Knowledge-informed Molecular Learning: A Survey on Paradigm Transfer
This is an incremental survey paper that synthesizes existing research on knowledge-informed molecular learning for researchers in biochemistry and AI.
This survey examines how integrating biochemical domain knowledge into machine learning models for molecular studies has led to paradigm transfer, where tasks are reformulated to enhance model generation and interpretability, and it identifies trends and future directions in the field.
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.