Our Science
Embedding the patient, from the earliest stages of innovation.
We constantly ask which new technologies will allow us to drive the progression of our programmes. To do this, we create experimental and computational systems to understand the biological processes in human health and disease.
Our technologies enable us to generate, analyse and interpret human
data about the behaviour of genes, cells and tissues in an unparalleled
fashion. Above all, we drive unprecedented integration to identify new
therapeutic targets and translate those into transformational
medicines.
World-class integration:
translating science into medicine.
Multi-modal patient data
Perturbational omics and translational cellular models
High-performance experimentation and computation
Machine learning
Multi-modal patient data
We use human genetics and proprietary omics generated directly from patient tissue to understand the genetic basis of clinical phenotypes.
Our approach enables us to build rich maps of disease biology from which we discover novel targets. The data we leverage enables us to link cause to phenotype, which we test with perturbational omics and translational cellular models.
Multi-modal patient data
Perturbational omics and translational cellular models
High-performance experimentation and computation
Machine learning
Perturbational omics and translational cellular models
We deploy interventional experiments with a range of perturbation technologies to test the role of a gene in driving cellular phenotype of disease.
Our cellular and pre-clinical models have translational phenotypes that faithfully capture human disease for our high-performance experimentation.
Multi-modal patient data
Perturbational omics and translational cellular models
High-performance experimentation and computation
Machine learning
High-performance experimentation and computation
Our lab integrates tissue profiling, single-cell and spatial transcriptomics, sequencing and target validation.
At Relation, we pioneer a Lab-in-the-Loop approach to drug target discovery and development. In partnership with NVIDIA, we leverage hyperscale compute to enable our class-leading machine learning platforms.
Multi-modal patient data
Perturbational omics and translational cellular models
High-performance experimentation and computation
Machine learning
Machine learning
To analyse and interpret the scale of data we generate, we deploy machine learning.
We leverage machine learning across target identification, prioritisation and validation, as well as experimental design. In every case, we use the most appropriate methods, including generative and large language models, and graph neural networks.
Multi-modal patient data
We use human genetics and proprietary omics generated directly from patient tissue to understand the genetic basis of clinical phenotypes.
Our approach enables us to build rich maps of disease biology from which we discover novel targets. The data we leverage enables us to link cause to phenotype, which we test with perturbational omics and translational cellular models.
Perturbational omics and translational cellular models
We deploy interventional experiments with a range of perturbation technologies to test the role of a gene in driving cellular phenotype of disease.
Our cellular and pre-clinical models have translational phenotypes that faithfully capture human disease for our high-performance experimentation.
High-performance experimentation and computation
Our lab integrates tissue profiling, single-cell and spatial transcriptomics, sequencing and target validation.
At Relation, we pioneer a Lab-in-the-Loop approach to drug target discovery and development. In partnership with NVIDIA, we leverage hyperscale compute to enable our class-leading machine learning platforms.
Machine learning
To analyse and interpret the scale of data we generate, we deploy machine learning.
We leverage machine learning across target identification, prioritisation and validation, as well as experimental design. In every case, we use the most appropriate methods, including generative and large language models, and graph neural networks.
Multi-modal patient data
Perturbational omics and translational cellular models
High-performance experimentation and computation
Machine learning
Our pipeline begins with osteoporosis.
Learn how we are rapidly advancing programmes in bone disease for patients in need.