Coulter Partners recently talked to Dr James Field, Chief Executive Officer and Founder of LabGenius about digital disruption and talent in drug discovery.
Coulter Partners: Do you see LabGenius as a technology company or a drug discovery company?
James Field: We see ourselves as a drug discovery company but one that we’re intentionally shaping in order to get the maximum benefit from technology. Specifically, we view the challenge of drug discovery as a systematic search problem rather than a hypothesis-led process of exploration. Over the years, we’ve found that to really harness technologies like machine learning (ML) in the discovery process, you can’t just throw historic sets of experimental data at data scientists, instead, you need to completely re-work the underlying workflows so that they produce robust ML-grade data. Achieving this takes not only time and resources but also an organisational structure that breaks down silos and encourages data scientists and bench scientists to work hand-in-hand. With this philosophy, we’ve been able to harness a totally new data-driven approach to protein engineering.
CP: Can you elaborate on what that means for the structure of your organisation?
James: We’ve developed a protein engineering platform that tightly integrates synthetic biology, machine learning, robotic automation and next generation sequencing. To achieve this, we’ve built a 50-strong team of data scientists, software engineers, automation engineers, drug discovery experts, molecular biologists, synthetic biologists… the list goes on. Domain experts on all sides have rarely been in the position where they have had to communicate across such disparate disciplines. Our greatest challenge is one of organisational engineering: how to structure diverse teams of scientists and engineers to create value at the confluence of so many disparate technologies. As a rapidly growing organisation, this is an on-going challenge rather than something that you can solve and then move-on from.
CP: How has the current pandemic affected this plan?
James: In the first wave of lockdown the general expectation was that we’d lose a quarter of our productivity and yet it ended up being our most productive quarter. We decided to really focus on what had to be done to move the needle and put everything else to one side – the same challenge faces all start-ups. As a company that has invested heavily in automation, we’ve also had the benefit of being able to run a huge number of experiments relative to the number of people on-site. What’s more, our automation team has developed a setup that enables everyone working from home to see the robots operating in real time. It’s small things like this that really help those of us who can’t be on-site to feel close to the science.
CP: What do you think you may have lost during this time? Is innovation still achievable in these circumstances?
James: The main challenge of lockdown is that it introduces micro-frictions into everyday interactions. Where you previously would have shared a conversation over a cup of tea, you now need to schedule time to “shoot the breeze”! For a company that thrives on innovation and multidisciplinary learning, this is a huge challenge. Over the course of the pandemic, we’ve tried to embed a culture where teams are able to have those water cooler moments in a hybrid environment that is both on-site and remote. We haven’t entirely solved this yet but are taking intentional steps to tackle it.
The flip side is that a forced switch to hybrid working has given me pause to think about the business in a whole new way. We have demonstrated that we can operate as a partially distributed company and this has opened up new talent pools we wouldn’t have been able to access before. We hope to carry many of these learnings and ways of working forward post-pandemic.
CP: At a high level what needs to change to accelerate advances in AI and drug discovery and is there a tipping point?
James: When you are deconstructing and reconstructing the drug discovery process to include fundamentally different technologies, these have to compete against established methods that work exceptionally well. The only way to surmount this challenge is through significant and sustained investment. In the near-term the biggest breakthroughs are most likely to come from well-capitalised companies operating in problem spaces where conventional drug discovery methods have really struggled. As this new generation of platform companies gain traction and existing pharma companies remodel themselves, these technologies will seep into everyday modes of operation.
CP: What will create the biggest impact in the AI and ML landscape over the next ten years?
James: This is an industry where large incumbents have established and maintained strong market positions through vertical integration. These pharma companies are now facing a major challenge. Significant and sustained venture financing has led to the creation of hundreds of tech-enabled companies that are successfully capturing market share at every point in the value chain. There are also a growing number of exceptionally well-financed start-ups that are going for full vertical integration and building what is likely to become the next generation of pharma companies. These dynamics have already catalysed a process of change in large pharma companies and as part of this, we’re likely to see several large acquisitions as they acquire and integrate new technologies and ways of working.
