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).