© 2022 ISOMORPHIC LABS
Thanks to advances in Machine Learning, right now, it feels like humanity’s understanding, development, and application of technology is growing exponentially.
With the scale and pace of change the world has witnessed in the last few decades, it’s a privilege to have played a very small part in this journey, working on a range of unexplored, complex, once seemingly impossible challenges of the new technological age.
Collaborating with a team of brilliant colleagues, I helped build Amazon’s first Canadian software engineering organisation, tackling the challenges of operating a global fulfilment network, the foundations of which are still in use today. Leading the Technical Working Group of the Pan Cancer Analysis of Whole Genomes project I helped collect and analyse the world’s cancer genome data to better understand mutational processes that underlie this disease. While serving as the CTO of SOPHiA GENETICS, we analysed over one million genomic profiles, from more than 70 countries, building a unique computational platform that allowed a more comprehensive understanding of the relationship between genetics and disease.
Still, the opportunity to join Isomorphic Labs as its first Chief Technology Officer felt incredible, even within the context of the innovation I contributed to throughout my career. As I spoke to the recruitment team and learned more about the company, it became clear that coming across a team, a mission, and a technical challenge like this happens once in a lifetime - or never.
As I think about the year that’s gone by since I joined, and everything I’ve learned in the process, the reasons that compelled me to join the team are even more exciting, motivating and fulfilling today.
Driven by our mission
Although our goal is to revolutionise the drug discovery process, this is not our ultimate mission. No matter who you are, or what you do, we have all witnessed the toll disease can take on us and our loved ones.
Many of us decided to join after asking ourselves “who are we doing this for?”. For me, it's the family members or friends who might one day rely on medicines that are yet to be discovered to improve their quality of life or even save their lives.
Many of us are similarly driven by the desire to apply our skills, expertise, and the technology that is pushing the boundaries of innovation, to a mission that has a clear positive impact beyond its own commercial viability. Put quite simply, we want to use technology to bring better healthcare to everyone in the world.
Machine Learning at the core
Advances in Machine Learning (ML) are permeating our lives, yet it feels like there is so much more to come. I liken the feeling to that of the internet in the early 2000’s. There seemed to already be a website for just about everything, but it was hard to imagine just how integral the internet would become to our lives and society in the present day.
And similarly, we already see the ubiquity of ML models in virtual assistants, recommender systems, self-driving technology, and a myriad other applications. In the past year alone there’s been an incredible surge of generative models in art, gaming, and text. Yet applications of ML, and especially deep learning, to science, appear to still be in their early days.
Scientific progress is rarely easy, owing to the complexity of biological systems and our limited ability to comprehensively interrogate them. Amazing breakthroughs are nevertheless possible if one is able to match up the right scientific question with a carefully constructed dataset, an appropriate model, and robust engineering to take it large-scale.
DeepMind’s AlphaFold2 (AF2) is a prime example of the power of deep learning, applied with mastery to a well-posed scientific problem, unlocking predicted structures of over 200 million proteins.
Here at Iso, we are devout users of AlphaFold 2, forming a strong partnership with the DeepMind Science team. But AF2 alone is not enough to solve drug discovery. What we need is a coordinated set of models that can reason comprehensively at multiple levels of chemical and biological systems - modelling how molecules work and interact with each other at the lowest level, how molecules aggregate to form organelles and entire cells along with their behaviour, how cells give rise to complex organisms, and finally how these organisms interact with their environment.
To achieve this lofty task we are placing ML at the core of our endeavour, building the world’s strongest applied ML research, data, and engineering teams and throwing the full weight of our experience, creativity and resources behind these problems.
Building an interdisciplinary approach
Though ML is our superpower, solving scientific problems at this scale really takes a village. Even the most sophisticated AI algorithms are helpless if they are not grounded in firm scientific reality, one in which intuition and expertise takes years to develop.
This is why we are building our team in a truly interdisciplinary manner, bringing together computer scientists, engineers, mathematicians, chemists, biologists, physicists and many other disciplines, to move the mountain together. This means having shared goals, a common space to physically be present in, spaces to have fun and hang out in, and interdisciplinary jams to freely share ideas and educate each other.
Importantly, our research and engineering teams have been only one piece of the talent puzzle Isomorphic Labs is solving for. Given the scale and the complexity of the challenge, we’re also thinking critically about finding the right talent across a number of professions - including operations, law, finance and HR - that will enable us to build an entire company that can be innovative, flexible, and creative enough to succeed in our mission.
A startup environment within Alphabet
As we continue to grow, we’re no doubt still developing the unique collaborative environment that will underpin Isomorphic’s work. In doing so we take a considered approach reflecting our part of a larger whole of the Alphabet ecosystem of companies.
Unlike a traditional startup, in getting off the ground, Isomorphic Labs has enjoyed the basic business structures that make a company work, provided by Alphabet from the start. This has allowed the team to devote much of their focus to the science and how we work together as a group. Thanks to this support, we can be more ambitious in our work, make mistakes, and learn in the process.
We also have access to technological resources at an unprecedented scale. You need an incredible amount of computing power to develop cutting-edge ML models, and Alphabet has been able to provide us with this, and many additional internal and external tools to aid us on our journey.
Looking ahead
It’s exciting to imagine how much is possible as we continue to build this company! The scientific and technical problems we are solving are ambitious, meaningful, and incredibly interesting.
Reaching our goal, however, will in part depend on us exploring the various aspects of how we create an environment that will enable us to do amazing things: from how we build the most diverse team possible; to how we collaborate to make scientific breakthroughs; to how we provide opportunities for colleagues to fuel their creativity outside the office, we are resolved to figuring out how to do what’s necessary to accelerate this transformative journey. I’m looking forward to the year ahead, when we’ll be even closer to our destination.
