In this mini-review, we explore the new prediction methods for drug
combination synergy relying on high-throughput combinatorial screens. The fast
progress of the field is witnessed in the more than thirty original machine
learning methods published since 2021, a clear majority of them based on deep
learning techniques. We aim to put these papers under a unifying lens by
highlighting the core technologies, the data sources, the input data types and
synergy scores used in the methods, as well as the prediction scenarios and
evaluation protocols that the papers deal with. Our finding is that the best
methods accurately solve the synergy prediction scenarios involving known drugs
or cell lines while the scenarios involving new drugs or cell lines still fall
short of an accurate prediction level.

本文通过探索基于高通量组合筛选的新的药物组合协同性预测方法，总结了 2021 年以来发表的三十多种原始机器学习方法，其中绝大多数基于深度学习技术。我们旨在通过突出核心技术、数据来源、输入数据类型、以及方法中使用的协同性评分、预测场景和评估协议，对这些论文进行统一的观察。我们发现，最好的方法能够准确解决已知药物或细胞系参与的协同性预测，然而当涉及到新的药物或细胞系时，准确预测的水平仍然有待提高。