한글목차

예측 독성학 인공지능(AI) 시장 규모는 2024년에 1억 7,000만 달러로 평가되었고, 2025-2031년의 예측 기간 중 CAGR29.6%로 성장하여 2031년까지 10억 8,300만 달러에 이를 것으로 예측됩니다.

인공지능(AI)은 최근 독성학계에 혁명을 가져온 가장 혁신적인 기술 중 하나입니다. 인공지능과 독성학의 융합으로 '예측 독성학'이라는 혁신적인 기법이 개발되었습니다. 이 방법은 컴퓨터 모델이 데이터 분석과 머신러닝을 활용하여 화학물질의 잠재적 유해성을 예측하는 방법입니다. 이러한 현대 기술의 융합은 위험 평가 프로세스를 가속화하고, 기존 실험 방법에 대한 의존도를 낮추며, 화학물질 노출과 생물학적 반응의 복잡한 연관성에 대한 이해를 심화시키는 데 있어 큰 잠재력을 가지고 있습니다.

최근 예측독성학에서 생체 내 연구에서 생체 외 연구로 전환하면서 머신러닝의 중요성이 커지고 있습니다. 현재는 in vitro 방법 외에도 정량적 구조-활성 상관관계(QSAR) 모델링, 약동학(ADME) 계산 등의 계산 기법이 병행되고 있습니다. 예측 독성학 분야에서는 사용 가능한 데이터의 특성상 서포트 벡터 머신(SVM), 랜덤 포레스트(RF), 결정 트리(DT)가 주요 머신러닝 기법으로 사용되고 있습니다.

자연어 처리(NLP)는 과학 문헌, 임상 보고, 부작용 데이터베이스에서 정보를 추출하는 측면에서 독성학에서 또 다른 중요한 AI 영역입니다. 최근 생물 의학 분야에서 NLP의 발전으로 수백만 건의 출판물에서 독성 관련 정보를 자동으로 추출할 수 있게 되면서 예측 모델에 사용할 수 있는 지식 기반이 크게 강화되었습니다.

예측 독성학용 AI 시장은 화학물질의 안전성 및 독성 평가에서 AI 기반 도구의 가능성에 대한 인식이 높아짐에 따라 꾸준히 성장하고 있습니다. 이러한 성장은 제약, 화장품, 화학, 농업 등 다양한 산업에서 사용되는 화학물질의 안전성을 평가하기 위한 보다 신속하고 정확한 방법에 대한 니즈에 의해 주도되었습니다. AI 알고리즘, 머신러닝 모델, 데이터 분석 기술의 발전이 예측 독성학의 혁신을 주도했습니다. 이러한 발전은 대규모 데이터 세트와 하이스루풋 스크리닝 기법을 활용하여 독성 예측의 정확성과 효율성을 높이는 것을 목표로 했습니다. AI 기반 예측 독성학 도구는 신약개발 프로세스 및 화학물질 안전성 평가의 효율화, 동물실험 의존도 감소 등의 기회가 존재했습니다. 또한, 환경독성 평가 및 규제 준수 평가의 적용 범위 확대도 성장 기회를 가져다주었습니다.

이 보고서는 예측 독성학 분야의 세계 AI 시장에 대해 총 매출액, 주요 기업의 시장 점유율 및 순위를 중심으로 지역별, 국가별, 유형별, 용도별 분석을 종합적으로 제시하는 것을 목적으로 합니다.

예측 독성학용 AI 시장 규모, 추정 및 예측은 2024년을 기준 연도로 하여 2020년부터 2031년까지의 과거 데이터와 예측 데이터를 포함하여 매출액으로 제공됩니다. 정량적, 정성적 분석을 통해 독자들이 예측 독성학용 AI 관련 비즈니스/성장 전략을 수립하고, 시장 경쟁을 평가하고, 현재 시장에서의 위치를 분석하고, 정보에 입각한 비즈니스 의사결정을 내릴 수 있도록 돕습니다.

