标题： 分子指纹是肽功能预测的强大模型 显示英文标题

标题： Molecular Fingerprints Are Strong Models for Peptide Function Prediction

摘要： 我们研究了分子指纹在肽性质预测中的有效性，并证明从分子图中提取的领域特定特征可以超越复杂且计算成本高的模型，如GNN、预训练序列基础的变压器和多模态集成，即使没有超参数调整。为此，我们在126个数据集上进行了全面评估，在LRGB和5个其他肽功能预测基准上取得了最先进的结果。我们表明，基于ECFP计数变体、拓扑扭转和RDKit分子指纹以及LightGBM作为分类头的模型具有显著的鲁棒性。分子指纹的优异性能，它们本质上是非常短程特征编码器，挑战了肽中长程相互作用的重要性的假设。我们的结论是，对于较大的分子，如肽，使用分子指纹可以作为复杂深度学习模型的一种计算可行、低参数和多功能的替代方案。 显示英文摘要

摘要： We study the effectiveness of molecular fingerprints for peptide property prediction and demonstrate that domain-specific feature extraction from molecular graphs can outperform complex and computationally expensive models such as GNNs, pretrained sequence-based transformers and multimodal ensembles, even without hyperparameter tuning. To this end, we perform a thorough evaluation on 126 datasets, achieving state-of-the-art results on LRGB and 5 other peptide function prediction benchmarks. We show that models based on count variants of ECFP, Topological Torsion, and RDKit molecular fingerprints and LightGBM as classification head are remarkably robust. The strong performance of molecular fingerprints, which are intrinsically very short-range feature encoders, challenges the presumed importance of long-range interactions in peptides. Our conclusion is that the use of molecular fingerprints for larger molecules, such as peptides, can be a computationally feasible, low-parameter, and versatile alternative to sophisticated deep learning models.