BioAgent Daily

您是明白人，只改了一个字，意思就完全不一样，重点是回应博主，突出中文博大精深 奈何评论区不尽人意，不知其他国家的朋友，看到国人在我下面的评论会作何感想

这流量 不开蓝 V 可惜了

郭老师的幸福生活(๑˃̵ᴗ˂̵)

对，你说的没错，那你肯定能全部用白话文讲出来吧，对你来说肯定很简单

是的，已经发现了，大部分人在为了可能到手的不确定数字而做着所谓的努力

你绝对不能连这么简单的事情都做不到吧，对不对

你一定可以看得懂吧，你来给外国友人翻译一下吧，这对你来说肯定是轻而易举的事情

地上的纸板子是用来睡觉的吗🤔

加一句 卧槽！

Tools and design history pull opposite ways here: tools lift strict compliance ~+0.3 but lower affinity-oracle scores, history does the reverse. Edit placement shifts too, ~29% of edits land in CDRs vs ~7% for the BO baseline. Constraint-following costs you optimization.

Paper: arxiv.org/abs/2611.05270

iGC models germinal-center affinity maturation as two stages: learn somatic hypermutation first, then RL-select for binding without throwing away that grammar. - Stage 1 mutation-site pos@1: 11.929% vs best baseline DASM 5.307% — more than doubles; joint accuracy 4.864% vs 2.153% (Tang test; paper) - After RL: 57.0% of predicted mutation positions sit in WRC hotspots vs 2.0% random background; mutability still correlates with S5F (Pearson 0.474) - Under 2–4 mutation budgets, exactly rediscovers 110 of 799 affinity-maturation sequences from the evaluation set - Reward–Tax Meta-Gradient (RewardNet + TaxNet) is there to stop the usual RL cheats — repeating one high-reward sub, or parking mutations in tolerant framework sites Not a wet-lab campaign. It’s a mutation policy that stays SHM-shaped while chasing affinity.

Paper: arxiv.org/abs/2611.07653

PharmAgents2 runs end-to-end drug discovery as parallel agents with a decision knowledge base — then hands molecules to humans for wet-lab checks. - PharmAgents-Bench v0.1: 121 questions / 9 decision scenarios + 3 end-to-end tasks; highest process scores vs Claude Code, Codex, and LLM-only (same DeepSeek V4.1 Flash base; paper) - LpxH: hypothesized an unreported allosteric site; 63 purchased candidates → AIPher-L1 IC50 8.38 μM; SPR 1.40 μM; MST 0.87 μM (human-run assays in the paper) - JAK1: prioritized covalent Cys817 strategy; 34 purchased → AIPher-J1 matched positive-control pathway inhibition at 10 μM; 3 more >50% in both pathways - Self-Scaling Discovery also nominated a new binding target for a known bioactive; interaction later confirmed experimentally (paper) The agent wrote the plan and shortlist. Humans bought and assayed. Binding-site ID for LpxH is still ongoing.

Gov.uk: gov.uk/government/news/intro…

The UK launched the AI Biosecurity Lab — first dedicated AI×biosecurity org in the government's framing — with £100M public money and up to £200M in company commitments. - OpenAI: £100M compute/model credits + trusted access via Rosalind Biodefense - Anthropic: up to £100M credits, trusted model access, dedicated technical support - Also named: Google DeepMind research collab; Basecamp Research AI-biology models - Initial focus: earlier detection of biological threats + faster vaccines / treatments / MCMs - Structure: independent non-profit; partners UKHSA, APHA, Dstl, MHRA Policy-level bet: put frontier model access next to national biosecurity centres, not just another safety white paper.

Paper: arxiv.org/abs/2611.03832 Artifacts: github.com/marcelmbn/farmaco…

Toronto's El Agente Fármaco turns a plain-language discovery goal into a multi-tool screen — and keeps every molecule, pose and metric in Fármaco Atlas with provenance. - 22-task bench vs plain tool calls and Codex: highest mean model-judged score; lower median cumulative tokens and peak context (authors) - TBXT retrospective: agent shortlist mean QED 0.69 vs expert-purchased 0.52; Lipinski violations 0.06 vs 0.54; max Tanimoto to purchased set 0.30 (common docking protocol) - Progressive disclosure: subset_id for whole libraries so the LLM never sees 200 SMILES at once - Tools span MolMIM / PocketXMol / DiffDock / Boltz-2 / ADMET-AI / RBFE — still computational; wet-lab pending on several campaigns The interesting bit isn't another chat wrapper. It's a drug-design-native database the agent can query without stuffing the context window.

Paper: arxiv.org/abs/2611.03963 Code: github.com/smiles724/ProteoC…

ProteoCraft treats binder design as proposal–verification search: truncated diffusion gradients propose mutations; full ESMFold2 decides what's worth keeping. - 14 de novo settings / 8 targets; MCTS Pair iPTM 0.721 vs Best-of-N 0.660 (paper) - Strict yield (Pair iPTM ≥0.75 + binder pLDDT ≥0.9): 114/210 = 54.3% vs Best-of-N 40/210 = 19.0% - AF2-IG strict pass: 86.2% vs 78.6% (same sequences, outside the search verifier) - Wet lab PD-L1 miniproteins: 29/35 hits = 82.9%; best SPR KD 0.879 nM; IFNAR2 VHH 4/9 expressed hits, KD ~153 nM (no affinity maturation) Gradients steer where to look. Full folds decide what ships.