无需预训练的分子指纹模型，性能全面领先基准方法。
WEECFP-SuRGE: Wide Embedded Extended Connectivity Fingerprint with Substructure Rotary Graph-distance Encoding
- 用连续指纹将分子子结构分布到向量中，结合图距离旋转编码提升表示能力。
- 在TDC ADMET 22项任务中10次夺冠，多项指标排名第一，且无需外部预训练。
- 指纹可近乎无损重建分子结构，支持高效位置记忆，适合药物研发场景。
我们提出WEECFP，一种无参数的1024维连续分子指纹，将每个摩根子结构散列至约32个带符号位置；以及WEECFP-SuRGE，一种采用子结构旋转图距离编码（SuRGE）的Transformer架构，该编码基于分子最短路径图距离进行旋转。7模型融合的WEECFP-SuRGE Blend在TDC ADMET排行榜上平均回归排名最低，总分排名第2（仅次于预训练的MapLight+GNN），在无外部预训练方法中排名第一。在完整22项基准测试中，其在Pgp、Lipophilicity、CYP2D6 Substrate、Clearance Microsome和LD50上均获第一；其中不带SuRGE的融合版本在HIA任务也排名第一。在MoleculeNet上，该模型在ESOL、Lipophilicity和QM9三个回归任务上超越所有经典指纹基线。进一步显示，WEECFP分词几乎无损：贪婪重构建可恢复99.9%的分布内分子的规范SMILES（跨9个MoleculeNet数据集），在跨数据集验证（HIV→Lipophilicity）中恢复率达98.93%；三参考最远优先编码的图距离与真实距离相关系数为Pearson r = 0.901，实现O(S)级位置记忆精度。
原文摘要 · Abstract (English)
We introduce WEECFP, a parameter-free 1024-dimensional continuous molecular fingerprint that scatters each Morgan substructure across roughly thirty-two signed positions of a single vector, and WEECFP-SuRGE, a transformer architecture whose self-attention applies SuRGE (Substructure Rotary Graph-distance Encoding) -- a RoPE-like rotation parameterized by molecular shortest-path graph distance -- to WEECFP substructure tokens. A 7-model blend of this architecture (the WEECFP-SuRGE Blend) achieves the lowest average regression rank on the TDC ADMET leaderboard; is #2 overall on the TDC ADMET leaderboard (behind only pretrained MapLight+GNN), and is #1 overall among methods that use no external pretraining; takes leaderboard #1 finishes on Pgp, Lipophilicity, CYP2D6 Substrate, Clearance Microsome, and LD50 (with the WEECFP-NoSuRGE Blend separately reaching #1 on HIA) across the full 22-benchmark suite -- without any external pretraining. On MoleculeNet, WEECFP-SuRGE beats every classical-fingerprint baseline on 3 of 4 regression tasks (ESOL, Lipophilicity, QM9). We further show that WEECFP tokenization is near-lossless: a greedy overlap reconstruction recovers the exact canonical SMILES of 99.9% of in-distribution molecules across 9 MoleculeNet datasets and 98.93% of molecules in a cross-dataset holdout (HIV->Lipophilicity), and that a three-reference farthest-first encoding of graph distance correlates at Pearson r = 0.901 with the true pairwise distance, enabling O(S) positional memory at matching accuracy.
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