用自动优化方法提升分子性质预测的多任务迁移学习效果。
Scalable Multi-Task Transfer Learning for Molecular Property Prediction
- 通过数据驱动的双层优化自动寻找最优迁移比例。
- 在40个分子性质预测任务中提升性能并加速收敛。
- 适合需要高效多任务建模的药物研发与材料设计人员。
分子具有多种不同性质，其重要性和应用各不相同。现实中，某些关键性质的标签难以获取。为应对数据稀缺问题，常采用具有良好泛化能力的迁移学习模型。传统方法依赖领域专家设计源任务与目标任务的配对以共享特征，但存在两大局限：一是因任务数量庞大，难以准确设计源-目标任务对；二是需大量试错验证迁移学习设计，带来显著计算负担，限制了多任务分子性质预测基础模型的潜力。本文提出一种数据驱动的双层优化方法，解决人工设计迁移学习的瓶颈。该方法可自动获取最优迁移比例，实现可扩展的多任务分子性质预测迁移学习。实证表明，该方法提升了40个分子性质的预测性能，并加速了训练收敛。
原文摘要 · Abstract (English)
Molecules have a number of distinct properties whose importance and application vary. Often, in reality, labels for some properties are hard to achieve despite their practical importance. A common solution to such data scarcity is to use models of good generalization with transfer learning. This involves domain experts for designing source and target tasks whose features are shared. However, this approach has limitations: i). Difficulty in accurate design of source-target task pairs due to the large number of tasks, and ii). corresponding computational burden verifying many trials and errors of transfer learning design, thereby iii). constraining the potential of foundation modeling of multi-task molecular property prediction. We address the limitations of the manual design of transfer learning via data-driven bi-level optimization. The proposed method enables scalable multi-task transfer learning for molecular property prediction by automatically obtaining the optimal transfer ratios. Empirically, the proposed method improved the prediction performance of 40 molecular properties and accelerated training convergence.
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