Here, we provide a comprehensive overview of the current status of in silico
repurposing methods by establishing links between current technological
trends, data availability and characteristics of the algorithms used in these
methods. Using the case of the computational repurposing of fasudil as an
alternative autophagy enhancer, we suggest a generic modular
organization of a repurposing workflow. We also review 3D structurebased,
similarity-based, inference-based and machine learning (ML)-based
methods. We summarize the advantages and disadvantages of these
methods to emphasize three current technical challenges. We finish by
discussing current directions of research, including possibilities offered by
new methods, such as deep learning.