：南美洲人工智慧在藥物發現市場前景

  • 市場規模及預測
    • 按價值
  • 市佔率及預測
    • 依組件類型
    • 依藥物類型
    • 按應用類型
    • 按治療領域
  • 南美洲：國家分析
    • 巴西
    • 阿根廷
    • 哥倫比亞

第 10 章：中東和非洲人工智慧在藥物發現市場前景

  • 市場規模及預測
    • 按價值
  • 市佔率及預測
    • 依組件類型
    • 依藥物類型
    • 按應用類型
    • 按治療領域
  • MEA：國家分析
    • 南非 藥物發現中的人工智慧
    • 沙烏地阿拉伯 人工智慧在藥物發現的應用
    • 阿拉伯聯合大公國人工智慧在藥物發現的應用

第 11 章：市場動態

第 12 章：市場趨勢與發展

第 13 章：藥物發現市場中的全球人工智慧：SWOT 分析

第14章：競爭格局

  • 商業概覽
  • 服務產品
  • 最近的發展
  • 主要人員
  • SWOT分析
    • GNS Healthcare
    • BioSymetrics
    • BPGbio, Inc.
    • Atomwise Inc.
    • Owkin Inc.
    • NVIDIA Corporation
    • IBM Corporation
    • Microsoft Corporation
    • Aria Pharmaceuticals, Inc.
    • Insilico Medicine Inc.

第 15 章：策略建議

第 16 章：關於我們與免責聲明

簡介目錄
Product Code: 4622

The Global AI in Drug Discovery Market was valued at USD 750.04 Million in 2022 and is expected to experience substantial growth throughout the forecast period, projecting a Compound Annual Growth Rate (CAGR) of 10.18% and expected to reach USD 1327.65 Million through 2028. Artificial intelligence (AI), a discipline within computer science, is focused on emulating intelligent behavior. It empowers computers to simulate human and animal-like thinking and task execution, while learning from mistakes. AI predominantly employs algorithms designed for efficient task completion with minimal errors. By harnessing deep learning and machine learning algorithms, AI applies personalized knowledge to perform a wide array of tasks. The application of AI in drug discovery holds immense significance, contributing to disease tracking, facilitating the development of treatments, and even predicting the emergence of mutated animal viruses. AI has revolutionized research and development in drug discovery, leading to breakthroughs in treating chronic diseases.

Key Market Drivers

Reduced Time in Medication Research

Market Overview
Forecast Period2024-2028
Market Size 2022USD 750.04 Million
Market Size 2028USD 1327.65 Million
CAGR 2023-202810.18%
Fastest Growing SegmentOncology
Largest MarketNorth America

The drive to accelerate the drug discovery process has spurred demand for artificial intelligence (AI) in pharmaceutical research, consequently propelling market growth. Traditional methods often take years to optimize compounds for human evaluation, while AI-powered startups could potentially accomplish the same in a matter of days or months. Increased healthcare budgets and advancements in healthcare infrastructure further contribute to market expansion. The integration of AI for efficient drug activity exploration is also driving demand in the drug development sector. AI-driven approaches streamline drug discovery stages, minimizing costs and time-consuming failures. AI algorithms enable rapid analysis of compound libraries, precise candidate prioritization, and accurate property predictions, ultimately expediting effective drug development.

Collaboration between Tech Giants and Pharma

Strategic agreements between technology giants like Microsoft and pharmaceutical companies like Novartis have paved the way for AI algorithm integration into the pharmaceutical landscape. Partnerships such as Nvidia's collaboration with Schrodinger to enhance predictive capabilities in molecular forecasting have significantly influenced the AI in Drug Discovery Market. Emerging enterprises like Exscientia focus on AI-based methodologies, attracting substantial investments. Companies such as Recursion Pharmaceuticals are developing tools to accelerate the identification of potential drug candidates using AI. Moreover, IT firms like IBM, Microsoft, and Google are investing and partnering with pharmaceutical companies to propel the advancement of AI in Drug Discovery Market.

