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There’s no shortage of old maxims telling us that we should always be willing and able to learn something new, rather than simply applying what we already know.
Few people, however, talk about how difficult this can actually be.
Sometimes it’s our very expertise that can be one of the biggest obstacles to continual learning. In many environments, people like me are heavily utilised for our knowledge, but don’t always have the opportunity or the bandwidth to be a student as much as a teacher. Particularly in fields like science, researchers can devote decades of their lives to one topic or method of exploration. We are called upon to contribute from our areas of expertise, and get used to being the expert in the room.
Due to that very expertise, it can be uncomfortable to admit what we don’t know or to make the shift from expert to novice. But not allowing for that discomfort prevents us from breaking down the silos that are essential to really transforming the way things have traditionally been done.
Achieving our mission of revolutionising drug discovery at Isomorphic Labs will, of course, depend on our ability to attract the best talent in chemistry, biology and computer science - but these individuals will not be able to work only in the domain in which they are experts for us to reach our goals. Our task is interdisciplinary by nature: success will depend on combining the expertise of subjects and methods which at times couldn’t be more different. We have to learn from each other, which requires all of us to step out of our comfort zone and into the intellectual vulnerability of our respective unknowns.
Using myself as an example, I’m not a mathematician. I marvel at the speed at which our machine learning team does their work, runs their simulations and can assess the probabilities of multiple outcomes. Having long experience within my own field, it's both humbling and exciting to adopt a beginner’s mindset and to immerse myself into what others in different areas might think are the basics.
Reaching beyond our comfort zones requires some personal resilience. We’re striving to create an environment where colleagues at Isomorphic Labs feel like they have permission to “not know”. This starts with trust, and so spending time to get to know each other as people has been a key part of allowing us to connect, appreciate our different backgrounds and understand what makes us tick.
We’re also focused on hiring people who are naturally willing to be generous with their time to teach their co-workers what they know. For every moment where I’ve struggled with an unfamiliar topic, I can think of many others where someone has graciously shared their knowledge, helping me see a challenge through the lens of their expertise. Some of this has been as informal as me pulling up a chair to talk to them about what they’re doing; others have been through more formal “tutorial” sessions where a member of the Isomorphic Labs team talks in more depth about their field.
Finally, we’ve thought carefully about how we create room for mistakes to be made when doing the work that will get us closer to achieving our mission. The reality is that today we get more things wrong than we get right in drug design. Fostering a culture where individuals can feel comfortable enough to work together and learn without the added pressure of being penalised for making a mistake is critical for us to be effective in working towards our goal.
One way we’ve been testing this out has been through creating “sandboxes,” akin to the approach taken by our colleagues at DeepMind when they developed AlphaGo. Here, machine learning capabilities were developed and enhanced with a striking and measurable outcome. At IsoLabs, we have established simple drug design settings to test our new platforms quickly and unambiguously. This has also allowed us to build strong relationships and learn from what’s less familiar, working together to explore approaches that are required to achieve our ambition.
We think of the process of drug design as a guided search, where we’re drawing on what we learn along the way to navigate a chemical landscape of almost infinite possibilities, and land on the optimal combination. And similarly, I find that my experience at Isomorphic Labs mirrors that process through the generosity and enthusiasm of my colleagues from other disciplines. This isn’t about any one person’s efforts - this is a collective endeavour for a collective purpose. And it’s great to work in an environment where, no matter how deep our expertise, we all continue to learn from one another whilst working towards a shared and greater mission.
Interested in joining Miles’ team? Register your details here
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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.