시장 세분화

기업별

• BenevolentAI
• Exscientia
• Instem
• Dassault Systemes
• Evotec
• Simulations Plus
• Schrodinger
• Insilico Medicine
• Chemcopilot
• Inotiv

유형별 부문

• 머신러닝
• 자연언어처리
• 컴퓨터 비전

용도별 부문

• 제약 및 바이오테크놀러지
• 화학 및 화장품
• 기타

지역별

• 북미
  • 미국
  • 캐나다
• 아시아태평양
  • 중국
  • 일본
  • 한국
  • 동남아시아
  • 인도
  • 호주
  • 기타 아시아태평양
• 유럽
  • 독일
  • 프랑스
  • 영국
  • 이탈리아
  • 네덜란드
  • 북유럽 국가
  • 기타 유럽
• 라틴아메리카
  • 멕시코
  • 브라질
  • 기타 라틴아메리카
• 중동 및 아프리카
  • 튀르키예
  • 사우디아라비아
  • 아랍에미리트(UAE)
  • 기타 중동 및 아프리카

영문목차

The global market for AI In Predictive Toxicology was estimated to be worth US$ 170 million in 2024 and is forecast to a readjusted size of US$ 1083 million by 2031 with a CAGR of 29.6% during the forecast period 2025-2031.

Artificial Intelligence (AI) is one of the most innovative technologies that has revolutionized the world of toxicology in recent years. The intersection of artificial intelligence and toxicology has led to the development of a revolutionary method called Predictive Toxicology. In this method, computer models use data analytics and machine learning to predict the possible negative effects of chemicals. This convergence of modern technologies has enormous promise in accelerating the risk assessment process, reducing dependency on traditional experimental approaches, and expanding our understanding of the complicated link between chemical exposure and biological reactions.

In recent times, machine learning has become increasingly prominent in predictive toxicology as it has shifted from in vivo studies towards in silico studies. Currently, in vitro methods together with other computational methods such as QSAR modelling and ADME calculations are being used. In predictive toxicology SVMs, RF and DTs are the dominant machine learning methods due to the characteristics of the data available.

Natural Language Processing (NLP) represents another critical AI domain for toxicology, extracting information from scientific literature, clinical reports, and adverse event databases. Recent advancements in biomedical NLP have enabled the automated extraction of toxicity relationships from millions of publications, significantly enhancing the knowledge base available for predictive models.

The market for AI in predictive toxicology was steadily growing due to increased awareness of the potential of AI-driven tools in assessing chemical safety and toxicity. This growth was driven by the need for faster and more accurate methods to evaluate the safety of chemicals used in various industries, including pharmaceuticals, cosmetics, chemicals, and agriculture. Advancements in AI algorithms, machine learning models, and data analytics were driving innovation in predictive toxicology. These advancements aimed to enhance the accuracy and efficiency of toxicity predictions, leveraging large datasets and high-throughput screening methods. Opportunities existed for AI-driven predictive toxicology tools to streamline drug discovery, chemical safety assessments, and reduce the reliance on animal testing. Additionally, expanding applications in assessing environmental toxicity and regulatory compliance presented growth opportunities.

This report aims to provide a comprehensive presentation of the global market for AI In Predictive Toxicology, focusing on the total sales revenue, key companies market share and ranking, together with an analysis of AI In Predictive Toxicology by region & country, by Type, and by Application.

The AI In Predictive Toxicology market size, estimations, and forecasts are provided in terms of sales revenue ($ millions), considering 2024 as the base year, with history and forecast data for the period from 2020 to 2031. With both quantitative and qualitative analysis, to help readers develop business/growth strategies, assess the market competitive situation, analyze their position in the current marketplace, and make informed business decisions regarding AI In Predictive Toxicology.

Market Segmentation

By Company

• BenevolentAI
• Exscientia
• Instem
• Dassault Systemes
• Evotec
• Simulations Plus
• Schrodinger
• Insilico Medicine
• Chemcopilot
• Inotiv

Segment by Type

• Machine Learning
• Natural Language Processing
• Computer Vision

Segment by Application

• Pharmaceutical & Biotechnology
• Chemical & Cosmetics
• Others

By Region

• North America
  • United States
  • Canada
• Asia-Pacific
  • China
  • Japan
  • South Korea
  • Southeast Asia
  • India
  • Australia
  • Rest of Asia-Pacific
• Europe
  • Germany
  • France
  • U.K.
  • Italy
  • Netherlands
  • Nordic Countries
  • Rest of Europe
• Latin America
  • Mexico
  • Brazil
  • Rest of Latin America
• Middle East & Africa
  • Turkey
  • Saudi Arabia
  • UAE
  • Rest of MEA

Chapter Outline

Chapter 1: Introduces the report scope of the report, global total market size. This chapter also provides the market dynamics, latest developments of the market, the driving factors and restrictive factors of the market, the challenges and risks faced by manufacturers in the industry, and the analysis of relevant policies in the industry.