Rise in Chronic Diseases

The prevalence of chronic diseases like diabetes, COPD, coronary artery disease, arthritis, asthma, hepatitis, and cancer has surged globally. This is attributed to the growing geriatric population, evolving lifestyles, and urbanization. The International Diabetes Federation reports that diabetes affected 537 million individuals globally in 2021. Predictions estimate around 643 million new cancer cases annually by 2030. China, for instance, accounts for over 50% of all lung cancer cases in the Asia Pacific region. AI is transforming personalized medicine through patient data integration, enabling precision healthcare, and enhancing treatment outcomes. It revolutionizes disease diagnosis, monitoring, and treatment, leading to more effective and tailored therapeutic interventions.

Technological Advancements

Advancements in AI technologies such as machine learning, deep learning, and natural language processing have significantly enhanced AI's capabilities in analyzing complex biological data. These advancements enable the integration of diverse data sources, including genomics, proteomics, and clinical data, leading to comprehensive insights and rapid decision-making in drug discovery. The exponential growth of biological data, including genomic sequences, protein structures, and drug-target interactions, offers ample opportunities for AI-driven analysis and modeling. Large-scale datasets empower AI algorithms to identify patterns, predict compound properties, and generate innovative hypotheses, enabling informed and data-driven decisions in drug discovery.

Key Market Challenges

Data Quality and Availability

AI relies heavily on high-quality, diverse, and comprehensive data for model development. In drug discovery, data privacy, intellectual property, and regulatory considerations are significant challenges. Obtaining reliable, well-curated datasets, especially those representing diverse patient populations and disease types, poses obstacles for AI-driven drug discovery. Addressing transparency concerns due to the opacity of AI models, especially deep learning models, is crucial. Regulators, clinicians, and patients seek transparent decision-making, making interpretability essential. Validating AI models and ensuring regulatory compliance present challenges. AI models must meet stringent standards and demonstrate robust performance to gain regulatory approval. Developing a regulatory framework catering to AI's unique considerations in drug discovery is vital for widespread adoption.

Technical Challenges

Although AI has made significant progress, data quality remains a substantial obstacle in using AI methods for drug development. Addressing challenges related to data ownership and confidentiality is imperative. Ongoing efforts aim to optimize current AI technologies in drug discovery.

Key Market Trends

R&D Expansion

Increased research and development activities, coupled with the use of cloud-based services, fuel growth in the AI in Drug Discovery Market. Emerging economies and advancements in biotechnology further accelerate the market's development. The COVID-19 pandemic significantly boosted the use of AI in drug development, especially in identifying and screening existing drugs for COVID-19 treatment. AI's effectiveness in identifying active substances for various diseases contributed to its growth during the pandemic.

Personalized Medicine and Precision Healthcare

AI's integration of patient data, including genetic and clinical information, has the potential to revolutionize personalized medicine. It predicts individual responses to therapies and optimizes treatment strategies, leading to more effective disease diagnosis, monitoring, and treatment.

Segmental Insights

Component Types

In terms of component types, Services are expected to dominate the AI in Drug Discovery Market in 2022, exhibiting the highest CAGR until 2028. The growth of services is driven by their advantages and strong demand among end users. Software also plays a significant role, with emerging companies focusing on deep learning solutions and generative models, facilitating innovative molecule design.

Therapeutic Area

The oncology segment is projected to experience the highest CAGR during the forecast period due to AI's adoption in discovering cancer drugs and collaborations between pharmaceutical companies and AI providers.

Regional Insights

North America

North America is set to lead the market due to high AI adoption, advanced healthcare infrastructure, and active clinical research in AI and drug discovery. Noteworthy research institutions and key developments further contribute to the region's dominance in AI-driven drug discovery.

Key Market Players

  • GNS Healthcare
  • BioSymetrics
  • BPGbio, Inc.
  • Atomwise Inc.
  • Owkin Inc.
  • NVIDIA Corporation
  • IBM Corporation
  • Microsoft Corporation
  • Aria Pharmaceuticals, Inc.
  • Insilico Medicine Inc.

Report Scope:

In this report, the Global AI in Drug Discovery Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below.

