Introduction: The complexity in the drug discovery pipeline, in combination with the exponential growth of experimental and computational data, the technological achievements, and the access to large data sets, has led to a continuous evolution and transformation of quantitative structure–activity relationships (QSAR) to compete with the challenges of multi-objective drug discovery.
Areas covered: After a short overview of the multiple objectives involved in drug discovery, this review focuses on definition of the drug-like space and the construction of local and/or global models, platforms and workflows for step-by-step single-objective optimization (SOO) of the different and often conflicting processes. Multi-targeted drug design is a particular case of multi-objective QSAR integrated into the new era of polypharmacology. Multi-objective optimization (MOO), based on desirability functions or Pareto surfaces and its application in QSAR, as an alternative optimization philosophy, is also discussed.
Expert opinion: Access to large databases as well as to software services by means of cloud technology facilitates research for more efficient and safer drugs. QSAR models implemented in web platforms and workflows provide sequential SOO for multiple biological and toxicity end points, while MOO, still restricted to a limited number of objectives, is helpful for multi-target or selectivity design, as well as for model prioritization.