Accelerate Early Drug Discovery with AI
Bringing a new drug to market traditionally takes 10–15 years in research and development (R&D) and billions of dollars of investment. The most costly and time-consuming phases occur early—target identification, target structure determination and hit identification from vast chemical spaces. Recent advances in predictive and generative AI combined with physics-based modeling show immense promise, but adoption remains fragmented and complex.
Pi-AFDD, Persistent’s AlphaFold Drug Discovery, addresses this gap with an end-to-end, integrated AI workbench for early drug discovery.
What We Deliver
Pi-AFDD is a unified, AI-driven drug discovery workbench that integrates target identification, protein structure prediction, virtual screening and automated reporting into a seamless workflow.
- Holistic Target Identification & Prioritization using OmniKG-driven knowledge graphs
- AI-powered protein structure prediction using AlphaFold2 and ESMFold
- Scalable virtual screening and molecular docking pipelines
- Gemini-powered scientific insight assistant and automated report generation
- Regulatory-ready documentation with full workflow traceability
How Pi-AFDD Transforms Early Drug Discovery
Target Identification & Prioritization
From: Fragmented literature, databases and expert-driven analysis
To: Integrated Omni Knowledge Graphs connecting diseases, genes, proteins, pathways and therapeutics with AI-driven prioritization support
Protein Structure Prediction & Variant Analysis
From: Time-intensive experimental structure determination
To: Rapid in silico structure prediction using AlphaFold2/ESMFold with mutation impact analysis via AlphaMissense
Virtual Screening & Molecular Docking
From: Multi-tool, manual docking workflows
To: One-click, integrated docking pipelines with binding site prediction, scoring and visualization
Scientific Reporting & Compliance
From: Manual report assembly across tools
To: Gemini-powered smart report builder capturing end-to-end workflow data in defined templates
From Disease Hypothesis to Lead Hits
- End-to-end disease-to-hit workflow
- Knowledge graph–driven scientific reasoning
- AI-assisted hypothesis generation and validation
- Physics-based molecular interaction modeling
- Full traceability for regulatory and audit readiness
How Pi-AFDD Drives Value
- Reduce early discovery timelines and costs
- Improve target quality and prioritization confidence
- Increase experimental success rates through better hit selection
- Enable adoption of advanced AI without deep infrastructure complexity
- Support scalable, governed, AI-driven discovery pipelines
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