AI in Drug Discovery Market, By Component Type:

  • Software
  • Services

AI in Drug Discovery Market, By Drug Type:

  • Small Molecule
  • Large Molecule

AI in Drug Discovery Market, By Application Type:

  • Preclinical Testing
  • Drug Optimization
  • Repurposing
  • Target Identification
  • Candidate Screening
  • Others

AI in Drug Discovery Market, By Therapeutic Area:

  • Oncology
  • Neurodegenerative Diseases
  • Cardiovascular Diseases
  • Rare Diseases
  • Others

AI in Drug Discovery Market, By Region:

  • North America
  • United States
  • Canada
  • Mexico
  • Europe
  • France
  • United Kingdom
  • Italy
  • Germany
  • Spain
  • Asia-Pacific
  • China
  • India
  • Japan
  • Australia
  • South Korea
  • South America
  • Brazil
  • Argentina
  • Colombia
  • Middle East & Africa
  • South Africa
  • Saudi Arabia
  • UAE
  • Kuwait
  • Turkey
  • Egypt

Competitive Landscape

  • Company Profiles: Detailed analysis of the major companies present in the Global AI in Drug Discovery Market.

Available Customizations:

  • Global AI in Drug Discovery market report with the given market data, Tech Sci Research offers customizations according to a company's specific needs. The following customization options are available for the report:

Company Information

  • Detailed analysis and profiling of additional market players (up to five).

Table of Contents

1. Service Type Overview

2. Research Methodology

3. Executive Summary

4. Voice of Customer

5. Global AI in Drug Discovery Market Outlook

  • 5.1. Market Size & Forecast
    • 5.1.1. By Value
  • 5.2. Market Share & Forecast
    • 5.2.1. By Component Type (Software and Services)
    • 5.2.2. By Drug Type (Small Molecule and Large Molecule)
    • 5.2.3. By Application Type (Preclinical Testing, Drug Optimization, and Repurposing, Target Identification, Candidate Screening, and Others)
    • 5.2.4. By Therapeutic Area (Oncology, Neurodegenerative Diseases, Cardiovascular Diseases, Rare Diseases, and Others)
    • 5.2.5. By Region
    • 5.2.6. By Company (2022)
  • 5.3. Market Map

6. North America AI in Drug Discovery Market Outlook

  • 6.1. Market Size & Forecast
    • 6.1.1. By Value
  • 6.2. Market Share & Forecast
    • 6.2.1. By Component Type
    • 6.2.2. By Drug Type
    • 6.2.3. By Application Type
    • 6.2.4. By Therapeutic Area
    • 6.2.5. By Country
  • 6.3. North America: Country Analysis
    • 6.3.1. United States AI in Drug Discovery Market Outlook
      • 6.3.1.1. Market Size & Forecast
        • 6.3.1.1.1. By Value
      • 6.3.1.2. Market Share & Forecast
        • 6.3.1.2.1. By Component Type
        • 6.3.1.2.2. By Drug Type
        • 6.3.1.2.3. By Application Type
        • 6.3.1.2.4. By Therapeutic Area
    • 6.3.2. Canada AI in Drug Discovery Market Outlook
      • 6.3.2.1. Market Size & Forecast
        • 6.3.2.1.1. By Value
      • 6.3.2.2. Market Share & Forecast
        • 6.3.2.2.1. By Component Type
        • 6.3.2.2.2. By Drug Type
        • 6.3.2.2.3. By Application Type
        • 6.3.2.2.4. By Therapeutic Area
    • 6.3.3. Mexico AI in Drug Discovery Market Outlook
      • 6.3.3.1. Market Size & Forecast
        • 6.3.3.1.1. By Value
      • 6.3.3.2. Market Share & Forecast
        • 6.3.3.2.1. By Component Type
        • 6.3.3.2.2. By Drug Type
        • 6.3.3.2.3. By Application Type
        • 6.3.3.2.4. By Therapeutic Area

