DecoyDB：用于蛋白质-配体结合亲和力预测的图对比学习数据集
DecoyDB: A Dataset for Graph Contrastive Learning in Protein-Ligand Binding Affinity Prediction
AI总结：
针对蛋白质-配体结合亲和力预测中标签稀缺问题，提出大规模结构感知数据集DecoyDB及定制图对比学习框架，预训练后微调显著提升准确性、标签效率和泛化性。
摘要：
预测蛋白质-配体复合物的结合亲和力在药物发现中起着至关重要的作用。然而，由于缺乏大规模、高质量的结合亲和力标签，该领域的进展一直受到阻碍。广泛使用的PDBbind数据集仅有不到20K个带标签的复合物。自监督学习，尤其是图对比学习（GCL），为打破这一障碍提供了独特的机会，它可以通过基于大量无标签复合物预训练图神经网络模型，并在数量少得多的带标签复合物上微调模型。然而，该问题面临独特的挑战，包括缺乏具有明确正/负复合物对的全面无标签数据集，以及需要设计能够融合此类数据独特特征的GCL算法。为填补这一空白，我们提出了DecoyDB，这是一个专为蛋白质-配体复合物上的自监督GCL设计的大规模、结构感知数据集。DecoyDB包含高分辨率真实复合物（小于2.5埃）以及多样化的诱饵结构，这些结构带有计算生成的结合姿态，范围从接近真实到次优（负对）。每个诱饵都标注了与天然姿态的均方根偏差（RMSD）。我们进一步设计了一个定制的GCL框架，基于DecoyDB预训练图神经网络，并使用PDBbind的标签微调模型。大量实验证实，使用DecoyDB预训练的模型在准确性、标签效率和泛化能力方面均表现优异。
英文摘要：
Predicting the binding affinity of protein-ligand complexes plays a vital role in drug discovery. Unfortunately, progress has been hindered by the lack of large-scale and high-quality binding affinity labels. The widely used PDBbind dataset has fewer than 20K labeled complexes. Self-supervised learning, especially graph contrastive learning (GCL), provides a unique opportunity to break the barrier by pre-training graph neural network models based on vast unlabeled complexes and fine-tuning the models on much fewer labeled complexes. However, the problem faces unique challenges, including a lack of a comprehensive unlabeled dataset with well-defined positive/negative complex pairs and the need to design GCL algorithms that incorporate the unique characteristics of such data. To fill the gap, we propose DecoyDB, a large-scale, structure-aware dataset specifically designed for self-supervised GCL on protein-ligand complexes. DecoyDB consists of high-resolution ground truth complexes (less than 2.5 Angstrom) and diverse decoy structures with computationally generated binding poses that range from realistic to suboptimal (negative pairs). Each decoy is annotated with a Root Mean Squared Deviation (RMSD) from the native pose. We further design a customized GCL framework to pre-train graph neural networks based on DecoyDB and fine-tune the models with labels from PDBbind. Extensive experiments confirm that models pre-trained with DecoyDB achieve superior accuracy, label efficiency, and generalizability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
