Who We Work With
7DNA partners with organisations where biological data, computation and AI drive research outcomes, from pharmaceutical discovery to academic neuroscience.
Pharma & Biopharma
Drug discovery · Clinical data · Pipeline R&DPharmaceutical and biopharmaceutical companies engage 7DNA for computational support across the early-to-mid drug discovery pipeline, from target identification through molecular modelling and ADMET prediction.
Use Cases
- →In silico target identification using network pharmacology
- →Molecular docking and structure-based virtual screening
- →ADMET prediction and compound prioritisation
- →Clinical trial data analytics and biomarker identification
Biotechnology
Genomics · Proteins · Bioinformatics pipelinesBiotech companies use 7DNA for bioinformatics and genomics services, from sequence analysis and variant calling to protein structure prediction and multi-omics integration that informs product development.
Use Cases
- →Genomics analysis: WGS, WES, RNA-seq pipelines
- →Protein structure analysis and homology modelling
- →Biomarker discovery from omics datasets
- →Custom bioinformatics pipeline development
Healthcare
Clinical informatics · Medical AI · Outcomes researchHealthcare organisations and research hospitals engage 7DNA for clinical data analytics, medical AI development and health outcomes research, bringing computational rigour to patient data at scale.
Use Cases
- →Clinical data analytics and EHR data studies
- →AI-based diagnostic support and image analysis
- →Survival analysis and health outcomes research
- →Population health analytics and biomarker studies
Academia & Research Institutions
Collaboration · Platforms · DatasetsAcademic researchers and research institutions use 7DNA's platforms and engage us for collaborative research, contributing computational expertise, shared datasets, and co-authorship on peer-reviewed publications.
Use Cases
- →Access to NeuroSignature and Research Decode platforms
- →Computational analysis for grant-funded research projects
- →Research collaboration with co-authorship on publications
- →Dataset sharing and scientific resource access
Common Themes in Computational Life Sciences
Whatever the sector, certain computational challenges recur. 7DNA is equipped to address them, with methodology tuned to the specific scientific context.
Biological Data at Scale
Genomics, proteomics and imaging data are large and complex. We build pipelines to process, clean and analyse them reproducibly.
Predictive Modelling
Predicting drug properties, patient outcomes or gene function from biological data, AI and ML applied with scientific rigour.
Cross-disciplinary Integration
The most interesting questions span disciplines. We connect molecular, cellular, systems and clinical levels of biology through computation.
Is your organisation working on a computational challenge in life sciences?
Tell us about it, we'll identify whether and how 7DNA can contribute.