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AI-Remedy: AI-Generated Molecular Binders for Selective Filtration and Monitoring of Emerging Contaminants in Water Systems

Reference number
Coordinator Lunds universitet - Lunds universitet Kemiska institutionen
Funding from Vinnova SEK 5 927 980
Project duration June 2026 - December 2028
Status Ongoing
Venture Impact Innovation Water Wave Societies - Programme-specific interventions
Call Impact Innovation: Innovations that can change systems – “Sustainable water for all 2050”

Purpose and goal

AI-Remedy brings together Lund University, LivingFilters, and RecoLab/NSVA to develop AI-designed protein binders and probes for the selective detection and removal of emerging water contaminants, including pharmaceuticals, PFAS-like compounds, and pesticides. The project will validate biological filtration and sensing technologies, creating a scalable platform for adaptive, efficient, and sustainable water treatment.

Expected effects and result

The project will deliver validated AI-designed protein binders and molecular probes, a prototype biological filtration material, and an integrated sensing platform for wastewater applications. The results will demonstrate a scalable approach for selective contaminant detection and removal, supporting more efficient water treatment, reducing energy usage, and enabling rapid adaptation to emerging pollutants through AI-driven molecular design.

Planned approach and implementation

The project combines AI-driven protein design, high-throughput experimental validation, and wastewater testing to develop and evaluate selective protein binders, molecular probes, and biological filtration materials. Solutions will be developed in close collaboration with LivingFilters, Lund University, and RecoLab/NSVA, progressing from laboratory validation to prototype integration and testing under representative wastewater conditions.

External links

The project description has been provided by the project members themselves and the text has not been looked at by our editors.

Last updated 30 June 2026

Reference number 2026-00696