Chapter 2: Detailed analysis of AI In Predictive Toxicology company competitive landscape, revenue market share, latest development plan, merger, and acquisition information, etc.

Chapter 3: Provides the analysis of various market segments by Type, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different market segments.

Chapter 4: Provides the analysis of various market segments by Application, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different downstream markets.

Chapter 5: Revenue of AI In Predictive Toxicology in regional level. It provides a quantitative analysis of the market size and development potential of each region and introduces the market development, future development prospects, market space, and market size of each country in the world.

Chapter 6: Revenue of AI In Predictive Toxicology in country level. It provides sigmate data by Type, and by Application for each country/region.

Chapter 7: Provides profiles of key players, introducing the basic situation of the main companies in the market in detail, including product revenue, gross margin, product introduction, recent development, etc.

Chapter 8: Analysis of industrial chain, including the upstream and downstream of the industry.

Chapter 9: Conclusion.

Table of Contents

1 Market Overview

• 1.1 AI In Predictive Toxicology Product Introduction
• 1.2 Global AI In Predictive Toxicology Market Size Forecast (2020-2031)
• 1.3 AI In Predictive Toxicology Market Trends & Drivers
  • 1.3.1 AI In Predictive Toxicology Industry Trends
  • 1.3.2 AI In Predictive Toxicology Market Drivers & Opportunity
  • 1.3.3 AI In Predictive Toxicology Market Challenges
  • 1.3.4 AI In Predictive Toxicology Market Restraints
• 1.4 Assumptions and Limitations
• 1.5 Study Objectives
• 1.6 Years Considered

2 Competitive Analysis by Company

• 2.1 Global AI In Predictive Toxicology Players Revenue Ranking (2024)
• 2.2 Global AI In Predictive Toxicology Revenue by Company (2020-2025)
• 2.3 Key Companies AI In Predictive Toxicology Manufacturing Base Distribution and Headquarters
• 2.4 Key Companies AI In Predictive Toxicology Product Offered
• 2.5 Key Companies Time to Begin Mass Production of AI In Predictive Toxicology
• 2.6 AI In Predictive Toxicology Market Competitive Analysis
  • 2.6.1 AI In Predictive Toxicology Market Concentration Rate (2020-2025)
  • 2.6.2 Global 5 and 10 Largest Companies by AI In Predictive Toxicology Revenue in 2024
  • 2.6.3 Global Top Companies by Company Type (Tier 1, Tier 2, and Tier 3) & (based on the Revenue in AI In Predictive Toxicology as of 2024)
• 2.7 Mergers & Acquisitions, Expansion

3 Segmentation by Type

• 3.1 Introduction by Type
  • 3.1.1 Machine Learning
  • 3.1.2 Natural Language Processing
  • 3.1.3 Computer Vision
• 3.2 Global AI In Predictive Toxicology Sales Value by Type
  • 3.2.1 Global AI In Predictive Toxicology Sales Value by Type (2020 VS 2024 VS 2031)
  • 3.2.2 Global AI In Predictive Toxicology Sales Value, by Type (2020-2031)
  • 3.2.3 Global AI In Predictive Toxicology Sales Value, by Type (%) (2020-2031)

4 Segmentation by Application

• 4.1 Introduction by Application
  • 4.1.1 Pharmaceutical & Biotechnology
  • 4.1.2 Chemical & Cosmetics
  • 4.1.3 Others
• 4.2 Global AI In Predictive Toxicology Sales Value by Application
  • 4.2.1 Global AI In Predictive Toxicology Sales Value by Application (2020 VS 2024 VS 2031)
  • 4.2.2 Global AI In Predictive Toxicology Sales Value, by Application (2020-2031)
  • 4.2.3 Global AI In Predictive Toxicology Sales Value, by Application (%) (2020-2031)

