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dev7halo

dev7halo
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liked a dataset 14 days ago
r0b0tlab/qwen3.8-max-glm5.2-kimi-k3-distillation
reacted to SeaWolf-AI's post with ❤️ 14 days 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.
reacted to vovaRL's post with 🔥 14 days ago
I made an AI agent for end-to-end ML research. It is based on Claude Agents Python SDK (you can use it with your existing Claude subscription). Also needs Voyage AI (optional, but highly recommended for quality paper analysis). The workflow is: you give the agent a research question (or topic/direction), it pulls papers from papers-with-code and arxiv, reranks them and picks top-15 (with Voyage), then it makes a research plan, writes pipelines (with tests, preflight checks, smoke-runs). Then it uses Kaggle, HF Jobs, or SSH to a GPU to execute the pipeline. Finally, it gets the results, makes visualizations, writes the paper (with citing from papers ingested after step-1) and makes a jupyter notebook. Also there is an auto-generated wiki (like DeepWiki) to quickly understand the generated code. The code is fully open-source under permssive MIT license (it is a personal project). Here is the github link: https://github.com/view321/Grad and a workspace screenshot is attached to the message.
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