Computational Biology Market
Computational Biology Market Size, Share & Industry Analysis, By Application (Cellular & Biological Simulation, Drug Discovery & Disease Modeling, Preclinical Drug Development, Clinical Trials, Human Body Simulation), By Service Type (In-House, Contract), By End User (Academics, Industry, Commercial), By Region (North America, Europe, Asia-Pacific, Latin America, Middle East & Africa) - Share, Size, Outlook, and Opportunity Analysis, 2024-2031.
Market Overview
The Computational Biology Market is projected to grow at a CAGR of 21.5% from 2024 to 2031. The market value is expected to rise from USD XX billion in 2024 to USD YY billion by 2031. North America currently dominates the market, accounting for the largest share of global revenue. Key metrics include the number of research publications, patent filings, and collaborations between academia and industry.
Advances in big data analytics, machine learning, and artificial intelligence applications in life sciences are driving the industry's rapid growth. Growing demand for personalised medicine and increased drug discovery efficiency are important drivers of market expansion.
Computational Biology Market Dynamics
Market Trend: AI and machine learning revolutionize drug discovery process
The combination of artificial intelligence (AI) and machine learning (ML) with computational biology is transforming the drug development landscape. These tools enable researchers to examine huge amounts of biological data, predict protein structures, and identify potential medicine options with remarkable speed and precision. The introduction of AI-powered platforms has significantly reduced the time and cost associated with early-stage drug discovery, allowing pharmaceutical companies to accelerate their research pipelines.
For example, recent developments in deep learning algorithms have improved the accuracy of protein structure prediction, which is a crucial step towards understanding illness causes and generating tailored therapies. This trend has led to increased investment in AI-powered computational biology tools and platforms, with many biotech and pharmaceutical companies establishing dedicated AI research sections or cooperating with technology firms to use these capabilities.
Market Driver: Rising prevalence of chronic diseases and demand for personalized medicine
The rising global burden of chronic diseases, such as cancer, cardiovascular disease, and neurological disorders, is driving the demand for more effective and tailored treatments. Computational biology is essential for processing complex genetic and molecular data in order to identify disease biomarkers and develop personalised therapies.
According to the World Health Organisation, chronic diseases account for 71% of all deaths worldwide, and this figure is expected to rise in the coming years. This has resulted in an increase in research funding and initiatives focused on understanding disease pathways at the molecular level. For example, the National Institutes of Health (NIH) has pledged more than $41 billion for biomedical research in 2020, with a significant portion earmarked for computational biology and bioinformatics programs.
The demand for customised medicine has accelerated the adoption of computational biology technology. These tools enable researchers to evaluate individual patient data, including genomic information, in order to change treatment regimens and predict drug results. The global personalised medicine industry is predicted to grow to $3.18 trillion by 2025, creating enormous prospects for computational biology applications in this field.
Market Restraint: Lack of standardization and data integration challenges
Despite fast advancements in computational biology, the field faces challenges such as data standardisation and integration. The lack of standardised data formats and standards among research institutes and platforms makes it difficult to interchange and analyse data effectively.
Approximately 30% of life sciences firms report problems integrating data from various sources, which delays research programs and raises costs. This issue is especially significant in multi-omics studies, when data from many molecular levels (genomics, proteomics, and metabolomics) must be combined for complete analysis.
Segment Overview
Drug Discovery & Disease Modeling segment dominates the Computational Biology market:
The Drug Discovery & Disease Modelling category currently holds the largest market share in the Computational Biology industry. This dominance stems primarily from the growing pressure on pharmaceutical companies to reduce medicine development timetables and costs while increasing success rates. This section's computational tools enable researchers to rapidly screen vast chemical libraries, predict drug-target interactions, and model disease processes.
Recent breakthroughs in structure-based drug design have hastened the adoption of computational biology tools in early-stage drug development. For example, molecular docking simulations have become widely used for identifying potential pharmaceutical candidates, reducing the requirement for extensive wet-lab experiments. A study published in the Journal of Chemical Information and Modelling discovered that computational methods could estimate drug-target binding affinities with up to 85% accuracy, proving the applicability of these approaches.
Furthermore, the use of in silico clinical trials revolutionised the medicine development process. These computational models allow researchers to simulate medication effects on virtual patient populations, which improves clinical trial designs and reduces the risk of late-stage failures. According to a Tufts Centre for the Study of Drug Development study, utilising in silico modelling in clinical trials has the potential to reduce development costs by up to 25% while cutting timelines by two years.
Major pharmaceutical companies have made significant investments in computational biology capabilities. For example, Pfizer has established a dedicated Centre for Therapeutic Innovation, which primarily depends on computational methods for target identification and validation. Similarly, AstraZeneca and BenevolentAI worked to apply artificial intelligence in drug discovery, resulting in the discovery of new targets for chronic kidney disease and idiopathic pulmonary fibrosis.
Regional Outlook
North America leads the Computational Biology market:
North America currently dominates the global Computational Biology market, generating the vast bulk of revenue. This prominent position is attributable to a number of factors, including the presence of large pharmaceutical and biotechnology companies, major research funding, and a robust academic research environment.