5 Segmentation by Region

• 5.1 Global AI In Predictive Toxicology Sales Value by Region
  • 5.1.1 Global AI In Predictive Toxicology Sales Value by Region: 2020 VS 2024 VS 2031
  • 5.1.2 Global AI In Predictive Toxicology Sales Value by Region (2020-2025)
  • 5.1.3 Global AI In Predictive Toxicology Sales Value by Region (2026-2031)
  • 5.1.4 Global AI In Predictive Toxicology Sales Value by Region (%), (2020-2031)
• 5.2 North America
  • 5.2.1 North America AI In Predictive Toxicology Sales Value, 2020-2031
  • 5.2.2 North America AI In Predictive Toxicology Sales Value by Country (%), 2024 VS 2031
• 5.3 Europe
  • 5.3.1 Europe AI In Predictive Toxicology Sales Value, 2020-2031
  • 5.3.2 Europe AI In Predictive Toxicology Sales Value by Country (%), 2024 VS 2031
• 5.4 Asia Pacific
  • 5.4.1 Asia Pacific AI In Predictive Toxicology Sales Value, 2020-2031
  • 5.4.2 Asia Pacific AI In Predictive Toxicology Sales Value by Region (%), 2024 VS 2031
• 5.5 South America
  • 5.5.1 South America AI In Predictive Toxicology Sales Value, 2020-2031
  • 5.5.2 South America AI In Predictive Toxicology Sales Value by Country (%), 2024 VS 2031
• 5.6 Middle East & Africa
  • 5.6.1 Middle East & Africa AI In Predictive Toxicology Sales Value, 2020-2031
  • 5.6.2 Middle East & Africa AI In Predictive Toxicology Sales Value by Country (%), 2024 VS 2031

6 Segmentation by Key Countries/Regions

• 6.1 Key Countries/Regions AI In Predictive Toxicology Sales Value Growth Trends, 2020 VS 2024 VS 2031
• 6.2 Key Countries/Regions AI In Predictive Toxicology Sales Value, 2020-2031
• 6.3 United States
  • 6.3.1 United States AI In Predictive Toxicology Sales Value, 2020-2031
  • 6.3.2 United States AI In Predictive Toxicology Sales Value by Type (%), 2024 VS 2031
  • 6.3.3 United States AI In Predictive Toxicology Sales Value by Application, 2024 VS 2031
• 6.4 Europe
  • 6.4.1 Europe AI In Predictive Toxicology Sales Value, 2020-2031
  • 6.4.2 Europe AI In Predictive Toxicology Sales Value by Type (%), 2024 VS 2031
  • 6.4.3 Europe AI In Predictive Toxicology Sales Value by Application, 2024 VS 2031
• 6.5 China
  • 6.5.1 China AI In Predictive Toxicology Sales Value, 2020-2031
  • 6.5.2 China AI In Predictive Toxicology Sales Value by Type (%), 2024 VS 2031
  • 6.5.3 China AI In Predictive Toxicology Sales Value by Application, 2024 VS 2031
• 6.6 Japan
  • 6.6.1 Japan AI In Predictive Toxicology Sales Value, 2020-2031
  • 6.6.2 Japan AI In Predictive Toxicology Sales Value by Type (%), 2024 VS 2031
  • 6.6.3 Japan AI In Predictive Toxicology Sales Value by Application, 2024 VS 2031
• 6.7 South Korea
  • 6.7.1 South Korea AI In Predictive Toxicology Sales Value, 2020-2031
  • 6.7.2 South Korea AI In Predictive Toxicology Sales Value by Type (%), 2024 VS 2031
  • 6.7.3 South Korea AI In Predictive Toxicology Sales Value by Application, 2024 VS 2031
• 6.8 Southeast Asia
  • 6.8.1 Southeast Asia AI In Predictive Toxicology Sales Value, 2020-2031
  • 6.8.2 Southeast Asia AI In Predictive Toxicology Sales Value by Type (%), 2024 VS 2031
  • 6.8.3 Southeast Asia AI In Predictive Toxicology Sales Value by Application, 2024 VS 2031
• 6.9 India
  • 6.9.1 India AI In Predictive Toxicology Sales Value, 2020-2031
  • 6.9.2 India AI In Predictive Toxicology Sales Value by Type (%), 2024 VS 2031
  • 6.9.3 India AI In Predictive Toxicology Sales Value by Application, 2024 VS 2031

