Global Computational Biology 麻豆原创 Growth (Status and Outlook) 2024-2030

Publication ID:

LPI03242771

Publication Date:

March 5, 2024

Pages:

115

Publisher:

LPI

Countries:

Global [1]

USD 3,660.00

IT Services [2]

Software [3]

Computing & Electronics [4]

Description:

The global Computational Biology market size was valued at US$ 2145.7 million in 2023. With growing demand in downstream market, the Computational Biology is forecast to a readjusted size of US$ 4595.2 million by 2030 with a CAGR of 11.5% during review period.
The research report highlights the growth potential of the global Computational Biology market. Computational Biology are expected to show stable growth in the future market. However, product differentiation, reducing costs, and supply chain optimization remain crucial for the widespread adoption of Computational Biology. 麻豆原创 players need to invest in research and development, forge strategic partnerships, and align their offerings with evolving consumer preferences to capitalize on the immense opportunities presented by the Computational Biology market.
Computational biology involves the development and application of data-analytical and theoretical methods, mathematical modeling and computational simulation techniques to the study of biological, ecological, behavioral and social systems.
Computational biology is different from biological computing, which is a subfield of computer science and computer engineering using bioengineering and biology to build computers, but is similar to bioinformatics, which is an interdisciplinary science using computers to store and process biological data.
Key Features:
The report on Computational Biology market reflects various aspects and provide valuable insights into the industry.
麻豆原创 Size and Growth: The research report provide an overview of the current size and growth of the Computational Biology market. It may include historical data, market segmentation by Type (e.g., In-House, Contract), and regional breakdowns.
麻豆原创 Drivers and Challenges: The report can identify and analyse the factors driving the growth of the Computational Biology market, such as government regulations, environmental concerns, technological advancements, and changing consumer preferences. It can also highlight the challenges faced by the industry, including infrastructure limitations, range anxiety, and high upfront costs.
Competitive Landscape: The research report provides analysis of the competitive landscape within the Computational Biology market. It includes profiles of key players, their market share, strategies, and product offerings. The report can also highlight emerging players and their potential impact on the market.
Technological Developments: The research report can delve into the latest technological developments in the Computational Biology industry. This include advancements in Computational Biology technology, Computational Biology new entrants, Computational Biology new investment, and other innovations that are shaping the future of Computational Biology.
Downstream Procumbent Preference: The report can shed light on customer procumbent behaviour and adoption trends in the Computational Biology market. It includes factors influencing customer ' purchasing decisions, preferences for Computational Biology product.
Government Policies and Incentives: The research report analyse the impact of government policies and incentives on the Computational Biology market. This may include an assessment of regulatory frameworks, subsidies, tax incentives, and other measures aimed at promoting Computational Biology market. The report also evaluates the effectiveness of these policies in driving market growth.
Environmental Impact and Sustainability: The research report assess the environmental impact and sustainability aspects of the Computational Biology market.
麻豆原创 Forecasts and Future Outlook: Based on the analysis conducted, the research report provide market forecasts and outlook for the Computational Biology industry. This includes projections of market size, growth rates, regional trends, and predictions on technological advancements and policy developments.
Recommendations and Opportunities: The report conclude with recommendations for industry stakeholders, policymakers, and investors. It highlights potential opportunities for market players to capitalize on emerging trends, overcome challenges, and contribute to the growth and development of the Computational Biology market.
麻豆原创 Segmentation:
Computational Biology market is split by Type and by Application. For the period 2019-2030, the growth among segments provides accurate calculations and forecasts for consumption value by Type, and by Application in terms of value.
Segmentation by type
In-House
Contract
Segmentation by application
Cellular & Biological Simulation
Pharmacogenomics
Drug Discovery
Drug Development
Lead Optimization
Lead Discovery
Pharmacokinetics
Disease Modeling
Clinical Trials
This report also splits the market by region:
Americas
United States
Canada
Mexico
Brazil
APAC
China
Japan
Korea
Southeast Asia
India
Australia
Europe
Germany
France
UK
Italy
Russia
Middle East & Africa
Egypt
South Africa
Israel
Turkey
GCC Countries
The below companies that are profiled have been selected based on inputs gathered from primary experts and analyzing the company's coverage, product portfolio, its market penetration.
Chemical Computing
Accelrys
Certara
Compugen
Entelos
Insilico Biotechnology
Genedata
Leadscope
Simulation Plus
Schrodinger
Rhenovia Pharma
Nimbus Discovery