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Isomorphic Labs, a subsidiary of Alphabet, today announced the first phase of its management team. The company, established in November, was launched from Alphabet’s DeepMind to build on the success of AlphaFold, DeepMind’s work in protein folding that was heralded as the “Breakthrough of the Year” by Science and “Method of the Year” by Nature in 2021. Isomorphic Labs’ mission is to use AI and machine learning methods to accelerate and improve the drug discovery process. The company is a pioneer in the emerging field of “digital biology” and aims to usher in a new era of biomedical breakthroughs in order to find cures for some of humanity’s most devastating diseases.
The new leaders join founder and acting CEO Demis Hassabis, who is currently serving as CEO of both DeepMind and Isomorphic Labs. Together they comprise a truly multidisciplinary leadership team with expertise in domains across science, engineering, machine learning and business operations that will be needed to achieve the company’s goal of dramatically improving how drugs are discovered and developed. Drug development is currently a lengthy, expensive and high-risk process that is poised to benefit from advances in the application of AI and computational methods. Studies peg the cost of bringing a molecule to market at more than $2.5 billion per drug and timelines approaching a decade; in fact, only about 12 percent of drugs entering clinical trials are ultimately approved by the U.S. Food and Drug Administration (FDA).
“We’re thrilled to announce the formation of our extraordinary multidisciplinary leadership team that will allow us to deliver on our ambition to reimagine the entire drug discovery process from first principles, with an AI-first approach,” said Hassabis. “The phenomenal success of AlphaFold demonstrated the huge impact that AI methods can potentially have on biology, and we plan to build powerful new predictive and generative models of biological phenomena to anticipate how drugs will perform and design novel molecules.”
The new additions to the Isomorphic Labs team include:
Dr. Congreve joins as Chief Scientific Officer from Sosei Heptares in the same role, where he pioneered Structure-Based Drug Design for G Protein-Coupled Receptors (GPCRs). Previously he was director of chemistry at Astex Pharmaceuticals, helping to establish Fragment-Based Drug Design as a radical new approach to small molecule lead generation, and team leader at GlaxoSmithKline in medicinal chemistry and chemical technologies. In his 28-year career, Dr. Congreve has contributed to the design of 20 clinically evaluated drugs and he is co-inventor of Kisqali® (Ribociclib), a marketed treatment for breast cancers. He is co-author of over 180 publications and patents. In 2015, he was co-recipient of the RSC Malcolm Campbell Memorial Prize for the seminal contributions to GPCR drug discovery made by Sosei Heptares. Dr. Congreve has a degree in biological chemistry from Leicester University and a PhD in synthetic chemistry from Cambridge University. He is also a fellow of the Royal Society of Chemistry.
Dr. Yakneen joins as Chief Technology Officer, bringing over 20 years of experience in engineering, machine learning, product, life science, and medical research. Prior to this role he was senior vice president and chief technology officer at SOPHiA GENETICS, where he was responsible for the development and operation of a global AI-based molecular diagnostics and imaging software platform operating in more than 70 countries. He’s held senior roles at Amazon, where he launched the first Canadian software engineering center; at BPS Inc., where he developed a best-in-class Governance, Risk, and Compliance platform; at the Ontario Institute for Cancer Research, where he led the Technical Working Group of the Pan Cancer Analysis of Whole Genomes Project, the world’s largest cancer data analysis initiative; and at EMBL where he developed a cloud-based scientific workflow framework. Dr. Yakneen holds a degree in computer science and mathematics from the University of Toronto and a PhD in computer science from Heidelberg University, where he developed novel distributed algorithms for analyzing cancer genomes.
Dr. Jaderberg joins as Director of Machine Learning from DeepMind, where he was a research scientist, leading the Open-Ended Learning research team and pioneering numerous algorithms bringing together large-scale deep learning and reinforcement learning to achieve breakthrough results with AI. Prior to that, he was CEO and co-founder of Vision Factory, a company that specialized in image recognition technology with deep learning, which was acquired by Google in 2014 and subsequently became part of DeepMind. He is widely published in the top journals and conferences, with work now part of textbooks. His research interests are in AI, deep learning, reinforcement learning and generative modeling. Dr. Jaderberg completed his undergraduate degree in engineering science at the University of Oxford and his PhD with the Visual Geometry Group at the University of Oxford, where he developed state-of-the-art algorithms for image understanding.
Ms. Richenburg joins as Director of People Operations from Eigen Technologies, where she was senior vice president of people. She is skilled in the development and implementation of innovative people strategies that drive business growth and performance. She has held a variety of human resources roles at companies including Meridian Life Sciences, Bioline and Evotec. Ms. Richenburg is a graduate of the University of Edinburgh with a bachelor’s degree in chemistry, medicinal and biological chemistry and earned a master’s degree in human resources management from Oxford Brookes University.
Join us
The company is continuing to scale and is currently seeking talent at various levels with a focus on scientific, engineering and operational roles, along with biologists, medicinal chemists, biophysicists, clinicians, computational scientists, and machine learning experts to contribute to our mission of using AI to accelerate the drug discovery process. For information on open roles, please visit: www.isomorphiclabs.com/join.
Isomorphic Labs is a subsidiary of Alphabet that was launched from Alphabet’s DeepMind in 2021, with headquarters in London. It was founded and is led by AI pioneer Demis Hassabis, who also co-founded and leads DeepMind. As pioneers in digital biology, the company’s mission is to use AI to accelerate drug discovery and ultimately find cures for some of humanity’s most devastating diseases. Using its AI-first approach to drug discovery and biology, the company’s ambition is to advance a new era of medical breakthroughs. For more information, go to www.isomorphiclabs.com.
