  Machine learning, notably deep learning, has significantly propelled
molecular investigations within the biochemical sphere. Traditionally, modeling
for such research has centered around a handful of paradigms. For instance, the
prediction paradigm is frequently deployed for tasks such as molecular property
prediction. To enhance the generation and decipherability of purely data-driven
models, scholars have integrated biochemical domain knowledge into these
molecular study models. This integration has sparked a surge in paradigm
transfer, which is solving one molecular learning task by reformulating it as
another one. With the emergence of Large Language Models, these paradigms have
demonstrated an escalating trend towards harmonized unification. In this work,
we delineate a literature survey focused on knowledge-informed molecular
learning from the perspective of paradigm transfer. We classify the paradigms,
scrutinize their methodologies, and dissect the contribution of domain
knowledge. Moreover, we encapsulate prevailing trends and identify intriguing
avenues for future exploration in molecular learning.
