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Relation Therapeutics

Drug discovery platform using ActiveGraph ML

Research Assistant - Cell Biology

Full-TimeUpdated
No salary listed
Mid
Bachelor's, Master's
London, UK
In Person
Biology & BiotechTranslational Research & Bench Biology

About the job

Requirements
  • Bachelor's and/or Master's degree in a Biological Science.
  • Prior wet-lab experience in an academic, biotechnology, pharmaceutical, or contract research organization environment.
  • Strong practical experience in cell culture techniques, including cell line generation and maintenance.
  • Experience running arrayed genetic perturbation screens using CRISPR.
  • Capability to run cell and molecular biological assays to characterise cell phenotype, such as nucleic acid extraction, flow cytometry, reverse transcription quantitative polymerase chain reaction, and enzyme-linked immunosorbent assay.
  • Competence in data handling, curve-fitting, and statistical analysis.
  • Experience in industry-standard record keeping.
  • Ability to work in a collaborative, cross-functional, matrixed environment.
  • Motivation, scientific rigor, and ability to operate in a fast-paced, evolving biotechnology setting.
  • Ability to balance multiple stakeholders and contribute effectively across teams.
  • Take ownership of work, proactively seek opportunities to contribute, and enable others to do their best work.
  • Communicate openly and directly, give and receive feedback constructively, and handle challenging conversations respectfully.
  • Seek diverse perspectives, build strong working relationships, and contribute to shared goals across teams.
  • Approach challenges with openness and resilience and set high standards for oneself.
Responsibilities
  • Contribute to the design and execution of cellular and biochemical assays to support ongoing projects, including screening in established cell models and running phenotypic readouts.
  • Grow and maintain primary and immortalised mammalian cell lines, and take responsibility for sample tracking and quality control.
  • Assist with reagent stock management, equipment maintenance, laboratory upkeep, and adherence to good laboratory practice.
  • Carry out data analysis and relevant quality assurance protocols during routine protocols.
  • Communicate work outcomes to project team members and the broader company in written or verbal reports.
  • Identify potential project concerns proactively and communicate them promptly to senior project team members.
  • Contribute ideas for new technologies, workflows, and operational infrastructure.
  • Collaborate with colleagues across biological and computational sciences to help solve scientific challenges and accelerate programme delivery.
Desired Qualifications
  • Experience with fibroblast, endothelial, and/or epithelial primary cell lines.
  • Prior use of walk-up automated laboratory systems, such as liquid handling.
  • Hands-on experience with high-content imaging instrumentation and software.
  • Working knowledge of multicellular models.
  • Experience contributing to a multi-party collaborative project.

About the company

R

Relation Therapeutics

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Relation Therapeutics uses machine learning to speed up drug discovery by combining a large-scale ActiveGraph ML platform with a Lab-in-the-Loop approach. Its system analyzes how genes, proteins, and drugs relate to each other to understand biology and predict promising therapeutics, using real cells from proprietary biobanks to generate genomic data that continuously informs new experiments. It differs from others by applying ActiveGraph ML at a large scale in drug discovery and integrating active learning with experimental validation in a closed loop. Its goal is to discover treatments for currently untreatable diseases and license successful candidates to pharmaceutical companies for development and commercialization.

Company Size

51-200

Company Stage

Late Stage VC

Total Funding

$139M

Headquarters

London, United Kingdom

Founded

2019

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Simplify's Take

What believers are saying

  • GSK’s July 30, 2026 collaboration reaches $110 million, funding Relation’s data factory.
  • Novartis deal and Deerfield collaboration extend capital, partnerships, and downstream NewCo optionality in 2026.
  • Relation’s proprietary human-cell datasets create switching costs competitors cannot replicate quickly.

What critics are saying

  • Relation still lacks a disclosed clinical asset; MORGAN failure would expose a pure-platform story.
  • GSK and Novartis control downstream development rights, limiting Relation’s long-term economics and control.
  • AI drug discovery is crowded; if petascale datasets miss predictive targets, Relation becomes expendable.

