Isomorphic Labs, DeepMind's Spinoff, Nears Human Trials for AI-Designed Drugs

The landscape of pharmaceutical research and development, long characterized by painstaking processes, exorbitant costs, and high failure rates, is on the brink of a profound transformation. At the forefront of this potential revolution is Isomorphic Labs, a company spun out of Google DeepMind, which is reportedly nearing a critical milestone: the initiation of human clinical trials for drug candidates designed with the assistance of artificial intelligence.

This development marks a significant step forward for the application of advanced AI in the life sciences, moving beyond theoretical modeling and preclinical research into the crucial phase of testing potential therapies in human subjects. The news, highlighted in a recent report, underscores the growing maturity and potential impact of AI technologies in tackling some of the most complex challenges in modern medicine.

From AlphaFold to AI-Powered Drug Discovery

Isomorphic Labs traces its lineage directly back to one of DeepMind's most celebrated achievements: AlphaFold. AlphaFold, first unveiled in 2018 and significantly advanced in subsequent iterations, demonstrated an unprecedented ability to accurately predict the three-dimensional structure of proteins solely from their amino acid sequence. This was a monumental breakthrough, as a protein's structure dictates its function, and understanding this structure is fundamental to understanding biological processes and designing drugs that can interact with specific proteins.

The initial versions of AlphaFold focused primarily on predicting the structure of individual proteins. However, the technology rapidly evolved. Later iterations, including AlphaFold 3 released in 2024, expanded its capabilities to model how proteins interact not just with each other, but also with other crucial molecules within a cell, such as DNA, RNA, and small molecules – the very building blocks of many drugs. This leap from predicting static structures to modeling dynamic interactions was pivotal, transforming AlphaFold from a powerful tool for structural biology into a potentially revolutionary engine for drug discovery.

Colin Murdoch, DeepMind's chief business officer and president of Isomorphic Labs, emphasized the direct link between AlphaFold's progress and Isomorphic's mission. "The company, which was spun out of DeepMind in 2021, was born from one of DeepMind's most celebrated breakthroughs, AlphaFold, an AI system capable of predicting protein structures with a high level of accuracy," he noted. "Interactions of AlphaFold progressed from being able to accurately predict individual protein structures to modeling how proteins interact with other molecules like DNA and drugs. These leaps made it far more useful for drug discovery, helping researchers design medicines faster and more precisely, turning the tool into a launchpad for a much larger ambition..."

This evolution laid the groundwork for Isomorphic Labs to be established as a dedicated entity focused on applying these advanced AI capabilities specifically to the complex task of discovering and designing new medicines. The vision is to create a platform that can not only predict protein structures but also simulate how potential drug molecules will bind to target proteins, predict their efficacy, and even design novel molecules from scratch optimized for specific therapeutic goals.

The Traditional Drug Discovery Pipeline: A Challenging Journey

To fully appreciate the potential impact of Isomorphic Labs' work, it's essential to understand the traditional drug discovery and development process. It is a long, arduous, and incredibly expensive journey, often taking 10-15 years and costing billions of dollars for a single drug to go from initial research to market. The process is typically broken down into several key stages:

1. Discovery and Exploration: Identifying a disease target (e.g., a protein involved in a disease pathway) and finding potential drug candidates (molecules) that can interact with that target. This often involves high-throughput screening of vast libraries of compounds.
2. Preclinical Research: Testing potential drug candidates in laboratory settings (in vitro, using cells or tissues) and in animals (in vivo) to assess safety, dosage, and efficacy before human testing.
3. Clinical Trials: Testing the drug in humans through a series of phases:
• Phase 1: Small group of healthy volunteers (20-100) to assess safety, dosage range, and side effects.
• Phase 2: Larger group of patients (100-500) with the target disease to evaluate efficacy and further assess safety.
• Phase 3: Large group of patients (hundreds to thousands) across multiple sites to confirm efficacy, monitor side effects, compare to standard treatments, and collect data for regulatory approval.
4. Regulatory Review: Submitting data from preclinical and clinical trials to regulatory bodies (like the FDA in the US or EMA in Europe) for approval to market the drug.
5. Post-Market Monitoring (Phase 4): Continued monitoring of the drug's safety and effectiveness in the general population after approval.

