Mage-Flow: An Efficient Native-Resolution Foundation Model for Image Generation and Editing
Paper • 2607.19064 • Published • 78
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reacted to SeaWolf-AI's post with ❤️ about 1 month ago
🧬 Your AI can design a malaria drug candidate. Can it tell you whether it's any good?
Open Discovery Challenge #1 — Malaria is live. Design a molecule with any model — OpenAI, Claude, Gemini, Qwen, KIMI, DeepSeek, open weights, or by hand — submit it as SMILES, and it's scored in minutes on whole-cell activity, target binding, selectivity over the human enzyme, ADMET, novelty and synthesisability.
You can check the scoring instead of trusting it. Approved drugs sit on the same leaderboard as the entries: DSM265, a clinical-stage antimalarial, scores 50.9. Teriflunomide — approved, but it hits the human enzyme — scores 2.8. Caffeine scores 1.8. If the clinical candidate lands on top and coffee lands at the bottom, the scorer discriminates.
We caught 14 defects before opening — conventional toxicity cutoffs rejected all three approved antimalarials and coffee. All written up, along with the rule we now hold everything to: a gate that rejects an approved drug is a broken gate.
Your molecule stays yours. No patent interest, nothing into our pipeline. You choose whether it's published — and publishing can cost you patentability, so we say so.
USD 1,000 to the top entry when Season #1 closes 30 September 2026 — not payment for your tokens, but a way of saying the work had worth.
Malaria killed ~597,000 people in 2023, three quarters of them children under five. Not for want of chemistry — for want of a market.
No chemistry needed: the guide ships five prompts you can paste straight into your model, and the full rubric is published.
📖 https://huggingface.co/blog/FINAL-Bench/open-discovery-challenge
🚀 https://huggingface.co/spaces/FINAL-Bench/open-discovery-challenge
Computational assessments of candidates — not measurements, not claims of efficacy. upvoted a paper 2 months ago
Mage-Flow: An Efficient Native-Resolution Foundation Model for Image Generation and Editing liked a model 2 months ago
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TRL v1.0: Post-Training Library Built to Move with the Field
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