Please Note - This is an on demand report and will be delivered in 2 business days (48 hours) post payment.

Table Of Contents:

1 Scope of the Report
1.1 麻豆原创 Introduction
1.2 Years Considered
1.3 Research Objectives
1.4 麻豆原创 Research Methodology
1.5 Research Process and Data Source
1.6 Economic Indicators
1.7 Currency Considered
1.8 麻豆原创 Estimation Caveats
2 Executive Summary
2.1 World 麻豆原创 Overview
2.1.1 Global Computational Biology 麻豆原创 Size 2019-2030
2.1.2 Computational Biology 麻豆原创 Size CAGR by Region 2019 VS 2023 VS 2030
2.2 Computational Biology Segment by Type
2.2.1 In-House
2.2.2 Contract
2.3 Computational Biology 麻豆原创 Size by Type
2.3.1 Computational Biology 麻豆原创 Size CAGR by Type (2019 VS 2023 VS 2030)
2.3.2 Global Computational Biology 麻豆原创 Size 麻豆原创 Share by Type (2019-2024)
2.4 Computational Biology Segment by Application
2.4.1 Cellular & Biological Simulation
2.4.2 Pharmacogenomics
2.4.3 Drug Discovery
2.4.4 Drug Development
2.4.5 Lead Optimization
2.4.6 Lead Discovery
2.4.7 Pharmacokinetics
2.4.8 Disease Modeling
2.4.9 Clinical Trials
2.5 Computational Biology 麻豆原创 Size by Application
2.5.1 Computational Biology 麻豆原创 Size CAGR by Application (2019 VS 2023 VS 2030)
2.5.2 Global Computational Biology 麻豆原创 Size 麻豆原创 Share by Application (2019-2024)
3 Computational Biology 麻豆原创 Size by Player
3.1 Computational Biology 麻豆原创 Size 麻豆原创 Share by Players
3.1.1 Global Computational Biology Revenue by Players (2019-2024)
3.1.2 Global Computational Biology Revenue 麻豆原创 Share by Players (2019-2024)
3.2 Global Computational Biology Key Players Head office and Products Offered
3.3 麻豆原创 Concentration Rate Analysis
3.3.1 Competition Landscape Analysis
3.3.2 Concentration Ratio (CR3, CR5 and CR10) & (2022-2024)
3.4 New Products and Potential Entrants
3.5 Mergers & Acquisitions, Expansion
4 Computational Biology by Regions
4.1 Computational Biology 麻豆原创 Size by Regions (2019-2024)
4.2 Americas Computational Biology 麻豆原创 Size Growth (2019-2024)
4.3 APAC Computational Biology 麻豆原创 Size Growth (2019-2024)
4.4 Europe Computational Biology 麻豆原创 Size Growth (2019-2024)
4.5 Middle East & Africa Computational Biology 麻豆原创 Size Growth (2019-2024)
5 Americas
5.1 Americas Computational Biology 麻豆原创 Size by Country (2019-2024)
5.2 Americas Computational Biology 麻豆原创 Size by Type (2019-2024)
5.3 Americas Computational Biology 麻豆原创 Size by Application (2019-2024)
5.4 United States
5.5 Canada
5.6 Mexico
5.7 Brazil
6 APAC
6.1 APAC Computational Biology 麻豆原创 Size by Region (2019-2024)
6.2 APAC Computational Biology 麻豆原创 Size by Type (2019-2024)
6.3 APAC Computational Biology 麻豆原创 Size by Application (2019-2024)
