Editorial [Hot Topic:Multivariate QSAR Methods (Guest Editor: Peter P. Mager Co-Guest Editor: Matheus P. Freitas)]

摘要

Increasing efforts to estimate the activity or any other biological property of a given compound have been made since the advent of in silico approaches for drug design. In ligand-based QSAR/QSPR methodologies, descriptors are used to correlate samples set with the corresponding dependent variables (bioactivities, toxicity, etc.), in lieu of testing experimentally the response of a drug-like compound. A large amount of information is usually required or produced to achieve such correlation, and then multivariate analysis has been invoked to manipulate the generated data in order to investigate their variance. Accordingly, chemometric techniques for regression, data exploration and variable selection must be capable to reduce dimensionality and allow the building of predictive models. The present Hot Topic Issue of Current Computer-Aided Drug Design (CC-ADD) provides comprehensive reviews of several multivariate QSAR methods, as well as variable selection and docking studies, covering useful aspects in multivariate modeling applied to drug discovery. In addition to the methodologies used for fast developing computer-aided drug design, their applications and end results are also presented to the scientific community involved in the prediction of drug targets.