通过相似分子对的相对差异学习，提升小样本下的药物活性预测精度。
Similarity-Quantized Relative Difference Learning for Improved Molecular Activity Prediction
- 将分子活性预测转为相似分子对的相对差异学习，利用预计算相似度增强训练。
- 在低数据场景下显著提升图神经网络的准确率与泛化能力，效果优于传统方法。
- 适用于医药研发中的小样本场景，尤其适合数据稀疏的真实工业数据。
精准预测分子活性对高效药物发现至关重要，但受限于数据量少且噪声多的挑战。本文提出相似性量化相对学习（SQRL），将分子活性预测重构为结构相似化合物对之间的相对差异学习。SQRL利用预先计算的分子相似度来增强图神经网络等模型的训练，在药物发现中常见的低数据环境下显著提升预测准确率与泛化性能。通过在公开数据集和专有工业数据上的基准测试，验证了该方法的广泛适用性和实际潜力。结果表明，利用感知相似性的相对差异学习是一种有效的分子活性预测新范式。
原文摘要 · Abstract (English)
Accurate prediction of molecular activities is crucial for efficient drug discovery, yet remains challenging due to limited and noisy datasets. We introduce Similarity-Quantized Relative Learning (SQRL), a learning framework that reformulates molecular activity prediction as relative difference learning between structurally similar pairs of compounds. SQRL uses precomputed molecular similarities to enhance training of graph neural networks and other architectures, and significantly improves accuracy and generalization in low-data regimes common in drug discovery. We demonstrate its broad applicability and real-world potential through benchmarking on public datasets as well as proprietary industry data. Our findings demonstrate that leveraging similarity-aware relative differences provides an effective paradigm for molecular activity prediction.
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