Computational Biology Market Size and Share
Computational Biology Market Analysis by Mordor Intelligence
The computational biology market currently generates USD 7.24 billion and is projected to reach USD 13.36 billion in 2030, advancing at a 13.02% CAGR. This outlook signals how transformer-based genome language models, synthetic-biology digital twins, and wider AI adoption now shape every application layer of the computational biology market. A sharp rise in multi-omics datasets, ongoing shifts toward contract research services, and the need for scalable cloud infrastructure keep fueling demand. North America still anchors the computational biology market thanks to mature biotech regulation, but Asia-Pacific’s supercomputer investments and expanding pharmaceutical manufacturing base are positioning the region as the next growth engine. Meanwhile, strategic acquisitions such as Siemens’ USD 5.1 billion deal for Dotmatics reflect intensifying platform consolidation inside the computational biology market.
Key Report Takeaways
- By application, cellular and biological simulation accounted for 32.52% of computational biology market share in 2024, while drug discovery and disease modeling is forecast to grow at a 15.64% CAGR through 2030.
- By tool, databases held the largest 36.46% share of the computational biology market size in 2024, yet analysis software and services are expected to expand at a 14.77% CAGR to 2030.
- By service model, contract arrangements commanded 52.45% of the computational biology market share in 2024 and are advancing at a 16.03% CAGR through 2030.
- By end user, academia retained 44.53% revenue share in 2024, whereas industry and commercial users are projected to post a 14.56% CAGR to 2030.
- By region, North America led with 42.78% computational biology market share in 2024; Asia-Pacific shows the fastest 16.35% CAGR outlook through 2030.
Global Computational Biology Market Trends and Insights
Drivers Impact Analysis
Driver | (~) % Impact on CAGR Forecast | Geographic Relevance | Impact Timeline |
---|---|---|---|
Rising volume of omics data & bioinformatics research | +2.8% | Global, concentrated in North America and EU | Medium term (2–4 years) |
Accelerated use in drug discovery & disease modeling | +3.1% | Global, led by North America, expanding to APAC | Short term (≤ 2 years) |
Expansion of clinical pharmacogenomics & pharmacokinetics studies | +1.9% | North America and EU, emerging in APAC | Medium term (2–4 years) |
Transformer-based genome language models enabling rapid annotation | +2.2% | Global, early adoption by research institutes | Short term (≤ 2 years) |
Synthetic-biology digital twins for in-silico workflows | +1.7% | North America and EU, pilots in APAC | Long term (≥ 4 years) |
Open-source single-cell lineage-tracing algorithms | +1.5% | Global, academic-led with industry uptake | Medium term (2–4 years) |
Source: Mordor Intelligence
Rising volume of omics data & bioinformatics research
Terabyte-scale single-cell RNA-sequencing, multi-omics integration, and lower sequencing costs continue to expand data flows into the computational biology market. cut RNA-seq costs 50–70%, widening access to precision-medicine datasets. Large language models now automate 94% of common data-element mapping, driving interoperability.[1]Rodney Alan Long, Jordan Klebanoff, and Vince D. Calhoun, “A New AI-Assisted Data Standard Accelerates Interoperability in Biomedical Research,” medRxiv, medrxiv.orgThe resulting data network effects reinforce first-mover advantages for stakeholders controlling the largest repositories. Cloud bioinformatics platforms therefore have become mandatory infrastructure for organizations lacking on-premises high-performance computing.
Accelerated use in drug discovery & disease modeling
Protein language models like ESM-3 simulate evolutionary processes, creating novel protein candidates at a pace drug developers could not reach a few years ago. Hybrid AI–quantum systems, exemplified by Model Medicines’ GALILEO, now deliver 100% hit-rate antiviral screens.[2]Model Medicines Communications Team, “The Future of Drug Discovery: 2025 as the Inflection Year for Hybrid AI and Quantum Computing,” Model Medicines, modelmedicines.comDigital twins let researchers run millions of virtual experiments, compressing hypothesis-testing cycles and reducing wet-lab costs. A 479,000-trial machine-learning benchmark provides unprecedented training data for trial-design optimization. M&A activity, such as the USD 688 million Recursion-Exscientia merger, shows incumbents racing to internalize these AI advantages consolidated platforms.
