In the field of artificial intelligence for science, it is consistently an essential challenge to face a limited amount of labeled data for real-world problems. The prevailing approach is to pretrain a powerful task-agnostic model on a large unlabeled corpus but may struggle to transfer knowledge to downstream tasks. In this study, we propose InstructMol, a semi-supervised learning algorithm, to take better advantage of unlabeled examples. It introduces an instructor model to provide the confidence ratios as the measurement of pseudo-labels' reliability. These confidence scores then guide the target model to pay distinct attention to different data points, avoiding the over-reliance on labeled data and the negative influence of incorrect pseudo-annotations. Comprehensive experiments show that InstructBio substantially improves the generalization ability of molecular models, in not only molecular property predictions but also activity cliff estimations, demonstrating the superiority of the proposed method. Furthermore, our evidence indicates that InstructBio can be equipped with cutting-edge pretraining methods and used to establish large-scale and task-specific pseudo-labeled molecular datasets, which reduces the predictive errors and shortens the training process. Our work provides strong evidence that semi-supervised learning can be a promising tool to overcome the data scarcity limitation and advance molecular representation learning.

翻译：在人工智能驱动的科学领域中，面对真实世界问题中有限数量的标注数据始终是一项核心挑战。当前主流方法是在大规模无标注语料库上预训练一个强大的任务无关模型，但这种方法可能难以将知识迁移至下游任务。在本研究中，我们提出一种半监督学习算法InstructMol，旨在更有效地利用无标注样本。该算法引入了一个指导模型，用于提供置信度比率作为伪标签可靠性的度量。这些置信度分数进而引导目标模型对不同数据点施加差异化关注，从而避免对标注数据的过度依赖以及错误伪标注带来的负面影响。综合性实验表明，InstructBio不仅在分子属性预测任务中，而且在活性悬崖估计任务中均显著提升了分子模型的泛化能力，证明了所提出方法的优越性。此外，我们的证据表明，InstructBio可与尖端预训练方法结合，用于构建大规模且任务特定的伪标注分子数据集，从而减少预测误差并缩短训练流程。本工作提供了有力证据，证明半监督学习能够成为克服数据稀缺限制并推进分子表示学习的有前景工具。