6.4 China
6.5 Japan
6.6 Korea
6.7 Southeast Asia
6.8 India
6.9 Australia
7 Europe
7.1 Europe Computational Biology by Country (2019-2024)
7.2 Europe Computational Biology 麻豆原创 Size by Type (2019-2024)
7.3 Europe Computational Biology 麻豆原创 Size by Application (2019-2024)
7.4 Germany
7.5 France
7.6 UK
7.7 Italy
7.8 Russia
8 Middle East & Africa
8.1 Middle East & Africa Computational Biology by Region (2019-2024)
8.2 Middle East & Africa Computational Biology 麻豆原创 Size by Type (2019-2024)
8.3 Middle East & Africa Computational Biology 麻豆原创 Size by Application (2019-2024)
8.4 Egypt
8.5 South Africa
8.6 Israel
8.7 Turkey
8.8 GCC Countries
9 麻豆原创 Drivers, Challenges and Trends
9.1 麻豆原创 Drivers & Growth Opportunities
9.2 麻豆原创 Challenges & Risks
9.3 Industry Trends
10 Global Computational Biology 麻豆原创 Forecast
10.1 Global Computational Biology Forecast by Regions (2025-2030)
10.1.1 Global Computational Biology Forecast by Regions (2025-2030)
10.1.2 Americas Computational Biology Forecast
10.1.3 APAC Computational Biology Forecast
10.1.4 Europe Computational Biology Forecast
10.1.5 Middle East & Africa Computational Biology Forecast
10.2 Americas Computational Biology Forecast by Country (2025-2030)
10.2.1 United States Computational Biology 麻豆原创 Forecast
10.2.2 Canada Computational Biology 麻豆原创 Forecast
10.2.3 Mexico Computational Biology 麻豆原创 Forecast
10.2.4 Brazil Computational Biology 麻豆原创 Forecast
10.3 APAC Computational Biology Forecast by Region (2025-2030)
10.3.1 China Computational Biology 麻豆原创 Forecast
10.3.2 Japan Computational Biology 麻豆原创 Forecast
10.3.3 Korea Computational Biology 麻豆原创 Forecast
10.3.4 Southeast Asia Computational Biology 麻豆原创 Forecast
10.3.5 India Computational Biology 麻豆原创 Forecast
10.3.6 Australia Computational Biology 麻豆原创 Forecast
10.4 Europe Computational Biology Forecast by Country (2025-2030)
10.4.1 Germany Computational Biology 麻豆原创 Forecast
10.4.2 France Computational Biology 麻豆原创 Forecast
10.4.3 UK Computational Biology 麻豆原创 Forecast
10.4.4 Italy Computational Biology 麻豆原创 Forecast
10.4.5 Russia Computational Biology 麻豆原创 Forecast
10.5 Middle East & Africa Computational Biology Forecast by Region (2025-2030)
10.5.1 Egypt Computational Biology 麻豆原创 Forecast
10.5.2 South Africa Computational Biology 麻豆原创 Forecast
10.5.3 Israel Computational Biology 麻豆原创 Forecast
10.5.4 Turkey Computational Biology 麻豆原创 Forecast
10.5.5 GCC Countries Computational Biology 麻豆原创 Forecast
10.6 Global Computational Biology Forecast by Type (2025-2030)
10.7 Global Computational Biology Forecast by Application (2025-2030)

