Isomorphic Labs, a UK-based subsidiary of Alphabet, is preparing for its first human clinical trial of a drug developed with AI.

The company has not disclosed the specific condition or the exact trial start date.

Colin Murdoch, president of Isomorphic Labs and chief business officer at DeepMind, said the drug candidates are currently in the preclinical stage in an interview with Fortune.

The company uses AlphaFold, a protein-structure prediction system developed by DeepMind.

Released in 2020, AlphaFold has been widely adopted in research and was jointly awarded the Nobel Prize in Chemistry in 2024.

The firm has partnered with pharmaceutical companies like Novartis and Eli Lilly, focusing on oncology and immunology.

In April 2025, Isomorphic Labs raised US$600 million from Thrive Capital to support clinical development and expand its AI infrastructure.

.source-ref{font-size:0.85em;color:#666;display:block;margin-top:1em;}a.ask-tia-citation-link:hover{color:#11628d !important;background:#e9f6f5 !important;border-color:#11628d !important;text-decoration:none !important;}@media only screen and (min-width:768px){a.ask-tia-citation-link{font-size:11px !important;}}

🔗 Source: YourStory

🧠 Food for thought

1️⃣ AI drug discovery reaches inflection point after decades of development

While Google’s Isomorphic Labs announcement marks a milestone, it represents the culmination of nearly 50 years of AI development in medicine, dating back to early clinical decision systems in the 1970s 1.

The industry has reached a critical mass in 2025, with multiple companies announcing AI-designed drug candidates entering clinical trials—not just Google’s efforts 2.

Financial markets recognize this inflection point, with the AI drug discovery sector projected to grow from $1.72 billion in 2024 to $8.53 billion by 2030, representing a 30.59% compound annual growth rate 3.

Companies like Insilico Medicine have already achieved significant milestones, including the first AI-discovered drug to enter a phase 2 clinical trial, demonstrating the practical application of these technologies 2.

2️⃣ Economics of drug development fundamentally changing through AI adoption

Traditional drug development costs billions and takes 10-15 years, but AI integration is expected to reduce timelines by 30% and costs by 40%, transforming the pharmaceutical business model 4.

By analyzing terabytes to petabytes of data, AI can identify promising compounds with significantly fewer experiments, allowing companies to make more informed decisions about which drug candidates to pursue 5.

These efficiency gains extend beyond discovery to clinical trials, where AI is transforming patient recruitment and monitoring, significantly speeding up processes that traditionally cause major delays 4.

Pharmaceutical giants like Pfizer and AstraZeneca are leading integration of AI across their pipelines, from initial discovery through regulatory submission, creating comprehensive digital transformations 4.

Even regulatory processes are being streamlined through AI, with systems that can predict regulatory queries and automate documentation, potentially reducing the lengthy approval process 5.

3️⃣ Collaborative ecosystem emerges between AI specialists and pharmaceutical companies

The most successful approach to AI drug discovery involves partnerships between AI specialists and established pharmaceutical companies, with over 18 major pharma companies and 75 startups now collaborating in this space 6.

These partnerships combine pharmaceutical companies’ extensive biological knowledge and clinical trial infrastructure with the specialized AI expertise of technology companies and startups 3.

The integration of AI with biological datasets has attracted substantial investment, with companies like Recursion Pharmaceuticals securing significant funding to merge computational and experimental methods 2.

Digital twin technology developed by companies like Unlearn demonstrates how these collaborations are revolutionizing clinical trials by creating virtual patient models that can reduce trial size and cost 7.

Addressing complex regulatory challenges requires ongoing collaboration between AI developers, pharmaceutical companies, and regulatory bodies to ensure safe implementation while enabling innovation 8.

Recent Isomorphic Labs developments

……