7. Europe AI in Drug Discovery Market Outlook

  • 7.1. Market Size & Forecast
    • 7.1.1. By Value
  • 7.2. Market Share & Forecast
    • 7.2.1. By Component Type
    • 7.2.2. By Drug Type
    • 7.2.3. By Application Type
    • 7.2.4. By Therapeutic Area
  • 7.3. Europe: Country Analysis
    • 7.3.1. Germany AI in Drug Discovery Market Outlook
      • 7.3.1.1. Market Size & Forecast
        • 7.3.1.1.1. By Value
      • 7.3.1.2. Market Share & Forecast
        • 7.3.1.2.1. By Component Type
        • 7.3.1.2.2. By Drug Type
        • 7.3.1.2.3. By Application Type
        • 7.3.1.2.4. By Therapeutic Area
    • 7.3.2. United Kingdom AI in Drug Discovery Market Outlook
      • 7.3.2.1. Market Size & Forecast
        • 7.3.2.1.1. By Value
      • 7.3.2.2. Market Share & Forecast
        • 7.3.2.2.1. By Component Type
        • 7.3.2.2.2. By Drug Type
        • 7.3.2.2.3. By Application Type
        • 7.3.2.2.4. By Therapeutic Area
    • 7.3.3. Italy AI in Drug Discovery Market Outlook
      • 7.3.3.1. Market Size & Forecast
        • 7.3.3.1.1. By Value
      • 7.3.3.2. Market Share & Forecasty
        • 7.3.3.2.1. By Component Type
        • 7.3.3.2.2. By Drug Type
        • 7.3.3.2.3. By Application Type
        • 7.3.3.2.4. By Therapeutic Area
    • 7.3.4. France AI in Drug Discovery Market Outlook
      • 7.3.4.1. Market Size & Forecast
        • 7.3.4.1.1. By Value
      • 7.3.4.2. Market Share & Forecast
        • 7.3.4.2.1. By Component Type
        • 7.3.4.2.2. By Drug Type
        • 7.3.4.2.3. By Application Type
        • 7.3.4.2.4. By Therapeutic Area
    • 7.3.5. Spain AI in Drug Discovery Market Outlook
      • 7.3.5.1. Market Size & Forecast
        • 7.3.5.1.1. By Value
      • 7.3.5.2. Market Share & Forecast
        • 7.3.5.2.1. By Component Type
        • 7.3.5.2.2. By Drug Type
        • 7.3.5.2.3. By Application Type
        • 7.3.5.2.4. By Therapeutic Area

8. Asia-Pacific AI in Drug Discovery Market Outlook

  • 8.1. Market Size & Forecast
    • 8.1.1. By Value
  • 8.2. Market Share & Forecast
    • 8.2.1. By Component Type
    • 8.2.2. By Drug Type
    • 8.2.3. By Application Type
    • 8.2.4. By Therapeutic Area
  • 8.3. Asia-Pacific: Country Analysis
    • 8.3.1. China AI in Drug Discovery Market Outlook
      • 8.3.1.1. Market Size & Forecast
        • 8.3.1.1.1. By Value
      • 8.3.1.2. Market Share & Forecast
        • 8.3.1.2.1. By Component Type
        • 8.3.1.2.2. By Drug Type
        • 8.3.1.2.3. By Application Type
        • 8.3.1.2.4. By Therapeutic Area
    • 8.3.2. India AI in Drug Discovery Market Outlook
      • 8.3.2.1. Market Size & Forecast
        • 8.3.2.1.1. By Value
      • 8.3.2.2. Market Share & Forecast
        • 8.3.2.2.1. By Component Type
        • 8.3.2.2.2. By Drug Type
        • 8.3.2.2.3. By Application Type
        • 8.3.2.2.4. By Therapeutic Area
    • 8.3.3. Japan AI in Drug Discovery Market Outlook
      • 8.3.3.1. Market Size & Forecast
        • 8.3.3.1.1. By Value
      • 8.3.3.2. Market Share & Forecast
        • 8.3.3.2.1. By Component Type
        • 8.3.3.2.2. By Drug Type
        • 8.3.3.2.3. By Application Type
        • 8.3.3.2.4. By Therapeutic Area
    • 8.3.4. South Korea AI in Drug Discovery Market Outlook
      • 8.3.4.1. Market Size & Forecast
        • 8.3.4.1.1. By Value
      • 8.3.4.2. Market Share & Forecast
        • 8.3.4.2.1. By Component Type
        • 8.3.4.2.2. By Drug Type
        • 8.3.4.2.3. By Application Type
        • 8.3.4.2.4. By Therapeutic Area
    • 8.3.5. Australia AI in Drug Discovery Market Outlook
      • 8.3.5.1. Market Size & Forecast
        • 8.3.5.1.1. By Value
      • 8.3.5.2. Market Share & Forecast
        • 8.3.5.2.1. By Component Type
        • 8.3.5.2.2. By Drug Type
        • 8.3.5.2.3. By Application Type
        • 8.3.5.2.4. By Therapeutic Area

