Transfer learning is a concept whereby data-driven models can be developed for tasks (e.g. molecular properties) with limited data availability (target task) by sharing information from a related task. In the context of chemical engineering, the two tasks can either pertain to related properties or to the same property calculated or measured in two different ways (with differing accuracies or resolution). Using an ensemble of linear and interpretable models, in this work, we present a conceptual study to explicate when transfer learning can be beneficial. We show that a large overlap of the underlying features of the two tasks (specifically greater than 50%) is required for transfer learning to improve the model for the target task. On the other hand, transferring information (in particular, information regarding salient features) from an uncorrelated task can be detrimental to train a model for the target task. Subsequently, we present three illustrative examples of transfer learning for molecular property prediction and rationalize the usefulness of transferred information based on the inferences from our conceptual studies. This work, thus, provides a simplified analysis of the concept of transfer learning for building molecular property models.

迁移学习是一个概念，通过共享来自相关任务的信息，可以为具有有限数据可用性（目标任务）的任务（例如分子特性）开发数据驱动模型。在化学工程的背景下，这两个任务可以与相关属性有关，也可以与以两种不同方式（具有不同的精度或分辨率）计算或测量的相同属性有关。使用一组线性和可解释模型，在这项工作中，我们提出了一项概念研究，以说明迁移学习何时是有益的。我们表明，迁移学习需要两个任务的基本特征有很大的重叠（特别是大于 50%），以改进目标任务的模型。另一方面，传输信息（特别是，有关显着特征的信息）来自不相关的任务可能不利于为目标任务训练模型。随后，我们展示了三个用于分子特性预测的迁移学习示例，并根据我们的概念研究的推论来合理化迁移信息的有用性。因此，这项工作提供了对用于构建分子特性模型的迁移学习概念的简化分析。