The vast majority of drug candidates fail during this process, particularly in the clinical trial phases. Failure can occur for many reasons, including lack of efficacy, unacceptable toxicity, or poor pharmacokinetics (how the body absorbs, distributes, metabolizes, and excretes the drug). The high failure rate contributes significantly to the overall cost and time required to bring a successful drug to market.

As Colin Murdoch pointed out, "Today, pharma companies often spend millions attempting to bring a single drug to market, sometimes with just a 10% chance of success once trials begin." This stark reality highlights the urgent need for tools and approaches that can improve the probability of success and streamline the entire pipeline.

AI's Promise: Speed, Efficiency, and Higher Success Rates

Isomorphic Labs believes its AI technology can radically improve these odds. By leveraging sophisticated models trained on vast datasets of biological and chemical information, the company aims to intervene at multiple stages of the drug discovery process:

• Target Identification: AI can analyze complex biological data (genomics, proteomics, etc.) to identify novel disease targets that were previously difficult to pinpoint.
• Molecule Design: Instead of relying solely on screening existing libraries, AI can generate novel molecular structures predicted to have desired properties (e.g., binding affinity to a target protein, favorable safety profile).
• Predicting Interactions: AlphaFold's core strength lies in predicting how molecules interact. This allows researchers to quickly evaluate potential drug candidates and predict their behavior in biological systems without needing extensive laboratory experiments initially.
• Optimizing Candidates: AI can help refine lead compounds to improve their potency, selectivity, and pharmacokinetic properties.
• Predicting Clinical Success: By integrating data from various sources, AI models might eventually be able to better predict the likelihood of a drug candidate succeeding in human trials, allowing researchers to prioritize the most promising candidates.

Murdoch articulated this ambition: "We're trying to do all these things: speed them up, reduce the cost, but also really improve the chance that we can be successful." The ultimate goal is to reach a point where the process is so efficient and predictive that researchers have a much higher conviction that the drugs they are developing will work effectively and safely in humans.

The vision extends to a future where the initial steps of drug design could be dramatically simplified. "One day we hope to be able to say — well, here's a disease, and then click a button and out pops the design for a drug to address that disease," Murdoch stated. "All powered by these amazing AI tools." While this might sound futuristic, it encapsulates the potential for AI to automate and optimize the early, labor-intensive phases of drug discovery.

Building Partnerships and Securing Investment

Isomorphic Labs isn't operating in isolation. Recognizing the need for deep pharmaceutical expertise and access to extensive biological data, the company has strategically partnered with established players in the industry. In 2024, the same year DeepMind released AlphaFold 3, Isomorphic signed major research collaborations with two pharmaceutical giants: Novartis and Eli Lilly. These partnerships are crucial, providing Isomorphic with valuable insights into the complexities of drug development, access to proprietary data, and a clear path towards translating their AI-designed candidates into potential therapies that can be tested and eventually brought to market.

These collaborations are part of Isomorphic's broader strategy to build a "world-class drug design engine." By combining their cutting-edge AI capabilities with the decades of experience and infrastructure of major pharma companies, Isomorphic aims to accelerate the validation and progression of its AI-designed molecules.

Further underscoring the confidence in Isomorphic's approach and potential, the company successfully raised $600 million in its first-ever external funding round in April 2025, led by Thrive Capital. This substantial investment provides Isomorphic with the resources needed to scale its operations, hire top talent (including pharma veterans to complement their AI experts), and push its most promising candidates towards clinical trials.

The combination of strategic partnerships and significant funding positions Isomorphic Labs strongly as it moves towards the expensive and complex phase of human clinical trials.

Preparing for Human Trials: The Next Big Milestone

The most significant hurdle and the next major milestone for Isomorphic Labs is transitioning from preclinical validation to human clinical trials. This phase is not only critical for testing the safety and efficacy of the AI-designed drugs but also represents a major validation of the AI platform itself.

