PUBLISHER: QYResearch | PRODUCT CODE: 1862419
PUBLISHER: QYResearch | PRODUCT CODE: 1862419
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
Segment by Type
Segment by Application
By Region
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.