How AI and Automation are Opening the Door to Biotech Innovators

Advances in AI, cloud laboratories and robotics are breaking down the entry barriers to biotech, enabling very small, focused teams to do R&D that was previously only possible for better-funded companies¹. Market analysis shows the drug discovery market was estimated at $3.5 billion in 2024, and is expected to exceed $49.5 billion by 2034².

For the UK, this presents a great opportunity for early-stage founders, as companies that understand how to combine biology with advanced technology are more fundable, more attractive to pharma partners and better positioned to grow.

What Changed Over The Past Year?

Cloud labs have now been becoming more accessible: platforms like Emerald Cloud Lab let scientists design experiments remotely, and researchers ship samples and run full-coded experiments via its software interface³. As cloud labs scale, the cost per experiment drops, making them viable for very small teams¹.

AI platforms are also becoming more powerful: advanced AI systems are being built specifically for drug discovery – a recent review tells us how next-generation AI platforms are helping researchers navigate complex challenges in biopharmaceutical discovery⁴. New generative models are also capable of designing de novo proteins⁵. For example, AlphaProteo (a model created in 2024) can produce high-affinity protein binders with minimal experimental screening⁵. Additionally, multi-agent large language model (LLM) systems are being explored for collaborative protein designs, combing ML, physics, and simulation all in a single platform⁶.

Furthermore, self-driving labs can now be run online⁷. Work is currently underway to build systems that tie together robots, AI decision-making, and data pipelines to run fully autonomous experiments⁷. Recently, a system called ‘Artificial’ was described that does exactly this, coordinating instruments and AI models in real-time⁷. These systems can be game-changing for start-ups.

Moreover, the UK is actively building its AI-Biotech ecosystem, with government initiatives pushing for the UK to lead in AI-driven drug discovery⁸. A UK Government press release this year announced a major new AI-Drug Discovery initiative, including an AI-Driven Innovation Centre⁸. Sources also say the UK is shifting toward a ‘technology-first’ biotech model, where many companies are integrating AI and data science, such as Brainomix , who create better treatments for stroke and lung fibrosis⁹. Another example is CardiaTex, claiming to be the first company to create an AI approach towards drug target discovery⁹.

Strategic Advantages

Research finds that AI, automated chemistry platforms and machine learning can reduce the team size required for drug discovery, allowing them to operate as traditional pharma does¹⁰

• Lower costs, more flexibility: ****Outsourced labs and smaller teams mean longer runway and faster decisions¹
• Automation and AI let teams test, learn, and adjust far quicker than traditional biotechs¹¹
• Clean, tech-enabled pipelines are easier for big pharma to license or partner with¹²
• Founders can run experiments remotely or through regional labs instead of building facilities¹
• Investors are actively backing small, AI-enabled biotechs with clear data-driven R&D plans, especially when lean operations can bring faster results¹³
• Early integration of data science helps founders plan their preclinical work more clearly¹⁴

Risks and Challenges

• Running large models and constant AI cycles can be costly¹⁵
• Some assays still require manual work and can’t be automated easily¹⁶
• AI-designed molecules still need traditional wet-lab lab tests to prove their safety and effectiveness¹⁴
• As AI tools become common, they could stop being a competitive advantage¹⁵

Strategic Recommendations

• Start with a compute-first mindset: ****Build your R&D around cloud labs, AI design tools, and automation rather than assuming you’ll need a large in-person lab.
• Outsource smartly: Use cloud labs, CROs, and automated facilities for routine work so your core team can focus on the decisions that really matter.
• Use AI for design, biology for confirmation: Let AI drive molecule design, but be ready to validate key findings using traditional experiments.
• Plan your regulatory path early: Think from the start about how your AI-designed molecules will move through preclinical work and regulatory steps to ensure compliance with laws and regulation

References

1. Armer C, Letronne F, DeBenedictis E. Support academic access to automated cloud labs to improve reproducibility. PLOS Biology. 2023;21(1):e3001919. doi:https://doi.org/10.1371/journal.pbio.3001919

2. GMI. AI in Drug Discovery Market Size & Share Forecast Report - 2032. Global Market Insights Inc. Published 2025. Accessed November 17, 2025. https://www.gminsights.com/industry-analysis/ai-in-drug-discovery-market

3. Emerald Cloud Lab. Emerald Cloud Lab: Remote Controlled Life Sciences Lab. www.emeraldcloudlab.com. Published 2025. Accessed November 17, 2025. https://www.emeraldcloudlab.com/

4. Bettanti A, Beccari AR, Biccarino M. Exploring the future of biopharmaceutical drug discovery: can advanced AI platforms overcome current challenges? Discover Artificial Intelligence. 2024;4(1). doi:https://doi.org/10.1007/s44163-024-00188-3

5. Zambaldi V, La D, Chu AE, et al. De novo design of high-affinity protein binders with AlphaProteo. arXiv.org. Published 2024. Accessed November 17, 2025. https://arxiv.org/abs/2409.08022

6. Ghafarollahi A, J BM. ProtAgents: Protein discovery via large language model multi-agent collaborations combining physics and machine learning. arXiv.org. Published 2024. Accessed November 17, 2025. https://arxiv.org/abs/2402.04268

7. Fehlis Y, Mandel P, Crain C, Liu B, Fuller D. Accelerating drug discovery with Artificial: a whole-lab orchestration and scheduling system for self-driving labs. arXiv.org. Published 2025. Accessed November 17, 2025. https://arxiv.org/abs/2504.00986

8. Department for Science, Innovation and Technology . UK to become world leader in drug discovery as Technology Secretary heads for London Tech Week. GOV.UK. Published June 8, 2025. Accessed November 17, 2025. https://www.gov.uk/government/news/uk-to-become-world-leader-in-drug-discovery-as-technology-secretary-heads-for-london-tech-week

9. UK Bioindustry Association. In Collaboration with UK Driving the AI Revolution TechBio 2023 Contents.; 2023. Accessed November 17, 2025. https://techbio.org.uk/wp-content/uploads/2023/10/TechBio-2023-UK-driving-the-AI-revolution-2.pdf

10. Zhang K, Yang X, Wang Y, et al. Artificial intelligence in drug development. Nature Medicine. 2025;31(31):1-15. doi:https://doi.org/10.1038/s41591-024-03434-4

11. Tobias AV, Wahab A. Autonomous “self-driving” laboratories: a review of technology and policy implications. Royal Society Open Science. 2025;12(7). doi:https://doi.org/10.1098/rsos.250646

12. Catrin Hasselgren, Oprea TI. Artificial Intelligence for Drug Discovery: Are We There Yet? arXiv (Cornell University). Published online July 12, 2023. doi:https://doi.org/10.1146/annurev-pharmtox-040323-040828

13. UK Bioindustry Association. Biotech Finance. Published 2025. Accessed November 17, 2025. https://biotechfinance.org/

14. Ocana A, Pandiella A, Privat C, et al. Integrating artificial intelligence in drug discovery and early drug development: a transformative approach. Biomarker Research. 2025;13(1). doi:https://doi.org/10.1186/s40364-025-00758-2

15. Pilz K, Heim L, Brown N. Increased Compute Efficiency and the Diffusion of AI Capabilities.; 2024. Accessed November 17, 2025. https://arxiv.org/pdf/2311.15377

16. Gul S. The changing landscape of automation in pre-clinical drug discovery. European Pharmaceutical Review. Published June 8, 2017. Accessed November 17, 2025. https://www.europeanpharmaceuticalreview.com/article/606/the-changing-landscape-of-automation-in-pre-clinical-drug-discovery/