7 Company Profiles

• 7.1 BenevolentAI
  • 7.1.1 BenevolentAI Profile
  • 7.1.2 BenevolentAI Main Business
  • 7.1.3 BenevolentAI AI In Predictive Toxicology Products, Services and Solutions
  • 7.1.4 BenevolentAI AI In Predictive Toxicology Revenue (US$ Million) & (2020-2025)
  • 7.1.5 BenevolentAI Recent Developments
• 7.2 Exscientia
  • 7.2.1 Exscientia Profile
  • 7.2.2 Exscientia Main Business
  • 7.2.3 Exscientia AI In Predictive Toxicology Products, Services and Solutions
  • 7.2.4 Exscientia AI In Predictive Toxicology Revenue (US$ Million) & (2020-2025)
  • 7.2.5 Exscientia Recent Developments
• 7.3 Instem
  • 7.3.1 Instem Profile
  • 7.3.2 Instem Main Business
  • 7.3.3 Instem AI In Predictive Toxicology Products, Services and Solutions
  • 7.3.4 Instem AI In Predictive Toxicology Revenue (US$ Million) & (2020-2025)
  • 7.3.5 Instem Recent Developments
• 7.4 Dassault Systemes
  • 7.4.1 Dassault Systemes Profile
  • 7.4.2 Dassault Systemes Main Business
  • 7.4.3 Dassault Systemes AI In Predictive Toxicology Products, Services and Solutions
  • 7.4.4 Dassault Systemes AI In Predictive Toxicology Revenue (US$ Million) & (2020-2025)
  • 7.4.5 Dassault Systemes Recent Developments
• 7.5 Evotec
  • 7.5.1 Evotec Profile
  • 7.5.2 Evotec Main Business
  • 7.5.3 Evotec AI In Predictive Toxicology Products, Services and Solutions
  • 7.5.4 Evotec AI In Predictive Toxicology Revenue (US$ Million) & (2020-2025)
  • 7.5.5 Evotec Recent Developments
• 7.6 Simulations Plus
  • 7.6.1 Simulations Plus Profile
  • 7.6.2 Simulations Plus Main Business
  • 7.6.3 Simulations Plus AI In Predictive Toxicology Products, Services and Solutions
  • 7.6.4 Simulations Plus AI In Predictive Toxicology Revenue (US$ Million) & (2020-2025)
  • 7.6.5 Simulations Plus Recent Developments
• 7.7 Schrodinger
  • 7.7.1 Schrodinger Profile
  • 7.7.2 Schrodinger Main Business
  • 7.7.3 Schrodinger AI In Predictive Toxicology Products, Services and Solutions
  • 7.7.4 Schrodinger AI In Predictive Toxicology Revenue (US$ Million) & (2020-2025)
  • 7.7.5 Schrodinger Recent Developments
• 7.8 Insilico Medicine
  • 7.8.1 Insilico Medicine Profile
  • 7.8.2 Insilico Medicine Main Business
  • 7.8.3 Insilico Medicine AI In Predictive Toxicology Products, Services and Solutions
  • 7.8.4 Insilico Medicine AI In Predictive Toxicology Revenue (US$ Million) & (2020-2025)
  • 7.8.5 Insilico Medicine Recent Developments
• 7.9 Chemcopilot
  • 7.9.1 Chemcopilot Profile
  • 7.9.2 Chemcopilot Main Business
  • 7.9.3 Chemcopilot AI In Predictive Toxicology Products, Services and Solutions
  • 7.9.4 Chemcopilot AI In Predictive Toxicology Revenue (US$ Million) & (2020-2025)
  • 7.9.5 Chemcopilot Recent Developments
• 7.10 Inotiv
  • 7.10.1 Inotiv Profile
  • 7.10.2 Inotiv Main Business
  • 7.10.3 Inotiv AI In Predictive Toxicology Products, Services and Solutions
  • 7.10.4 Inotiv AI In Predictive Toxicology Revenue (US$ Million) & (2020-2025)
  • 7.10.5 Inotiv Recent Developments

8 Industry Chain Analysis

• 8.1 AI In Predictive Toxicology Industrial Chain
• 8.2 AI In Predictive Toxicology Upstream Analysis
  • 8.2.1 Key Raw Materials
  • 8.2.2 Raw Materials Key Suppliers
  • 8.2.3 Manufacturing Cost Structure
• 8.3 Midstream Analysis
• 8.4 Downstream Analysis (Customers Analysis)
• 8.5 Sales Model and Sales Channels
  • 8.5.1 AI In Predictive Toxicology Sales Model
  • 8.5.2 Sales Channel
  • 8.5.3 AI In Predictive Toxicology Distributors

9 Research Findings and Conclusion

10 Appendix

• 10.1 Research Methodology
  • 10.1.1 Methodology/Research Approach
    • 10.1.1.1 Research Programs/Design
    • 10.1.1.2 Market Size Estimation
    • 10.1.1.3 Market Breakdown and Data Triangulation
  • 10.1.2 Data Source
    • 10.1.2.1 Secondary Sources
    • 10.1.2.2 Primary Sources
• 10.2 Author Details
• 10.3 Disclaimer