What makes Relation Therapeutics unique

  • MORGAN, launched July 30, 2026, trains on petascale perturbation biology, not static correlations.
  • Relation’s Lab-in-the-Loop couples automated wet labs with AI, tightening target discovery feedback loops.
  • GSK and Novartis pay for Relation’s human cellular data engine, validating the platform’s uniqueness.

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Benefits

  • Hybrid Work Options

Growth & Insights and Company News

6 month growth

-2%

1 year growth

0%

2 year growth

0%
U.S. Securities and Exchange Commission
Oct 25th, 2026
EDGAR Filing Documents for 0001876828-21-000001

EIN.: 000000000 | State of Incorp.: DE | Fiscal Year End: 1231Type: D | Act: 33 | File No.: 021-409358 | Film No.: 211158504

GlobeNewswire
Oct 14th, 2026
Cloud Based Drug Discovery Platforms Market - global trends, Growth, and Forecast to 2035 | AI adoption and Hybrid Cloud demand drive 11.7% CAGR toward USD 11.4 billion.

Cloud Based Drug Discovery Platforms Market - global trends, Growth, and Forecast to 2035 | AI adoption and Hybrid Cloud demand drive 11.7% CAGR toward USD 11.4 billion. Boost ROI through cost-saving cloud adoption, provider benchmarking, strategic partnerships, and investment in SaaS, hybrid cloud, AI-enabled workflows, and Asia-Pacific expansion. Dublin, Oct. 14, 2026 (GLOBE NEWSWIRE) - "Cloud Based Drug Discovery Platforms Market - Trends and Forecast Till 2035" has been added to ResearchAndMarkets.com's offering. The global cloud based drug discovery platforms market is projected to grow from USD 4.2 billion in the current year to USD 11.4 billion by 2035, representing a compound annual growth rate (CAGR) of approximately 11.7% during the forecast period. Market expansion is being supported by rising pharmaceutical research activity, greater adoption of AI-enabled discovery workflows, increasing data volumes, and demand for scalable computing infrastructure. Market Growth and Key Trends Pharmaceutical and biotechnology companies are increasingly adopting cloud based drug discovery platforms to support target identification, molecular modeling, virtual screening, lead optimization, multi-omics analysis, and candidate prioritization. These platforms provide centralized data access, scalable high-performance computing, and integration with artificial intelligence and machine learning applications. Modern drug discovery programs generate substantial volumes of biological, chemical, genomic, transcriptomic, clinical, and real-world data. Cloud infrastructure allows organizations to process these datasets at scale while improving collaboration among internal research teams, contract research organizations, academic institutions, and technology partners. The continued shift toward data driven and AI-enabled research models is expected to strengthen long-term demand. Strategic partnerships are also accelerating market development. Amazon Web Services introduced Amazon Bio Discovery, providing access to more than forty AI biology models, AI-guided model selection, computational workflow design, and wet lab validation support. Merck and Google Cloud announced a multiyear collaboration valued at up to USD 1 billion to deploy AI and cloud infrastructure across research, development, manufacturing, commercial, and corporate operations. Primary Market Growth Drivers * AI-enabled drug discovery: Cloud platforms support parallel predictive modeling, molecule generation, virtual screening, lead optimization, and property prediction without requiring significant investment in internal computing infrastructure. * Scalable data integration: Centralized cloud environments help organizations combine research data from laboratory systems, internal databases, genomic resources, external partners, and clinical sources. * Collaborative research models: Distributed teams can securely access shared workflows and data across pharmaceutical companies, biotechnology firms, CROs, universities, and technology providers. * Strategic investment: More than USD 4 billion has been invested in cloud-native drug discovery technology providers, AI-enabled research platforms, and computational biology companies through venture capital, private equity, and strategic funding. * Technology partnerships: Bristol Myers Squibb announced the deployment of NVIDIA's next-generation DGX SuperPOD AI infrastructure to accelerate drug discovery, reporting that AI has already reduced development timelines by 20-30%. Relation Therapeutics also signed a collaboration with GSK valued at up to USD 110 million to develop AI-driven drug discovery models and human cellular datasets. Such agreements demonstrate growing commercial interest in cloud-enabled computational research. Market Challenges Data security, intellectual property protection, regulatory compliance, and vendor governance remain significant adoption considerations. Cloud based platforms manage proprietary molecular structures, target biology, screening results, patient-linked information, and competitive pipeline data. Pharmaceutical companies must therefore maintain robust access controls, auditability, cybersecurity safeguards, and compliance with applicable data protection requirements. Fragmented research data and legacy infrastructure