9. South America AI in Drug Discovery Market Outlook

  • 9.1. Market Size & Forecast
    • 9.1.1. By Value
  • 9.2. Market Share & Forecast
    • 9.2.1. By Component Type
    • 9.2.2. By Drug Type
    • 9.2.3. By Application Type
    • 9.2.4. By Therapeutic Area
  • 9.3. South America: Country Analysis
    • 9.3.1. Brazil AI in Drug Discovery Market Outlook
      • 9.3.1.1. Market Size & Forecast
        • 9.3.1.1.1. By Value
      • 9.3.1.2. Market Share & Forecast
        • 9.3.1.2.1. By Component Type
        • 9.3.1.2.2. By Drug Type
        • 9.3.1.2.3. By Application Type
        • 9.3.1.2.4. By Therapeutic Area
    • 9.3.2. Argentina AI in Drug Discovery Market Outlook
      • 9.3.2.1. Market Size & Forecast
        • 9.3.2.1.1. By Value
      • 9.3.2.2. Market Share & Forecast
        • 9.3.2.2.1. By Component Type
        • 9.3.2.2.2. By Drug Type
        • 9.3.2.2.3. By Application Type
        • 9.3.2.2.4. By Therapeutic Area
    • 9.3.3. Colombia AI in Drug Discovery Market Outlook
      • 9.3.3.1. Market Size & Forecast
        • 9.3.3.1.1. By Value
      • 9.3.3.2. Market Share & Forecast
        • 9.3.3.2.1. By Component Type
        • 9.3.3.2.2. By Drug Type
        • 9.3.3.2.3. By Application Type
        • 9.3.3.2.4. By Therapeutic Area

10. Middle East and Africa AI in Drug Discovery Market Outlook

  • 10.1. Market Size & Forecast
    • 10.1.1. By Value
  • 10.2. Market Share & Forecast
    • 10.2.1. By Component Type
    • 10.2.2. By Drug Type
    • 10.2.3. By Application Type
    • 10.2.4. By Therapeutic Area
  • 10.3. MEA: Country Analysis
    • 10.3.1. South Africa AI in Drug Discovery Market Outlook
      • 10.3.1.1. Market Size & Forecast
        • 10.3.1.1.1. By Value
      • 10.3.1.2. Market Share & Forecast
        • 10.3.1.2.1. By Component Type
        • 10.3.1.2.2. By Drug Type
        • 10.3.1.2.3. By Application Type
        • 10.3.1.2.4. By Therapeutic Area
    • 10.3.2. Saudi Arabia AI in Drug Discovery Market Outlook
      • 10.3.2.1. Market Size & Forecast
        • 10.3.2.1.1. By Value
      • 10.3.2.2. Market Share & Forecast
        • 10.3.2.2.1. By Component Type
        • 10.3.2.2.2. By Drug Type
        • 10.3.2.2.3. By Application Type
        • 10.3.2.2.4. By Therapeutic Area
    • 10.3.3. UAE AI in Drug Discovery Market Outlook
      • 10.3.3.1. Market Size & Forecast
        • 10.3.3.1.1. By Value
      • 10.3.3.2. Market Share & Forecast
        • 10.3.3.2.1. By Component Type
        • 10.3.3.2.2. By Drug Type
        • 10.3.3.2.3. By Application Type
        • 10.3.3.2.4. By Therapeutic Area

11. Market Dynamics

12. Market Trends & Developments

13. Global AI in Drug Discovery Market: SWOT Analysis

14. Competitive Landscape

  • 14.1. Business Overview
  • 14.2. Services Offerings
  • 14.3. Recent Developments
  • 14.4. Key Personnel
  • 14.5. SWOT Analysis
    • 14.5.1. GNS Healthcare
    • 14.5.2. BioSymetrics
    • 14.5.3. BPGbio, Inc.
    • 14.5.4. Atomwise Inc.
    • 14.5.5. Owkin Inc.
    • 14.5.6. NVIDIA Corporation
    • 14.5.7. IBM Corporation
    • 14.5.8. Microsoft Corporation
    • 14.5.9. Aria Pharmaceuticals, Inc.
    • 14.5.10. Insilico Medicine Inc.

15. Strategic Recommendations

16. About Us & Disclaimer