Healthcare
Computational biology and ML systems that accelerate target identification, compound screening, and trial cohort selection — reducing the time and cost of early-stage drug development.
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Industry overview
AI systems for pharmaceutical drug discovery that accelerate target identification, virtual compound screening, ADMET property prediction, and clinical trial cohort selection — compressing the timeline of early-stage development.
At a glance
Drug discovery is one of the most expensive and time-consuming processes in any industry — a new molecule takes an average of 12 years and over $2 billion to reach approval. The early stages — target identification, lead compound selection, and optimisation — are data-intensive processes where AI can compress timelines significantly by evaluating far more candidates computationally than wet lab processes can test physically.
We build target identification systems that mine genomic, proteomic, and clinical literature data to propose novel disease targets. Virtual screening pipelines evaluate molecular libraries against target structures computationally, ranking candidates by predicted binding affinity before expensive synthesis. ADMET prediction models assess absorption, distribution, metabolism, excretion, and toxicity properties early — eliminating compounds that would fail in vivo before resources are committed. Clinical trial cohort selection tools identify eligible patients from EHR data to accelerate trial recruitment.
Key capabilities
Engagements are scoped to your business context — these are the core capabilities we bring to healthcare clients.
Target and pathway identification from genomic and literature data
Virtual compound screening against target molecular structures
ADMET property prediction for early toxicity and efficacy assessment
Clinical trial cohort identification from EHR population data
Scientific literature mining and hypothesis generation
Regulatory submission data preparation and documentation support
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