Expansion of clinical pharmacogenomics & pharmacokinetics studies
Preemptive pharmacogenomics testing cut psychiatric adverse drug reactions 34.1% and hospitalizations 41.2%.[3]Maria Skokou, Konstantinos Tziomalos, and Georgios Papazisis, “Clinical Implementation of Preemptive Pharmacogenomics in Psychiatry,” eBioMedicine, thelancet.com Real-world panels show 60.4% of patients receive at least one actionable prescription. UCLA leveraged a 342,000-person biobank to identify 156 genes modulating statin efficacy, proof that genetic diversity improves dosing accuracy. AI-enhanced PK/PD models now account for population-specific variants, a requirement as Asia-Pacific pharmacogenomics adoption rises.
Transformer-based genome language models enabling rapid annotation
Open-source protein models deliver AlphaFold-class performance while requiring only commodity GPUs . Bidirectional DNA foundation models like JanusDNA process 1 million base pairs without specialized hardware. Parameter-efficient fine-tuning methods such as LoRA cut training costs yet still improve downstream prediction accuracy. These gains democratize advanced analytics and lower barriers to entry, extending the computational biology market well beyond traditional bioinformatics centers.
Restraints Impact Analysis
Restraint | (~) % Impact on CAGR Forecast | Geographic Relevance | Impact Timeline |
---|---|---|---|
Shortage of multidisciplinary talent | -1.8% | Global, acute in North America and EU | Short term (≤ 2 years) |
Interoperability & data-standardization gaps | -1.2% | Global, especially cross-border collaborations | Medium term (2–4 years) |
Escalating cloud & compute costs | -0.9% | Global, strongest effect in cost-sensitive markets | Short term (≤ 2 years) |
Biosecurity & dual-use regulatory scrutiny | -0.7% | Mainly North America and EU, expanding worldwide | Long term (≥ 4 years) |
Source: Mordor Intelligence
Shortage of multidisciplinary talent
Demand for professionals versed in biology, software engineering, and statistics outstrips supply. Life-science employers foresee a 35% shortfall by 2030, with hiring demand growing 11.75% annually. Salary inflation and project delays follow, particularly for mid-sized biotechs that compete with tech giants entering the field. Skills-based hiring, apprenticeships, and cross-industry recruitment are interim mitigation strategies.
Interoperability & data-standardization gaps
While Matrix and Analysis Metadata Standards (MAMS) begin to align single-cell datasets, broad harmonization remains elusive. Semantic mapping tools can integrate unstructured health records, yet implementation burdens slow adoption. Federated-learning pilots protect privacy but still confront regulatory uncertainty, leaving multinational studies reliant on manual data cleaning.
Segment Analysis
By Application: Drug discovery and disease modeling power next-generation workflows
Drug discovery and disease modeling already posts the fastest 15.64% CAGR, whereas cellular and biological simulation retained a 32.52% 2024 stake in the computational biology market size. AI-enhanced target identification and lead optimization let companies such as Insilico Medicine screen millions of compounds in silico. Preclinical teams now integrate genomic, proteomic, and metabolomic data sets to raise compound-to-clinic success odds. Clinical-trial operations employ retrieval-augmented systems that reach 97.9% eligibility-screen accuracy, cutting recruitment bottlenecks. A growing number of investigators exploit digital twins to run virtual dose-response studies, shrinking wet-lab timelines. Consequently, the computational biology market experiences deeper pharmaceutical engagement at every R&D gate.
Human-body simulation software emerges as a high-potential sub-segment. Stanford’s AI-driven “virtual cell” illustrates how integrated multi-omics and biophysical models can map pathway perturbations for individualized therapy strategies. This development expands the computational biology market to frontline precision-medicine clinicians. As digital twin fidelity rises, insurers begin evaluating reimbursement models for computer-optimized treatment plans, hinting at downstream revenue pools.
Note: Segment shares of all individual segments available upon report purchase
By Tool: Analysis software accelerates AI integration
Databases still represent 36.46% of computational biology market share, but analysis software and services chart the fastest 14.77% CAGR. Protein and genome language models are pushing organizations to buy analytic capacity rather than maintain static archives. Vendors embed multimodal data pipelines that fuse genomic, proteomic, and clinical streams. The shift also encourages academic-industry consortia to co-develop open-source stacks; Boltz-1’s AlphaFold-comparable accuracy on standard GPUs underscores how community innovation fuels wider adoption.
On-premises high-performance computing remains important for sensitive datasets; however, cloud cost curves and managed-service maturity encourage migration. Providers differentiate by auto-scaling algorithms and security certifications. Database incumbents react by building analytics layers on top of repositories to defend their install base. The net effect increases competition yet lifts overall software quality, supporting sustained growth in the computational biology market.
