MLDR 1.0: A Machine Learning based web-server for Drug Repositioning
Drug repositioning is a cutting-edge field in drug development (DD) that
enables the discovery of new pharmacological applications for already-approved (or investigational) drugs. This strategy, which allows to skipping
the firsts DD phases related with the assessment of the drug safety, has
gained increased interest in part because it can accelerate (diminishing
between 5-7 years) and reduce the costs of the drug development processes
(Hua Y et al., 2022; Krishnamurthy N. et al., 2022). Several approaches
have been recently reported for the candidate identification, most of them
based in computational tools that comprise methods such as the discovery
of similar transcriptomic signature among different known drugs (Jia Z.
et al., 2021)), the use of high throughput virtual screening strategies (Gan J.
honget al., 2022)), the discovery of novel drug-targets interaction through
system biology approaches (Pandita V. et al., 2022) and the detection of
similar binding sites among protein structures (Ab Ghani N.S. et al., 2019).