Murdoch confirmed that the company is actively preparing for this step. "The next big milestone is actually going out to clinical trials, starting to put these things into human beings," he said. "We're staffing up now. We're getting very close."

Preparing for clinical trials involves a multitude of steps, including:

• Finalizing preclinical studies and submitting Investigational New Drug (IND) applications to regulatory agencies.
• Designing clinical trial protocols (determining patient populations, dosages, endpoints, duration, etc.).
• Identifying and partnering with clinical research organizations (CROs) and trial sites.
• Recruiting and enrolling patients.
• Manufacturing the drug candidate at scale and to regulatory standards.

This phase requires not only scientific rigor but also significant operational expertise and financial resources. Isomorphic's recent funding round and its partnerships with experienced pharmaceutical companies are undoubtedly crucial for navigating this complex process.

The fact that Isomorphic is "very close" to starting human trials suggests that their AI platform has successfully generated drug candidates that have shown sufficient promise and safety in preclinical testing to warrant evaluation in humans. This is a testament to the power of the underlying AI models, particularly AlphaFold and its successors, in accurately predicting molecular behavior and designing potentially viable therapeutic molecules.

AI in the Lab: Collaboration Between Humans and Algorithms

While the vision of clicking a button to design a drug is compelling, the reality today involves a close collaboration between human scientists and AI tools. Isomorphic Labs employs a team of researchers, chemists, biologists, and pharmacologists who work alongside the AI platform.

"There are people sitting in our office in King's Cross, London, working, and collaborating with AI to design drugs for cancer," said Colin Murdoch. "That's happening right now."

In this collaborative model, AI acts as a powerful assistant, capable of sifting through vast amounts of data, identifying patterns, predicting outcomes, and generating novel hypotheses at a speed and scale impossible for humans alone. Human experts provide the biological and chemical intuition, design the experiments, interpret the results, and guide the AI towards the most promising avenues.

For example, AI might suggest thousands of potential molecules that could bind to a target protein. Human chemists would then evaluate these suggestions based on factors like synthesizability, potential toxicity, and other properties, selecting the most promising candidates for laboratory testing. The results from these experiments can then be fed back into the AI models, allowing them to learn and improve their predictions.

This human-AI synergy is key to unlocking the full potential of AI in drug discovery. It combines the computational power and pattern recognition abilities of AI with the creativity, critical thinking, and domain expertise of human scientists.

The Broader Impact of AI on Healthcare

Isomorphic Labs' progress is part of a larger trend of AI being applied across various facets of healthcare and life sciences. Beyond drug discovery, AI is being used in:

• Medical Imaging: Assisting radiologists in detecting anomalies in X-rays, CT scans, and MRIs.
• Diagnostics: Analyzing patient data to help diagnose diseases more accurately and earlier.
• Personalized Medicine: Identifying genetic markers and other factors to tailor treatments to individual patients.
• Clinical Trial Optimization: Improving patient recruitment, trial design, and data analysis.
• Epidemiology: Predicting disease outbreaks and tracking their spread.

The application of AI in drug discovery, however, holds the potential for one of the most transformative impacts: enabling the development of new therapies for diseases that are currently difficult to treat, or developing existing therapies more quickly and affordably. Success in this area could lead to a new era of precision medicines and potentially tackle long-standing health challenges.

Companies like Isomorphic Labs are not just building tools; they are attempting to fundamentally change the economics and timelines of bringing life-saving and life-improving drugs to patients. Reducing the time and cost, while increasing the probability of success, could make previously unfeasible drug targets viable and accelerate the availability of new treatments.

Challenges and the Road Ahead

Despite the immense promise and the significant progress made by Isomorphic Labs and others in the field, challenges remain. The complexity of biological systems is staggering, and predicting how a molecule will behave in the human body is incredibly difficult. AI models require vast amounts of high-quality data, which can be challenging to obtain and curate in the pharmaceutical space.