By Service: Contract models dominate growth
Contract research services lead both share and velocity—52.45% in 2024 and a 16.03% CAGR outlook—as pharmaceutical companies outsource complex in-silico workflows. CROs now bundle genomic analysis, AI model development, and virtual screening in unified subscriptions. In-house teams retain core IP-intensive algorithms but partner externally for compute-heavy simulations.
Hybrid service frameworks gain traction. Enterprises maintain data-governance nodes on premises while bursting to cloud-based CRO platforms for peak workloads. Strategic alliances distribute risk: clients pay usage-based fees, while providers guarantee service-level agreements that include regulatory support. As adoption rises, the computational biology market further integrates into traditional drug-development value chains.

Note: Segment shares of all individual segments available upon report purchase
By End User: Industry adoption accelerates
Academia controlled 44.53% revenue in 2024, yet industry users capture momentum with a 14.56% CAGR through 2030. Declining sequencing costs, validated AI pipelines, and urgent therapeutic timelines drive pharmaceutical uptake. Enterprise buyers seek turnkey solutions that embed audit trails and GxP compliance.
Academic institutions remain knowledge engines, pioneering algorithms later licensed commercially. To counter budget limits, universities expand partnership models where technology vendors provide compute credits in exchange for co-authorship and early-access feedback. This symbiosis sustains innovation funnels for the computational biology industry.
Geography Analysis
North America, commanding 42.78% 2024 revenue, benefits from deep biotech venture capital, mature regulator engagement, and a dense talent pool. The FDA’s evolving AI framework gives local firms a clearer commercialization path than many peers. Thermo Fisher’s USD 2 billion multiyear domestic investment underscores confidence in infrastructure scalability. Nonetheless, workforce shortages and rising cloud costs temper acceleration.
Asia-Pacific posts the highest 16.35% CAGR. Governments bankroll exaflop supercomputers—South Korea’s plan targets launch by 2025—while China’s distributed national centers already propel multi-omics projects. Regional pharmaceutical manufacturing booms, and genetic-diversity research programs tailor AI models to local populations, creating edge-case data assets unavailable elsewhere. Decentralized clinical-trial pilots and mRNA platform build-outs reinforce long-term demand for computational biology market capabilities.
Europe maintains steady growth anchored by cross-border consortia and robust data-privacy safeguards. Ethical-AI initiatives crank up compliance overhead, yet also foster trust among payers and regulators. Digital-twin pilots align with public-health goals to optimize resource use. Meanwhile, Latin America, Africa, and the Middle East inch forward as internet infrastructure and bioinformatics curricula expand. Partnerships with multinational pharma groups compensate for local funding gaps, ensuring gradual but persistent computational biology market penetration.

Competitive Landscape
The computational biology market remains moderately fragmented but shows a clear M&A uptrend. Siemens’ USD 5.1 billion Dotmatics acquisition integrates lab-informatics with industrial digital-twin offerings, reflecting buyers’ desire for end-to-end stacks. Danaher brought Genedata into its portfolio, mirroring the same logic. Illumina collaborates with NVIDIA to speed GPU-powered omics analytics, an example of tech–biotech convergence.
Start-ups leverage open-source communities to punch above their scale. EvolutionaryScale raised USD 142 million to commercialize protein-generating AI that competes directly with incumbents’ proprietary chemistries. Patent filings around hybrid quantum-classical models and lineage-tracing algorithms indicate intensifying IP battles. Competitive success will hinge on access to curated datasets, scalable compute, and integrated workflows that minimize switching costs.
Large vendors pursue ecosystem lock-in through subscription licensing and data-network effects. Mid-tier players differentiate via vertical specialization—single-cell analytics, digital-twin engines, or pharmacogenomics toolkits. Price competition is muted because accuracy, regulatory compliance, and turnaround speed remain decisive purchase factors.
Computational Biology Industry Leaders
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Dassault Systèmes SE
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Schrödinger Inc.
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Certara
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Simulation Plus Inc.
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Illumina Inc.
- *Disclaimer: Major Players sorted in no particular order

Recent Industry Developments
- June 2025: Illumina acquired SomaLogic for up to USD 425 million to broaden proteomics and biomarker capabilities, enlarging its multi-omics portfolio.
