  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.
