Shaping Open Targets’ research programme for the next decade
We talk to Gosia Trynka, Science Director of Open Targets and Group Leader at the Wellcome Sanger Institute, about how the scientific direction of Open Targets has evolved since the consortium was founded, and what the future might hold.
This is the second part of a set of posts looking back at how the partnership has changed over the past decade, and how it might evolve in the future. The accompanying piece features our interview with project lead Lewis Evans, and the collaborative nature of his project investigating neurodegeneration.
Helena Cornu
How have Open Targets projects changed in the past decade?
Scientifically, the programme has always been anchored on gene-disease causality that we can leverage from genetics. But since the foundation, Open Targets projects have grown in sophistication and complexity.
Open Targets was founded to foster ambitious, systematic projects at scale that address questions comprehensively. This remains a core tenet of project generation, and as the consortium has grown and partners' connections have expanded, Open Targets projects have naturally become more layered and multidisciplinary.
Where we used to talk about an informatics research programme and an experimental research programme, we now see it as a unified Open Targets research programme, with overlaps between therapy areas and approaches, and projects interconnecting. Large-scale experimental projects are intertwined with new analytical approaches, and sophisticated computational methods require high-quality data for training and validation. Rather than opportunistically, we evolved to drive these efforts more actively.
Science progresses faster when you have better integration, a flow of information, and a stronger community, where people can bounce ideas off one another and borrow concepts from one another. The ultimate goal is to create a loop where the data generated and technologies applied in one project can inform many others.
Why has this change happened?
I think we have achieved a critical mass. We have a large enough consortium now that we can afford to pursue coordinated efforts across three therapy areas, and we have a more refined understanding of how to maximise the value of partner interactions.
What has influenced the direction of science at Open Targets?
Three main factors have shaped our scientific direction.
First, over the past decade, extensive research has highlighted the importance of integrating genetic evidence into drug discovery programmes, including several Open Targets studies showing that genetic support is both predictive of drug approval, protective against early clinical trial stoppage, and informative of safety risks through measures of genetic pleiotropy.
The first genome-wide association study (GWAS) was published in 2002. It provided a successful framework for mapping genetic variants underpinning human traits. As the number of studies increased, so did the realisation that causal biology that is captured by genetics can more efficiently inform drug discovery. However, in 2014, when Open Targets was initiated, this was a budding idea, and many industry partners were not putting that much weight on genetics in their drug discovery programmes. This has dramatically changed; broadly, most of the industry now appreciates the importance of genetics in drug discovery. As a result, it had a substantial impact on the science we’ve been doing at Open Targets.
The Wellcome Genome Campus itself has been the second defining influence. It is a unique environment where new scientific approaches are developed, scaled, and shared at pace. Being embedded in this setting means that Open Targets both responds to external research trends and helps to set them. By working at the intersection of world-leading academic innovation and industry needs, Open Targets harnesses rapidly evolving science and actively shapes the direction of research that underpins drug discovery.
A third defining influence has been the evolving relationship between academia and industry within the Open Targets partnership itself. Open Targets projects are delivered by teams across partner academic institutes, working in close collaboration with researchers from industry. Over time, this has evolved from a way of organising projects to actively shaping how science is done on both sides. The active dialogue between academic and industry scientists sparks original approaches, for example, industry partners often adopt assays, cell models, or analytical pipelines developed in academic labs, without needing to divert resources into such foundational work themselves. Conversely, academic teams are encouraged to view their research through a translational, drug discovery lens, which is not always the natural direction for academic science but can unlock new opportunities.
Looking back, it is striking how much the scientific landscape has shifted in both academia and industry, reshaping our collective thinking about current and future directions. Advances in genetics and genomic technologies, the scale at which we generate data from omics assays, the increasing multi-modality of readouts at high-throughput, the development of a range of genomic perturbation tools and their deployment for large-scale screens, and the rapid progress of machine learning are all transforming how we approach drug discovery. Open Targets sits at the intersection of these changes between both academia and industry, not just adapting to them, but actively helping to shape the next chapter of biomedical research in the context of drug discovery.
What is Open Targets’ strategy in developing and awarding new projects, and has that changed?
In some ways, project generation felt more straightforward in the early years. There were clear gaps to fill, linking genetic variants to genes, building foundational datasets, and establishing approaches that could connect genetics to biology. Today, the landscape is more complex. We are not short of ideas. If anything, the challenge is deciding which ones to pursue.
Over time, our approach has become more deliberate. We think carefully about how individual projects fit into the broader programme, how they connect to each other, and how they contribute to building a coherent body of data and insight.
Systematic projects remain incredibly important. These are the efforts that generate large, harmonised datasets, essential for enabling downstream discovery. At the same time, we continue to invest in more exploratory and higher-risk ideas, particularly when new technologies or approaches have the potential to shift how we understand biology. Striking the right balance between these two is not always easy. Highly innovative projects can be transformative, but they also carry risk and may take time to mature. Systematic efforts, on the other hand, provide stability and long-term value. Both are necessary. We have also seen a shift towards fewer, larger, and more integrated projects. Rather than funding many smaller, independent efforts, we increasingly prioritise programmes that bring together multiple capabilities—combining perturbation, deep phenotyping, and computational analysis to address a question more comprehensively.
Another important consideration is timing. Not all technologies are ready to be deployed at scale. Part of our strategy is deciding when to invest in developing a new approach, and when to apply existing technologies more broadly. The strength of the programme lies in being able to do both.
Throughout this process, the partnership model remains central. Project ideas are shaped collaboratively, with input from both academic and industry partners. This ensures that projects are scientifically ambitious, but also grounded in questions that are relevant for drug discovery.
Where do you see the scientific direction of Open Targets heading?
One of our biggest challenges now is moving beyond disease risk to really understanding disease biology—how it develops, how it progresses, and what is actually happening in cells. That means investing more in patient sample profiling, so we can study biology in real disease contexts, and linking that to experimental systems where we can start to test and interpret what we see.
More broadly, the field still struggles to interpret biology in a systematic way. That is a big part of why there is a gap between genetic discovery and translation. Biology is complex and context-dependent, and not easy to model. There is a lot of excitement around AI and machine learning, but those approaches will only be as good as the data behind them. For us, that means continuing to focus on generating the right kinds of datasets.
A big part of that is cell biology and perturbation. Genetics can take us from variant to gene, but the key question is what that gene actually does in different cell types and conditions. Systematic perturbation datasets help us get at that by anchoring our data in causal biology, rather than just association.
We are also in a strong position because of what is happening on Campus. New efforts like generative genomics, findings from somatic genomics at Sanger, and growing AI-focused work at EMBL-EBI are all shaping how we think about data generation and interpretation.
The opportunity for Open Targets is to bring these pieces together—patient data, perturbation biology, and computational approaches—in a way that helps us move more confidently from genetic signals to biological understanding.
Helena Cornu