Regulatory hurdles are also significant. AI-designed drugs will need to meet the same rigorous safety and efficacy standards as traditionally discovered drugs. Regulatory bodies are still developing frameworks for evaluating therapies developed using AI, which could introduce complexities and delays.

Furthermore, the "black box" nature of some advanced AI models can make it difficult to fully understand *why* a model makes a particular prediction or design choice. In drug discovery, where understanding mechanisms of action and potential off-target effects is crucial, this lack of interpretability can be a challenge.

However, the progress made by Isomorphic Labs, culminating in the imminent launch of human trials, suggests that these challenges are being actively addressed and overcome. The collaboration model with experienced pharma partners is likely key to navigating the regulatory and clinical complexities.

The move to human trials is the ultimate test. Success in these trials would not only validate Isomorphic's AI platform but also pave the way for wider adoption of AI-driven approaches across the pharmaceutical industry. Failure, while a setback, would provide invaluable data for refining the AI models and the drug design process.

Conclusion

Isomorphic Labs stands at a pivotal moment. By leveraging the foundational breakthroughs of DeepMind's AlphaFold and combining them with targeted AI development for molecular interaction and design, the company has positioned itself at the cutting edge of AI-driven drug discovery. The impending launch of human clinical trials for its AI-designed drug candidates is a landmark event, representing the transition from theoretical potential to real-world application in the most critical phase of drug development.

The partnerships with industry leaders like Novartis and Eli Lilly, coupled with substantial financial backing, provide Isomorphic with the necessary resources and expertise to navigate the complex path ahead. While challenges remain, the prospect of using AI to dramatically accelerate the discovery process, reduce costs, and increase the success rate of finding new medicines offers a compelling vision for the future of healthcare.

If successful, Isomorphic Labs' efforts could significantly shorten the time it takes to bring life-saving therapies to patients, making treatments for currently intractable diseases a reality sooner than previously imagined. The journey from a disease target to a marketable drug is long and fraught with difficulty, but with advanced AI tools now entering the clinical phase, that journey may be about to become significantly more efficient and successful.

The world watches with anticipation as Isomorphic Labs takes this crucial step, potentially ushering in a new era of AI-powered medicine where the dream of rapidly designing drugs to combat specific diseases moves closer to reality.

The potential implications for global health are enormous. Faster, cheaper, and more successful drug discovery could lead to treatments for rare diseases that were previously uneconomical to pursue, more effective therapies for common ailments, and quicker responses to emerging health crises. Isomorphic Labs is not just developing drugs; it is helping to build the future infrastructure of pharmaceutical innovation.

As the company staffs up and finalizes preparations, the scientific and medical communities are keenly aware that the outcomes of these initial human trials could provide critical validation for the entire field of AI-driven drug discovery. The journey is far from over, but the fact that AI-designed molecules are now entering human testing is a powerful indicator of the progress being made and the transformative potential that lies ahead.

The collaboration between AI researchers and pharmaceutical experts within Isomorphic Labs, supported by partnerships with industry leaders, exemplifies the multidisciplinary approach required to tackle the grand challenges in medicine. The next few years, as these AI-designed drugs progress through clinical evaluation, will be critical in determining the extent to which artificial intelligence can truly revolutionize the way we discover and develop the medicines of tomorrow.

The promise is clear: a future where diseases that are currently devastating might be treated with therapies designed with unprecedented speed and precision, thanks to the power of AI. Isomorphic Labs is on the front lines of making that future a reality.

This milestone is not just about one company or one set of trials; it represents a paradigm shift in how we approach the fundamental challenge of creating new medicines. If successful, AI could become an indispensable partner in the fight against disease, accelerating the pace of medical innovation and bringing hope to millions around the world.

The journey from a groundbreaking AI model like AlphaFold to a drug being tested in humans is complex and requires overcoming numerous scientific, engineering, and regulatory hurdles. Isomorphic Labs' progress is a testament to the dedication of its team and the potential of AI when applied to real-world problems of immense importance. The coming months and years will be crucial in observing the outcomes of these trials and understanding the full impact of AI on the future of pharmaceuticals.