ABSTRACT
Computational drug repositioning (CDR) is today an assessed research field taking advantage of contributions from a wide range of computer science, mathematics, and statistics areas, with proven efficacy in different therapeutical contexts including neuropharmacology. This chapter is devoted to the presentation of some of the main technical tools currently used in CDR. However, the chosen techniques should provide enough insight into the field for the reader to get started and figure out its main potentials and limitations. The chapter introduces the fundamentals of machine learning (ML), followed by concepts related to the use of CDR-relevant publicly available resources. From an application point of view, two main areas of ML have been formalized in literature, both of which find a wide range of applications in the context of CDR: unsupervised and supervised learning. The chapter presents a selection of the state-of-the-art CDR methodologies with applications to neurotherapeutics.