create additional implementation barriers. Organizations often manage information across laboratory platforms, electronic lab notebooks, internal databases, and external systems with inconsistent formats. AI models also require validation, explainability, and context-specific performance assessment before their outputs can reliably support discovery and development decisions. The report evaluates the cloud based drug discovery platforms market by type of cloud service, type of cloud, end user, and geographical region. * Cloud service: Software as a Service, Infrastructure as a Service, Platform as a Service, and others * Cloud type: Hybrid Cloud, Public Cloud, and Private Cloud * End user: Pharmaceutical and Life Science Companies, CROs, and others * Region: North America, Europe, Asia-Pacific, Middle East and North Africa, and Latin America Software as a Service currently accounts for the largest market share due to lower upfront infrastructure requirements, faster implementation, and subscription-based access to advanced drug discovery tools. Platform as a Service is expected to record the highest CAGR through 2035 as pharmaceutical and biotechnology companies develop customized workflows for AI modeling, molecular simulation, data analytics, and collaborative research. Hybrid Cloud represents the leading deployment category. This model allows organizations to retain regulated data, proprietary AI models, and sensitive intellectual property within private infrastructure while using public cloud resources for compute-intensive activities such as model training, virtual screening, and large-scale omics analysis. Pharmaceutical and Life Science Companies hold the largest end-user share. Their position reflects sustained investment in digital research capabilities and extensive involvement across target identification, molecular modeling, predictive analytics, virtual screening, and lead optimization. Regional Market Outlook North America is expected to remain the largest regional market through 2035. Growth is supported by an established pharmaceutical and biotechnology sector, advanced cloud infrastructure, strong adoption of AI-enabled research platforms, and substantial research and development investment. Asia-Pacific is forecast to register the highest CAGR during the forecast period. China, India, Japan, and South Korea are expanding their biotechnology, pharmaceutical research, digital health, and life sciences ecosystems. Demand for cost-efficient computing, collaborative data management, and cloud-based drug discovery infrastructure is expected to drive regional adoption. Competitive Landscape Companies identified within the cloud based drug discovery platforms market include: * Amazon Web Services * Clarivate * Dassault Systemes * Google Cloud * Accenture * Alibaba Cloud * Cognizant * HCLTech Future Opportunities Future market growth will depend on improved interoperability among laboratory information management systems (LIMS), electronic lab notebooks (ELNs), genomic databases, and AI discovery platforms. Federated learning, secure multi-party data sharing, explainable AI, standardized data architectures, deep learning, machine learning, and quantum computing are expected to influence platform development. Regulatory guidance concerning electronic records, AI model validation, data integrity, and Good Machine Learning Practices (GMLP) may further support adoption. Continued advances in cybersecurity standards and cloud compliance frameworks are also expected to strengthen industry confidence. Report Coverage and Business Value The report provides market sizing, revenue forecasts, segment analysis, company benchmarking, provider profiles, partnership assessment, cost saving analysis, and evaluation of anticipated technology upgrades through 2035. It also examines market drivers, restraints, opportunities, and implementation challenges across major geographical regions. Insights were informed by discussions with executives and specialists from companies in the US, Switzerland, Canada, the UK, India, and Germany. Participants included chief executive officers, founders, technology leaders, data science professionals, regional managers, partnership executives, and scientific leaders. The report is designed to support distributors, channel partners, investors, technology providers, and business development teams evaluating opportunities in the cloud based drug discovery platforms market. It offers data for assessing customer demand, competitive positioning, regional expansion, strategic partnerships, and entry opportunities across high-growth market segments. * Amazon Web Services * Clarivate * Dassault Systemes * Google Cloud * Accenture * Alibaba Cloud * Cognizant * HCLTech List of Tables [130] A selection of companies mentioned in this report includes, but is not limited to: * Amazon Web Services * Clarivate * Dassault Systemes * Google Cloud * Accenture * Alibaba Cloud * Cognizant * HCLTech About ResearchAndMarkets.com ResearchAndMarkets.com is the world's leading source for international market research reports and market data. GovCloud Network provide you with the latest data on international and regional markets, key industries, the top companies, new products and the latest trends.

