Great — here’s a compact, source-backed market reference for the Computational Biology Market you can drop into a slide or report. I included a short company table with concrete values, concise analysis (Recent developments → Drivers → Restraints → etc.), and citations for the most important claims.

This versatile research report is presenting crucial details on market relevant information, harping on ample minute details encompassing a multi-dimensional market that collectively maneuver growth in the global Computational biology market.

This holistic report presented by the report is also determined to cater to all the market specific information and a take on business analysis and key growth steering best industry practices that optimize million-dollar opportunities amidst staggering competition in Computational biology market.

Read complete report at: https://www.thebrainyinsights.com/report/computational-biology-market-12501

Company references (company → concrete value / metric)

• Schrödinger — Total revenue (FY 2024): $207.5M (software revenue $180.4M; drug-discovery revenue $27.2M). 

• Recursion Pharmaceuticals — Revenue (2024): ~$58–64M (TTM ~ $64.6M; 2024 annual ≈ $58.8M).

• Insilico Medicine — Major recent financing (Series E) $123M; reported expanding clinical/IND portfolio. (company funding & pipeline activity).

• Atomwise — Total funding raised ~$220–226M (multiple rounds; active AI drug-discovery partnerships). 

•  

• Benchling — Bio R&D platform; private valuation ~$6.1B (2024) — important commercial data/ELN player supplying life-science teams. 

Note: the computational-biology ecosystem includes dedicated software/drug-discovery AI firms (Schrödinger, Atomwise, Insilico, Recursion), informatics/platform providers (Benchling, BenchSci), and CROs / life-science service firms that embed computational biology (PerkinElmer, Charles River, Evotec).

Market snapshot & forecasts

• Estimates vary by vendor: examples include ~USD 6.3–8.1B (2024) current market sizing and forecasts to ~USD 21–32B by 2033/34 depending on source and scope (CAGRs commonly reported in the low-to-mid double digits, ~13–17% in several forecasts).

Recent developments

• Rapid inflows of capital into AI/ML drug-discovery and computational-biology startups (large fundraises, Series E rounds and strategic pharma collaborations).

• Public / listed computational-biology companies reporting accelerating software revenue while drug-discovery services and milestone revenues fluctuate (example: Schrödinger FY2024 split: strong software growth but lower drug-discovery milestone recognition vs prior year).

• Large pharma is forming consortia and partnerships to share structural/biophysical data and accelerate models (shows strategic adoption but also careful, collaborative deployment).

Drivers

• Explosion of omics & structural data (genomics, proteomics, cryo-EM / structural databases) — fuels model training and simulation. 

• AI/ML advances + compute availability (better models, cloud/GPU capacity) lowering time/cost to generate hypotheses and triage candidates.

• Pharma interest in de-risking discovery (cost/time reductions sought via in-silico screening and simulation).

Restraints

• Validation gap — few fully AI-discovered molecules have completed regulatory approval; translation from computational hits to clinical success is still uncertain.

• Data quality & fragmentation — proprietary, biased, or non-standardized datasets limit model generalizability.

• Regulatory and IP complexity around models and generated candidates can slow deployment.

Regional segmentation analysis

• North America — largest & most mature (largest share of companies, investment, and platform adoption).

• Europe — strong academic/industrial structural biology and biotech clusters (growing adoption).

• APAC — accelerating investment and talent; growing fast but more heterogeneous by country.

Emerging trends

• Hybrid wet-lab / in-silico discovery workflows (computational triage → focused experimental follow-up). 

• Federated / privacy-preserving model training and cross-company consortia to unlock proprietary structural data without exposing IP. 

• Platform consolidation — informatics platforms (Benchling, PerkinElmer) bundling more computational modules into R&D workflows. 

Top use cases

1. Virtual screening & molecular docking to prioritize small-molecule hits.

2. Protein structure prediction & design (folding, binding-site modelling). 

3. Multi-omics integration & biomarker discovery for patient stratification and target ID.

4. Biosimulation / ADMET prediction to reduce late-stage attrition.

Major challenges

• Proving ROI — demonstrating that in-silico approaches materially shorten timelines or reduce costs at scale.

• Talent & operationalizing models inside pharma R&D (need cross-disciplinary teams).

• Interoperability between computational outputs and regulated lab/clinical workflows.

Attractive opportunities

• Value-based partnerships with pharma — co-developing assets where computational biology contributes clear go/no-go decisions. 

• Platform plays for R&D data management (Benchling-style) that can upsell computational modules to large biotech customers. 

• Specialized verticals (antibodies, gene-editing design, RNA therapeutics) where targeted computational methods can unlock outsized value.

Key factors of market expansion (what to watch)

1. Clinical validation of AI-discovered candidates (first approvals or clear clinical breakthroughs will accelerate adoption). 

2. Availability & sharing of high-quality datasets (structural, multi-omics, ADMET). 

3. Compute economics & platforms — cheaper GPU/cloud compute and integrated software workflows.

4. Commercial partnerships & licensing models between computational firms and large pharma (milestones, royalties, joint pipelines). 

If you want, I can now:

• convert this into a one-slide PowerPoint (company table + 6 bullets), or

• produce a CSV with the company names and cited values (revenues, funding, valuation) for import into your dataset.

Which output should I build next?