The adjustment of multiple criteria in hit-to-lead identification and lead optimization is a major advance in
drug discovery. Thus, the development of approaches able to handle additional criteria for the early simultaneous
treatment of the most important properties determining the pharmaceutical profile of a drug candidate is an
emergent issue in this area. In this paper, we review a desirability-based multi-objective QSAR method allowing
the joint handling of multiple properties of interest in drug discovery: the MOOP-DESIRE methodology. This
methodology adapts desirability theory concepts allowing the holistic modeling of the many and conflicting biological
properties determining the therapeutic utility of a drug candidate. Here we survey their suitability for key tasks
involving the use of chemoinformatics methods in medicinal chemistry and drug discovery.
Keywords: MOOP-DESIRE methodology, desirability theory, multi-objective QSAR, drug discovery.
Rights & PermissionsPrintExport