- April 2025: Siemens closed the USD 5.1 billion takeover of Dotmatics, merging R&D informatics with industrial digital-twin frameworks.
- February 2025: Illumina released constellation-mapped reads and 5-base sequencing solutions, set for commercial roll-out in 2026.
- January 2025: Illumina partnered with NVIDIA to accelerate multi-omics data pipelines using GPUs, targeting faster therapeutic discovery.
Global Computational Biology Market Report Scope
As per the scope, computational biology involves developing and applying data-analytical and theoretical methods, mathematical modeling, and computational simulation techniques to study biological, ecological, behavioral, and social systems. Computational biology uses biological data to develop algorithms to understand biological systems and relationships. The Computational Biology market is segmented by Application (Cellular and Biological Simulation, Drug Discovery and Disease Modelling, Preclinical Drug Development, Clinical Trials, and Human Body Simulation Software), Tool (Databases, Infrastructure (Hardware), Analysis Software and Services), Service (In-house and Contract), End User (Academics and Industry and Commercials) and Geography (North America, Europe, Asia-Pacific, Middle-East, and Africa, and South America). The report also covers the estimated market sizes and trends for 17 countries across major regions globally. The report offers the value (in USD) for the above segments.
By Application | Cellular & Biological Simulation | Computational Genomics | |
Computational Proteomics | |||
Pharmacogenomics | |||
Other Simulations (Transcriptomics/Metabolomics) | |||
Drug Discovery & Disease Modelling | Target Identification | ||
Target Validation | |||
Lead Discovery | |||
Lead Optimization | |||
Preclinical Drug Development | Pharmacokinetics | ||
Pharmacodynamics | |||
Clinical Trials | Phase I | ||
Phase II | |||
Phase III | |||
Human Body Simulation Software | |||
By Tool | Databases | ||
Infrastructure (Hardware) | |||
Analysis Software & Services | |||
By Service | In-house | ||
Contract | |||
By End-User | Academics | ||
Industry & Commercials | |||
By Geography | North America | United States | |
Canada | |||
Mexico | |||
Europe | Germany | ||
United Kingdom | |||
France | |||
Italy | |||
Spain | |||
Rest of Europe | |||
Asia-Pacific | China | ||
Japan | |||
India | |||
Australia | |||
South Korea | |||
Rest of Asia-Pacific | |||
Middle East and Africa | GCC | ||
South Africa | |||
Rest of Middle East and Africa | |||
South America | Brazil | ||
Argentina | |||
Rest of South America |
Cellular & Biological Simulation | Computational Genomics |
Computational Proteomics | |
Pharmacogenomics | |
Other Simulations (Transcriptomics/Metabolomics) | |
Drug Discovery & Disease Modelling | Target Identification |
Target Validation | |
Lead Discovery | |
Lead Optimization | |
Preclinical Drug Development | Pharmacokinetics |
Pharmacodynamics | |
Clinical Trials | Phase I |
Phase II | |
Phase III | |
Human Body Simulation Software |
Databases |
Infrastructure (Hardware) |
Analysis Software & Services |
In-house |
Contract |
Academics |
Industry & Commercials |
North America | United States |
Canada | |
Mexico | |
Europe | Germany |
United Kingdom | |
France | |
Italy | |
Spain | |
Rest of Europe | |
Asia-Pacific | China |
Japan | |
India | |
Australia | |
South Korea | |
Rest of Asia-Pacific | |
Middle East and Africa | GCC |
South Africa | |
Rest of Middle East and Africa | |
South America | Brazil |
Argentina | |
Rest of South America |
Key Questions Answered in the Report
1. What is the current size of the computational biology market?
The computational biology market generates USD 7.24 billion in 2025 and is on track to hit USD 13.36 billion by 2030.
2. Which application area is expanding fastest?
Drug discovery and disease modeling posts the highest 15.64% CAGR through 2030, driven by AI-enabled target identification and digital-twin workflows.
3. Why are contract research services growing rapidly?
Pharmaceutical firms outsource data-intensive modeling to specialized CROs, giving contract services a 52.45% share and a 16.03% growth rate.
4. Which region will contribute most to future growth?
Asia-Pacific leads with a 16.35% CAGR thanks to government supercomputer projects and rapidly expanding pharmaceutical manufacturing.
5. What is hindering wider adoption of computational biology platforms?
A shortage of multidisciplinary talent, rising cloud-compute costs, and evolving biosecurity regulations are the main constraints.
Page last updated on: June 26, 2025