🧬 A thank you, and an open invitation. Over the past few weeks we shared our AMR landscape report and five proposals built on it. Huge thanks to the 130+ researchers, 60+ workshop experts, and working groups who made it happen. The proposals are the starting line, not the finish. The real work of building these datasets and models is still ahead, and we'd love help: data, collaboration, funding. Here's to seeing it get done. 📄 hubs.la/Q04sJ50g0 📬 [email protected] 🌐 hubs.la/Q04sJ0kx0 #AMR #OpenScience #BioAI

🧬 Proposal spotlight: predicting resistance before it emerges in a patient. When a patient turns from susceptible to resistant mid-treatment, is it new mutation or reinfection? We'd build a prospective, multi-site LMIC cohort (an ACORN extension) with paired WGS to predict it, and tell the two apart. The gap: almost no ML-for-AMR work uses LMIC data. 📄 hubs.la/Q04sJ0gR0 📬 [email protected] #AMR #BioAI #GlobalHealth

🧬 Proposal spotlight: flagging antibiotic liver toxicity before synthesis. Toxicity models trained on typical drugs stumble on antibiotics. We'd build the first matched antibiotic dataset across liver cell lines and organoids, then predict hepatotoxicity and its mechanism from structure. The gap: antibiotics are barely present in existing toxicity data. 📄 hubs.la/Q04sHYks0 📬 [email protected] #AMR #DrugDiscovery #BioAI

🧬 Proposal spotlight: personalized antibiotic dosing for kids. Start by extending KidsCalc, a validated vancomycin dosing tool, beyond Australia. Then build a model predicting outcomes, toxicity, and resistance emergence, so dosing adapts to each child. The gap: the data pieces exist but aren't linked to outcomes. 📄 hubs.la/Q04sHNWH0 📬 [email protected] #AMR #BioAI #GlobalHealth

🧬 Proposal spotlight: getting drugs into Gram-negatives. Their outer membrane is the problem: will a compound get in, and will it stay in? We'd build an LC-MS/MS accumulation dataset across E. coli, A. baumannii, and P. aeruginosa, using active learning to pick the most informative compounds. The gap: almost no direct accumulation data for two of the three. 📄 hubs.la/Q04sHR820 📬 [email protected] #AMR #DrugDiscovery #BioAI

🧬 Proposal spotlight: predicting quantitative MIC straight from a genome. Resistance is shaped by genes, mutations, copy number, and epistasis. We'd build species-specific MIC-from-WGS models across ~10 WHO priority pathogens, on globally diverse isolates so predictions hold up beyond where the data came from. The bottleneck: data that's sizable but geographically narrow. 📄 hubs.la/Q04sHH5z0 📬 [email protected] #AMR #OpenScience #BioAI