Apply AI to identify, design, and prioritize high-probability drug candidates.
Scenarios
Bring AI to discovery's highest-stakes decisions
Validate targets, design and rank candidates, stratify patient populations, and guide lab experimentation with AI on a trusted data foundation. Relevant for CSOs, Heads of R&D, CDOs, and VPs of Clinical Development.
Target identification & validation
Surface and validate disease-relevant biological targets using genomic, proteomic, and experimental datasets.
Molecular design & in-silico simulation
Generate and rank drug candidates using AI-driven property prediction and molecular simulation.
Biomarker discovery & patient stratification
Identify predictive biomarkers and stratify populations to improve trial design and therapy targeting.
Lab automation & AI-assisted experimentation
Guide and automate experimental workflows to accelerate hypothesis testing and data capture.
Discover how organizations are using AI for healthcare
"Using data with AI to define patients’ risk of developing diseases will help us provide better treatments. This is precision medicine, and it’s going to be important for us to operate in the future."
Karin Conde-Knape, Senior Vice President of Global Drug Discovery, Novo Nordisk
"Customers will have greater success with our product if we augment it with context. Our Azure-based bot uses retrieval-augmented generation (RAG) to guide customers through coding in our specific Python dialect to work in their particular application domains."
Charles Crain, Chief Technology Officer, Artificial
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