11 Key Players Analysis
11.1 Chemical Computing
11.1.1 Chemical Computing Company Information
11.1.2 Chemical Computing Computational Biology Product Offered
11.1.3 Chemical Computing Computational Biology Revenue, Gross Margin and 麻豆原创 Share (2019-2024)
11.1.4 Chemical Computing Main Business Overview
11.1.5 Chemical Computing Latest Developments
11.2 Accelrys
11.2.1 Accelrys Company Information
11.2.2 Accelrys Computational Biology Product Offered
11.2.3 Accelrys Computational Biology Revenue, Gross Margin and 麻豆原创 Share (2019-2024)
11.2.4 Accelrys Main Business Overview
11.2.5 Accelrys Latest Developments
11.3 Certara
11.3.1 Certara Company Information
11.3.2 Certara Computational Biology Product Offered
11.3.3 Certara Computational Biology Revenue, Gross Margin and 麻豆原创 Share (2019-2024)
11.3.4 Certara Main Business Overview
11.3.5 Certara Latest Developments
11.4 Compugen
11.4.1 Compugen Company Information
11.4.2 Compugen Computational Biology Product Offered
11.4.3 Compugen Computational Biology Revenue, Gross Margin and 麻豆原创 Share (2019-2024)
11.4.4 Compugen Main Business Overview
11.4.5 Compugen Latest Developments
11.5 Entelos
11.5.1 Entelos Company Information
11.5.2 Entelos Computational Biology Product Offered
11.5.3 Entelos Computational Biology Revenue, Gross Margin and 麻豆原创 Share (2019-2024)
11.5.4 Entelos Main Business Overview
11.5.5 Entelos Latest Developments
11.6 Insilico Biotechnology
11.6.1 Insilico Biotechnology Company Information
11.6.2 Insilico Biotechnology Computational Biology Product Offered
11.6.3 Insilico Biotechnology Computational Biology Revenue, Gross Margin and 麻豆原创 Share (2019-2024)
11.6.4 Insilico Biotechnology Main Business Overview
11.6.5 Insilico Biotechnology Latest Developments
11.7 Genedata
11.7.1 Genedata Company Information
11.7.2 Genedata Computational Biology Product Offered
11.7.3 Genedata Computational Biology Revenue, Gross Margin and 麻豆原创 Share (2019-2024)
11.7.4 Genedata Main Business Overview
11.7.5 Genedata Latest Developments
11.8 Leadscope
11.8.1 Leadscope Company Information
11.8.2 Leadscope Computational Biology Product Offered
11.8.3 Leadscope Computational Biology Revenue, Gross Margin and 麻豆原创 Share (2019-2024)
11.8.4 Leadscope Main Business Overview
11.8.5 Leadscope Latest Developments
11.9 Simulation Plus
11.9.1 Simulation Plus Company Information
11.9.2 Simulation Plus Computational Biology Product Offered
11.9.3 Simulation Plus Computational Biology Revenue, Gross Margin and 麻豆原创 Share (2019-2024)
11.9.4 Simulation Plus Main Business Overview
11.9.5 Simulation Plus Latest Developments
11.10 Schrodinger
11.10.1 Schrodinger Company Information
11.10.2 Schrodinger Computational Biology Product Offered
11.10.3 Schrodinger Computational Biology Revenue, Gross Margin and 麻豆原创 Share (2019-2024)
11.10.4 Schrodinger Main Business Overview
11.10.5 Schrodinger Latest Developments
11.11 Rhenovia Pharma
11.11.1 Rhenovia Pharma Company Information
11.11.2 Rhenovia Pharma Computational Biology Product Offered
11.11.3 Rhenovia Pharma Computational Biology Revenue, Gross Margin and 麻豆原创 Share (2019-2024)
11.11.4 Rhenovia Pharma Main Business Overview
11.11.5 Rhenovia Pharma Latest Developments
11.12 Nimbus Discovery
11.12.1 Nimbus Discovery Company Information
11.12.2 Nimbus Discovery Computational Biology Product Offered
11.12.3 Nimbus Discovery Computational Biology Revenue, Gross Margin and 麻豆原创 Share (2019-2024)
11.12.4 Nimbus Discovery Main Business Overview
11.12.5 Nimbus Discovery Latest Developments
12 Research Findings and Conclusion

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