没有蛋白结构，也没有预定义口袋：Ptarmigan-1 的超大规模虚拟筛选路线
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Observed public metrics from 1 member.
Merged summary
TL;DR — Talus Bioscience's Ptarmigan-1 (bioRxiv preprint, July 2026) is a structure-free virtual screening model that embeds per-residue protein vectors (ESM-C) and ligand SMILES vectors (ChemBERTa) into a shared 256-d space, turning screening into vector retrieval instead of pairwise 3D complex prediction. It matters because it extends hit-finding to cryptic pockets, covalent sites, and disordered proteins that structure-based pipelines cannot economically cover.
- Architecture: Frozen ESM-C (~600M) + ChemBERTa backbones with LoRA adapters (rank 32, α 64, dropout 0.1) and linear projection heads; residue–ligand cosine similarity becomes a residue-level binding score, aggregated to protein level via temperature-scaled softmax pooling. No pocket, structure, or 3D pose is required — and none is output.
- Mixed-resolution training: ~2.6M protein–compound interactions combining PDB complexes (~81K pairs, 5 Å residue labels), public ABPP (~12K), Talus internal chemoproteomics (~229K, not public), and protein-level bioactivity data (BindingDB/KIBA/LCIdb, ~2.2M, 10 μM cutoff), trained with residue- and protein-level contrastive losses plus a calibration loss.
- Results: On LIT-PCBA it ranks second (ROC-AUC 0.672, adjusted logAUC 0.120, EF@1% 7.32), beating Glide-SP and Protenix but clearly below Boltz-2 (0.776). Site localization is strong: true covalent cysteine in the top 1% of residues for 8/9 COValid targets, and 0.98–0.99 median pocket-vs-rest AUROC on PoseBusters with 92% top-residue recovery (77% after ligand swap).
- Cost structure: ~10.1 ms/ligand vs Boltz-2's ~53.5 s/ligand on an H100 (~5000× throughput); a 3.4B-compound OnePot CORE screen across 20,431 human proteins ran in ~20 H100 GPU-hours via a pre-built LanceDB IVF-PQ ANN index — approximate retrieval, not exhaustive enumeration.
- Caveats stated by the authors: not peer-reviewed; Boltz-2's training data may overlap LIT-PCBA targets; the residue-pair loss lacks a published formula; in-batch negatives may hide false negatives for polypharmacological compounds.
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没有蛋白结构，也没有预定义口袋：Ptarmigan-1 的超大规模虚拟筛选路线
TL;DR — Talus Bioscience's Ptarmigan-1 (bioRxiv preprint, July 2026) is a structure-free virtual screening model that embeds per-residue protein vectors (ESM-C) and ligand SMILES vectors (ChemBERTa) into a shared 256-d space, turning screening into vector retrieval instead of pairwise 3D complex prediction. It matters because it extends hit-finding to cryptic pockets, covalent sites, and disordered proteins that structure-based pipelines cannot economically cover.
- Architecture: Frozen ESM-C (~600M) + ChemBERTa backbones with LoRA adapters (rank 32, α 64, dropout 0.1) and linear projection heads; residue–ligand cosine similarity becomes a residue-level binding score, aggregated to protein level via temperature-scaled softmax pooling. No pocket, structure, or 3D pose is required — and none is output.
- Mixed-resolution training: ~2.6M protein–compound interactions combining PDB complexes (~81K pairs, 5 Å residue labels), public ABPP (~12K), Talus internal chemoproteomics (~229K, not public), and protein-level bioactivity data (BindingDB/KIBA/LCIdb, ~2.2M, 10 μM cutoff), trained with residue- and protein-level contrastive losses plus a calibration loss.
- Results: On LIT-PCBA it ranks second (ROC-AUC 0.672, adjusted logAUC 0.120, EF@1% 7.32), beating Glide-SP and Protenix but clearly below Boltz-2 (0.776). Site localization is strong: true covalent cysteine in the top 1% of residues for 8/9 COValid targets, and 0.98–0.99 median pocket-vs-rest AUROC on PoseBusters with 92% top-residue recovery (77% after ligand swap).
- Cost structure: ~10.1 ms/ligand vs Boltz-2's ~53.5 s/ligand on an H100 (~5000× throughput); a 3.4B-compound OnePot CORE screen across 20,431 human proteins ran in ~20 H100 GPU-hours via a pre-built LanceDB IVF-PQ ANN index — approximate retrieval, not exhaustive enumeration.
- Caveats stated by the authors: not peer-reviewed; Boltz-2's training data may overlap LIT-PCBA targets; the residue-pair loss lacks a published formula; in-batch negatives may hide false negatives for polypharmacological compounds.