Yahoo Finance
Aug 1st, 2026
GSK opens $1.3B+ Cambridge research hub and partners with Relation Therapeutics

GSK will establish a new global R&D centre on the Cambridge Biomedical Campus in the UK, accommodating over 1,000 scientists. The facility will support closer collaboration with nearby research institutions and biotech companies. The pharmaceutical firm has also agreed a research collaboration with Relation Therapeutics. The partnership will use advanced cellular biology models and large-scale data generation. The moves signal GSK's increased focus on data-driven and AI-supported drug discovery. The Cambridge location places the company at the centre of one of Europe's leading life sciences hubs. GSK shares currently trade at £19.32, approximately 10% below the £21.42 analyst consensus target.

Longevity.Technology
Jul 30th, 2026
Relation builds causal AI for drug discovery.

Relation builds causal AI for drug discovery. New foundation model and an expanded GSK partnership reflect a growing shift from observational biology towards mechanistic understanding. Artificial intelligence has become an increasingly familiar fixture in drug discovery, but not all biological models are asking the same question. Many seek patterns within vast observational datasets - identifying associations between genes, cells and disease. Relation Therapeutics believes the next leap requires something different: models trained not simply to recognize what changes alongside disease, but to understand what happens when biology is deliberately perturbed. That philosophy sits behind two announcements from the London-based biotechnology company. Relation has unveiled MORGAN (Multi-Omic Regulatory Genomics using Artificial Neural Networks), a foundation model trained on large-scale perturbation datasets generated from human cellular systems [1], while simultaneously expanding its strategic collaboration with GSK to deepen the application of its Lab-in-the-Loop platform across therapeutic discovery [2]. Together, the announcements reflect the company's ambition to couple machine learning with experimental biology in a continuous cycle, allowing computational predictions to inform laboratory experiments - and laboratory results to refine the model. Longevity.Technology: Longevity has become remarkably adept at describing aging. Longevity Ltd. can estimate biological age, stratify populations and identify molecular signatures associated with healthspan. Yet describing biology and understanding biology are not the same thing. Much of today's aging biomarker landscape remains fundamentally correlational - enormously valuable for prediction, largely silent on the question that actually matters: what should Longevity Ltd. perturb to change the outcome? Relation's latest platform is interesting not because it claims to be a longevity technology, but because it is built around the sort of causal, interventional framework that geroscience increasingly needs. As the field matures, the winners may not be those who collect the largest datasets, but those who can tell association from cause. Adjacent, not identical. Relation's therapeutic focus - immunology, metabolic disease, bone disorders - has nothing to do with aging, officially. Unofficially, all three diseases get considerably more common the longer a person is alive, which is the sort of overlap no biotech can quite dodge. Asked whether MORGAN is being developed with aging biology in mind, CEO David Roblin is careful not to overstate the connection. "Many of the diseases we focus on become more prevalent with age, so there is naturally an overlap between disease biology and aspects of aging biology," he told Longevity.Technology. "Our primary objective, however, is to better understand the cellular mechanisms that drive human disease and use that knowledge to discover new therapeutic targets." That distinction matters. Rather than training MORGAN to identify age-related signatures specifically, Relation is building what Roblin describes as a broad biological foundation capable of interpreting cellular responses across multiple contexts. "MORGAN is designed to learn how human cells respond to genetic and pharmacological perturbations across different biological contexts," he explains. "By generating large-scale, high-quality multi-omic perturbation datasets, we're building AI models like MORGAN that can distinguish meaningful biological states and responses." As the model expands to incorporate increasingly diverse datasets, he believes it may eventually provide deeper insights into "how different factors - including age, disease and therapeutic intervention - shape cellular behavior." Cause and effect, not just correlation. Perhaps the more intriguing aspect of Relation's approach is not the model itself, but the data upon which it is trained. Much of contemporary aging research still runs on longitudinal cohorts, biobanks and