The region's supremacy is supported by significant funding from government agencies and commercial entities. The National Institutes of Health (NIH) in the United States has gradually boosted funding for computational biology research, allocating more than $1.2 billion annually to bioinformatics and computational biology efforts. This enormous investment has fostered innovation and collaboration between academia and industry, resulting in increased market expansion.
Recent improvements in the North American market include the advent of various large-scale genomics initiatives. The National Institutes of Health's All of Us study Program plans to collect genetic and health data from one million people in the United States, creating a large resource for computer study. This effort has already enlisted more than 300,000 people and is expected to result in significant advancements in precision medicine.
Collaborations between technological heavyweights and life sciences companies have also increased in the region. For example, Google's DeepMind has collaborated with several academic institutions to apply its AI algorithms to protein folding prediction, a critical area of computational biology. The AlphaFold system's capacity to predict protein structures with near-experimental accuracy has been hailed as a significant scientific breakthrough.
In Canada, the government has launched the Digital Health and Discovery Platform, a $49 million initiative to create a nationwide, AI-powered health data platform. The goal of this study is to use computational biology methods to accelerate medication discovery and development.
Competitive Intelligence
The computational biology sector is marked by severe competition and rapid technological advancements. To maintain their market positions, important industry players prioritise the creation of breakthrough software solutions, expanding their service offerings, and forming strategic alliances.
Dassault Systèmes, Genedata AG, Insilico Medicine, Schrödinger, Inc., and Certara are among the industry's leading companies. These companies have established themselves as market leaders by employing proprietary algorithms, massive databases, and strong collaborations with the pharmaceutical and biotechnology sectors.
Dassault Systèmes, for example, used its 3D modelling and simulation skills to develop the BIOVIA brand, which offers a comprehensive suite of computational tools for life sciences research. The company's market share has steadily increased, and life sciences revenue is predicted to grow by 13% in 2020.
Schrödinger, Inc. has had substantial growth in recent years, with revenue predicted to rise 26% to $108 million in 2020. The company's success can be attributed to its physics-based computational platform for drug discovery, which has been adopted by major pharmaceutical corporations throughout the world.
Insilico Medicine has emerged as a significant player in the AI-powered drug discovery area. The company just secured $255 million in Series C funding, demonstrating investors' strong interest in computational biology companies. Insilico's end-to-end AI platform has demonstrated the ability to rapidly discover, synthesise, and analyse novel drug candidates, positioning the company as a potential disruptor in the pharmaceutical industry.
Mergers and acquisitions have also played a significant role in shaping the competitive landscape. For example, Certara's $310 million acquisition of Pinnacle 21 in 2021 has strengthened its position in the regulatory submission and data standardisation industries, complementing its existing computational biology offerings.
Looking ahead, the competitive landscape is likely to shift as IT behemoths and specialised AI companies enter the computational biology space. This trend is projected to drive greater innovation and, potentially, industry consolidation as businesses strive to build comprehensive platforms that cover the whole drug discovery and development process.
Analyst Opinion
The combination of big data, artificial intelligence, and advances in biological understanding is likely to drive exponential growth in the Computational Biology market in the coming years. The discipline is rapidly transitioning from a supporting role in biosciences research to a vital component in medication development and personalised medicine.
One of the most exciting developments is the rise of "digital twins" in healthcare. These personalised patient computational models, which incorporate genetic, physiological, and environmental data, have the potential to revolutionise treatment planning and pharmaceutical development. Early adopters of this technology have already reported significant results in areas such as cardiovascular disease management and cancer treatment optimisation.
Another issue to keep an eye on is how quantum computing is incorporated into computational biology. While still in their early stages, quantum algorithms show promise in handling complex problems such as protein folding and molecular dynamics simulations with unprecedented speed and accuracy. As quantum hardware becomes more widely available, we should expect to see new advancements in drug discovery and molecular modelling.
The increased emphasis on multi-omics techniques, which incorporate data from genomics, proteomics, metabolomics, and other -omics disciplines, will drive up demand for powerful computational tools. This broad approach to understanding biological systems will be essential for deciphering complex disease mechanisms and identifying new therapy targets.
However, the discipline faces some challenges, particularly in data standardisation, reproducibility, and ethical issues concerning the use of AI in healthcare. Getting these issues resolved is critical to the long-term viability and public acceptance of computational biology techniques.
Overall, the computational biology market is reaching a tipping point, with the potential to dramatically impact healthcare and life sciences research. Companies and researchers that can effectively exploit the power of computational approaches while navigating the associated challenges will be well-positioned to lead in this rapidly evolving field.
Major Players
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Dassault Systèmes
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Genedata AG
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Insilico Medicine
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Schrödinger, Inc.
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Certara
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Simulation Plus
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Compugen Ltd.
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Strand Life Sciences
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Illumina, Inc.
