论文标题
知识信息分子学习：范式转移的调查
Knowledge-informed Molecular Learning: A Survey on Paradigm Transfer
论文作者
论文摘要
机器学习，尤其是深度学习，在生化领域中显着推动了分子研究。传统上，此类研究的建模围绕着少数范式。例如，预测范式经常用于诸如分子属性预测之类的任务。为了增强纯粹数据驱动模型的产生和解密性，学者将生化领域知识综合为这些分子研究模型。这种整合引发了范式转移的激增，该范式转移正在通过将其重构为另一个分子学习来解决一项分子学习任务。随着大型语言模型的出现，这些范式表明朝着协调统一的趋势不断升级。在这项工作中，我们从范式转移的角度描述了一项文献调查，该文献调查着眼于知识知识分子学习。我们对范式进行分类，仔细检查其方法，并剖析领域知识的贡献。此外，我们封装了普遍的趋势，并确定了分子学习未来探索的有趣途径。
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.
