AI Agents for Life Sciences
Bringing a drug to market costs $2.6 billion on average, takes 12 years, and fails 90% of the time. Most failures are predictable from signals that were present but not caught early enough. AI agents that surface those signals earlier change the probability of catching critical issues before they become expensive late-stage failures.
Life Sciences AI Agents
Why AI Matters in Life Sciences
- Bringing a drug to market costs $2.6 billion on average and takes 12 years, with a 90% clinical failure rate - and most late-stage failures result from efficacy or safety signals that could theoretically have been caught earlier in the right patient subpopulation.
- Physical synthesis and assay cycles in early drug discovery are expensive and slow; every compound tested without a computational filter wastes resources that could have been directed toward higher-probability candidates.
- Clinical trial design decisions made without full use of historical biomarker and stratification data increase required sample sizes, extend timelines, and reduce the probability of detecting efficacy in the population most likely to respond.
- AI continuously analysing all available trial data and flagging unexpected patterns as they emerge changes the probability of catching critical signals before they become expensive late-stage failures.
Top Use Cases
Target Identification and Compound Screening
Analyse genomic, proteomic, and phenotypic datasets to identify disease targets and predict the activity and selectivity of candidate compounds - dramatically reducing the number of physical synthesis and assay cycles required.
Clinical Trial Design and Patient Stratification
Use historical trial data and biomarker information to design more efficient trials, identify the patient subpopulations most likely to respond, and optimise endpoint selection for the fastest path to meaningful data.
Genomic and Multi-Omics Data Analysis
Process whole-genome sequencing, RNA expression, and proteomics datasets to identify biomarkers, resistance mechanisms, and patient stratification signals that inform both development strategy and label claims.
Regulatory Dossier Compilation and Gap Analysis
Assemble regulatory submission components from study data, cross-reference against agency-specific requirements, identify gaps in the evidence package, and track submission status across multiple geographies.