Hermes：大规模DEL数据集训练通用性蛋白-配体结合预测模型
Hermes: Large DEL Datasets Train Generalizable Protein-Ligand Binding Prediction Models
AI总结：
Hermes通过大规模DEL数据集训练，实现了无需传统亲和力测量的蛋白质-配体结合预测模型，具有高效推理能力与广泛泛化能力。
摘要：
训练数据的质量和一致性仍然是蛋白质-配体结合预测的关键瓶颈。公共亲和力数据集，汇集自成千上万的实验室和检测方法，引入了限制模型泛化能力和复杂评估的偏见。DNA编码化学库（DELs）提供了一个潜在的解决方案：统一的实验协议生成了跨越多样化学和蛋白质靶标空间的大量结合数据集。我们提出了Hermes，一个轻量级的Transformer模型，仅在针对数百种蛋白质靶标进行筛选的DEL数据上进行训练，代表了截至目前最大的、最多样化的DEL训练集之一，应用于蛋白质-配体相互作用（PLI）建模。尽管在训练过程中从未见过传统亲和力测量，Hermes仍能泛化到未见的靶标、新型化学骨架以及源自公共结合数据和高通量筛选的外部基准。我们的结果表明，仅使用DEL数据即可捕获可转移的蛋白质-配体相互作用表示，而Hermes的最小架构使推理速度适合大规模虚拟筛选。
英文摘要：
The quality and consistency of training data remain critical bottlenecks for protein-ligand binding prediction. Public affinity datasets, aggregated from thousands of labs and assay formats, introduce biases that limit model generalization and complicate evaluation. DNA-encoded chemical libraries (DELs) offer a potential solution: unified experimental protocols generating massive binding datasets across diverse chemical and protein target space. We present Hermes, a lightweight transformer trained exclusively on DEL data from screens against hundreds of protein targets, representing one of the largest and most protein-diverse DEL training sets applied to protein-ligand interaction (PLI) modeling to date. Despite never seeing traditional affinity measurements during training, Hermes generalizes to held-out targets, novel chemical scaffolds, and external benchmarks derived from public binding data and high-throughput screens. Our results demonstrate that DEL data alone captures transferable protein-ligand interaction representations, while Hermes' minimal architecture enables inference speeds suitable for large-scale virtual screening.
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