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The programme will leverage FCDO’s convening power and thought leadership to crowd in more investment and ensure those pushing the scientific frontier on global development challenges can benefit from and further the evidence on cutting edge AI approaches.\n \n ID: GB-GOV-1-400868-401\nTitle: Next GenAI Cures Platform\nDescription: AI Co-Scientists – agentic AI tools for scientific hypothesis generation coupled with domain-specific models (e.g., protein design tools) – are proliferating rapidly, but the field lacks robust ways of determining what works, when, and why, which risks wasting time, effort, and resource on tools that are not fit-for-purpose, ultimately slowing scientific progress. This programme aims to address this problem by creating: (1) a living landscape map of Co-Scientist tools, (2) an openly published benchmark (i.e., a suite of automated evaluations) to test the performance of Co-Scientist tools on a range of relevant tasks, and (3) a light-touch secretariat to help FCDO and its partners implement Co-Scientist workflows to accelerate discovery. In parallel, we will collaborate with FCDO’s extensive subject matter expert bench to demonstrate the potential impact of best-in-class AI Co-Scientists by applying them to address three critical global health challenges: novel therapeutics design to address the growing global burden of mental health issues (e.g., a new druggable target); novel susceptibility testing for antimicrobial resistance (e.g., a low-cost and stable point-of-care susceptibility test suitable for use in LMICs); and malaria vector control (e.g., a novel low-cost, synthetic mosquitocidal agent). For each challenge, we will move from AI-assisted hypothesis generation to a small, catalytic validation study, such that by the end of the initial (2-year) phase, we have tangible evidence of success from at least one exemplar field, ready for further investment from other donors to drive global impact.\n \n ID: GB-GOV-1-400868-402\nTitle: Artificial Intelligence (AI) in Crop Breeding\nDescription: This project aims to improve crop breeding in low- and middle-income countries by using advanced computer-based tools to speed up the identification and selection of useful plant traits. Working in partnership with UK researchers, international agricultural research centres and local research organisations, the project will test new approaches for analysing plant genetics to support the development of crops that are more resilient to climate change and better suited to local farming needs.  It will compare the benefits of these AI-enabled methods with existing breeding practices. 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