CP: When it comes to machine learning and AI, what are you seeing in the current market for talent? How has the pandemic accelerated change?
James: Skilled machine learning and artificial intelligence specialists working in more conventional tech companies are asking themselves the question, “do I really want to be using my abilities to optimise the amount of time that people spend looking at screens?” In part precipitated by the pandemic, there has been an exodus of talented engineers from large tech organisations to more purpose-driven start-ups like LabGenius.
CP: Can you tell us more about your plans and the LabGenius road map for the future?
James: If we were having this conversation from LabGenius HQ, you’d see a large, highly automated facility where liquid handling robots conduct experiments that have been designed by data scientists. We’re using this capability for drug discovery programmes in both inflammation and oncology. We’re about 50 strong today and envisage growing to 70 people over the next 12 months. We’ll use our next round of financing to double-down on our internal pipeline and to keep pushing forward with cutting-edge platform development work.
CP: When you think about your talent needs now and into the future, where are the biggest challenges, particularly at the leadership and C-suite level?
James: Running LabGenius is my first job so it has been a steep learning curve! On the hiring front, I initially indexed really highly a candidate’s existing skills but quickly realised that this is far less important than their ability to quickly learn outside their realm of expertise. We have been very successful in hiring people who are domain experts in one area but have climbed steep learning curves across several different areas outside their comfort zone. People who don’t pigeon-hole themselves into one area but are intellectually curious are invaluable to a company like LabGenius. Secondly, we look for highly collaborative people. While a bright spark of innovation from one genius can sometimes lead to a huge breakthrough, scientific discovery at LabGenius is a team sport. We absolutely depend on people being able to work in a cross-functional way.
On the senior management team, the same principles apply – we look for people who can provide functional leadership but are highly collaborative as well. Another key attribute is the capacity to be reflective and we screen carefully for this. In a senior management team, I think it’s especially important that members have a good feel for each other’s strengths and weaknesses and proactively lean-in to support each other.
CP: Do you benefit because your team is very diverse by nature?
James: Absolutely. I’ve spoken already about the importance of technical diversity but something that’s just as important is diversity of thought, experience and background. We each bring our own unconscious biases to the table and it’s only through building a diverse team that you can challenge these and find the best solutions. It is refreshing to adopt a new perspective – hiring the non-conformist, for instance, over the double first from Cambridge because they are the best candidate for the role. If you only hire in your own image you are likely to compound weaknesses. Diversity helps to build better teams that make better decisions.
CP: How do board and external advisors support your decision making?
James: Like many scientist founders, my steepest learning curves have been on the operational side. I have been incredibly fortunate to work with a very experienced board and advisors who have dramatically accelerated my progress on the journey. Whether this takes the form of hands-on mentoring to understand challenging operational situations in biopharma and the intricate business of drug discovery, or whether it consists in coaching that is based on a life-time’s experience of building high-performance teams in the tech world, a great deal is gained from these top-level relationships. Our investors are always providing invaluable insights drawn from their deep experience working at the interface of biotech and tech.
Of course, everyone at LabGenius is on their own learning journey and we’ve tried hard to build a culture that recognises and embraces this.
LabGenius is discovering next generation protein therapeutics using an innovative machine learning-driven evolution engine (EVA™). EVA™ is a next generation protein engineering platform underpinned by machine learning, robotic automation and synthetic biology. The organisation is using deep-learning neural networks to explore protein fitness landscapes and improve multiple drug properties simultaneously. The company is staffed with approximately 50 Synthetic Biologists, Software Engineers, Data Scientists and Automation Engineers. LabGenius is a London-based, privately-owned company, backed by top-tier deep tech investors. To date the company has raised over $25 million from some of Europe’s and the US’s leading investors, including Atomico, Lux Capital, Obvious Ventures and Felicis. The company was founded by Dr James Field, the winner of many prestigious awards; BBSRCs Innovator of the Year award for early career impact and a listing on the Forbes 30 under 30 list for Science & Healthcare.
For more information, please visit www.labgeni.us
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