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Agilent Technologies
Key Developments
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In August 2023, Schrödinger, Inc. announced a multi-year collaboration with AstraZeneca to speed up small molecule drug discovery using Schrödinger's computational platform.
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June 2023: Insilico Medicine successfully completed the first-in-human study of their AI-discovered treatment candidate for idiopathic pulmonary fibrosis, marking a significant advancement in AI-driven medication development.
1. INTRODUCTION
1.1. Market Definitions & Study Assumptions
1.2. Market Research Scope & Segment
1.3. Research Methodology
2. EXECUTIVE SUMMARY
2.1. Market Overview & Insights
2.2. Segment Outlook
2.3. Region Outlook
3. COMPETITIVE INTELLIGENCE
3.1. Companies Financial Position
3.2. Company Benchmarking -- Key Players
3.3. Market Share Analysis -- Key Companies
3.4. Recent Companies Key Activities
3.5. Pricing Analysis
3.6. SWOT Analysis
4. COMPANY PROFILES (Key Companies list by Country) (Premium)
5. COMPANY PROFILES
5.1. Dassault Systèmes
5.2. Genedata AG
5.3. Insilico Medicine
5.4. Schrödinger, Inc.
5.5. Certara
5.6. Simulation Plus
5.7. Compugen Ltd.
5.8. Strand Life Sciences
5.9. Illumina, Inc.
5.10. Agilent Technologies (*LIST NOT EXHAUSTIVE)
6. MARKET DYNAMICS
6.1. Market Trends
6.1.1. AI and machine learning revolutionize drug discovery process
6.1.2. Digital twins in healthcare for personalized treatment
6.1.3. Integration of quantum computing in computational biology
6.2. Market Drivers
6.2.1. Rising prevalence of chronic diseases and demand for personalized medicine
6.2.2. Advancements in big data analytics and AI applications
6.2.3. Increasing research funding and collaborations in computational biology
6.3. Market Restraints
6.3.1. Lack of standardization and data integration challenges
6.3.2. Ethical considerations surrounding AI use in healthcare
6.4. Market Opportunities
6.5. Porter's Five Forces Analysis
6.5.1. Threat of New Entrants
6.5.2. Bargaining Power of Buyers/Consumers
6.5.3. Bargaining Power of Suppliers
6.5.4. Threat of Substitute Products
6.5.5. Intensity of Competitive Rivalry
6.6. Supply Chain Analysis
6.7. Value Chain Analysis
6.8. Trade Analysis
6.9. Pricing Analysis
6.10. Regulatory Analysis
6.11. Patent Analysis
6.12. SWOT Analysis
6.13. PESTLE Analysis
7. BY APPLICATION (MARKET SIZE/VALUE (US$ Mn), SHARE (%), MARKET FORECAST (%), YOY GROWTH (%)-- 2020-2031)
7.1. Cellular & Biological Simulation
7.2. Drug Discovery & Disease Modeling
7.3. Preclinical Drug Development
7.4. Clinical Trials
7.5. Human Body Simulation
8. BY SERVICE TYPE (MARKET SIZE/VALUE (US$ Mn), SHARE (%), MARKET FORECAST (%), YOY GROWTH (%)-- 2020-2031)
8.1. In-House
8.2. Contract
9. BY END USER (MARKET SIZE/VALUE (US$ Mn), SHARE (%), MARKET FORECAST (%), YOY GROWTH (%)-- 2020-2031)
9.1. Academics
9.2. Industry
9.3. Commercial
10. REGION (MARKET SIZE/VALUE (US$ Mn), SHARE (%), MARKET FORECAST (%), YOY GROWTH (%)-- 2020-2031)
10.1. North America
10.1.1. United States
10.1.2. Canada
10.1.3. Mexico
10.2. South America
10.2.1. Brazil
10.2.2. Argentina
10.2.3. Rest of South America
10.3. Europe
10.3.1. Germany
10.3.2. United Kingdom
10.3.3. France
10.3.4. Italy
10.3.5. Spain
10.3.6. Russia
10.3.7. Rest of Europe
10.4. Asia-Pacific
10.4.1. China
10.4.2. Japan
10.4.3. India
10.4.4. Australia
10.4.5. South Korea
10.4.6. Rest of Asia-Pacific
10.5. Middle-East
10.5.1. UAE
10.5.2. Saudi Arabia
10.5.3. Turkey
10.5.4. Rest of Middle East
10.6. Africa
10.6.1. South Africa
10.6.2. Egypt
10.6.3. Rest of Africa
*NOTE: All the regions mentioned in the scope will be provided with (MARKET SIZE/VALUE (US$ Mn), SHARE (%), MARKET FORECAST (%), YOY GROWTH (%)-- 2020-2031)
By Application:
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Cellular & Biological Simulation
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Drug Discovery & Disease Modeling
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Preclinical Drug Development
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Clinical Trials
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Human Body Simulation
By Service Type:
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In-House
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Contract
By End User:
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Academics
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Industry
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Commercial
By Region:
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North America
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Europe
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Asia-Pacific
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Latin America
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Middle East & Africa
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