biological clocks - observational data, essentially, however sophisticated the maths dressing it up. It has transformed what the field can measure: age, trajectory, risk, candidate biomarkers by the thousand. What it cannot do, however carefully it is stratified, is tell you what to do about any of it. "Our approach starts with understanding causal biology through controlled perturbation experiments rather than observation alone," Roblin says. "Longitudinal cohorts and biobanks tell us what is associated with disease, while perturbation data helps us understand cause and effect - how cells respond when specific pathways are altered. This is essential to get at the biology causing disease and therapeutics which can reverse. We believe those two approaches are highly complementary." Rather than presenting perturbation biology as a replacement for cohort science, Relation sees the approaches as addressing different questions - one descriptive, the other mechanistic. Foundations first. For geroscientists, an obvious question follows. Could a platform built on causal cellular biology ultimately contribute to aging biomarkers or even the discovery of geroprotective interventions? Roblin does not dismiss the possibility, but neither does he suggest it is the company's immediate objective. "Today, MORGAN is being developed to improve target discovery and therapeutic development across disease areas by building predictive models of cellular behavior," he says. "As the platform grows, we expect it to generate insights that extend beyond individual indications because many biological pathways are shared across diseases and physiological processes." He adds that whether those insights eventually contribute to aging biomarkers or therapies targeting the biology of aging remains "an exciting scientific question," while emphasizing that Relation's near-term priority is "building robust foundation models that deepen our understanding of human biology and enable the discovery of better medicines." That emphasis on infrastructure rather than indication also appears to underpin the expanded GSK collaboration. Rather than focusing on a single target, the partnership continues to integrate human data, wet-lab experimentation and machine learning to improve target identification across complex diseases. Mechanisms matter. The excitement surrounding AI in biomedicine often centers on scale - larger models, larger datasets and greater computational power. Relation's announcements hint at a different trajectory. If the next generation of biological AI is judged less by how accurately it predicts associations and more by how effectively it identifies mechanisms, perturbation biology may prove just as significant as model architecture. For longevity research, where understanding why biology changes increasingly matters as much as measuring that it has changed, that distinction could become increasingly difficult to ignore. Photographs courtesy of Relation Therapeutics. LTUI: 2372 Relation Therapeutics. Powered by: Longevity level(s): * Level 6: Aging disease prevention therapeutics Overview. Relation Therapeutics is a clinical-stage biotechnology company headquartered in London that integrates single-cell multi-omics, functional assays, and machine learning to discover transformational medicines. The company employs a Lab-in-the-Loop platform combining patient-derived tissue data with computational systems to identify novel drug targets across multiple disease areas, with initial focus on bone-related diseases, fibrotic conditions, and atopic diseases. Relation's approach aims to translate high-resolution biological understanding into therapeutics that address some of the most challenging human diseases by linking genetic cause to clinical phenotype. Access all data points on DLT: * Financial Data * Patents * Technology * Management * Advisory Board * News * Classifications * Similar Companies

Associated Press
Jul 30th, 2026
Relation unveils MORGAN, AI cellular model to accelerate drug discovery

Relation has announced MORGAN, a cellular foundation model designed to predict how human cells respond to genetic and pharmacological interventions. The model aims to provide insight into disease mechanisms and help identify therapeutic opportunities. MORGAN combines large-scale computation with multi-omic perturbation data generated in automated laboratories. The company will create petascale datasets using high-throughput cellular perturbation experiments designed for consistency beyond conventional laboratory approaches. Each tissue-specific MORGAN model focuses on a therapeutically important cell type central to disease biology. The general-purpose model can be applied across different cell types and disease areas. Chief executive David Roblin said the platform represents infrastructure needed to accelerate medicine discovery by combining frontier AI with industrial-scale data generation.