Multi- and Many-Objective Optimisation: Present and Future in de novo Drug Design

de novo Drug Design (dnDD) aims to create new molecules that satisfy multiple conflicting objectives. Since several desired properties can be considered in the optimisation process, dnDD is naturally categorised as a many-objective optimisation problem (ManyOOP), where more than three objectives must be simultaneously optimised. However, a large number of objectives typically pose several challenges that affect the choice and the design of optimisation methodologies. Herein, we cover the application of multi- and many-objective optimisation methods, particularly those based on Evolutionary Computation and Machine Learning techniques, to enlighten their potential application in dnDD. Additionally, we comprehensively analyse how molecular properties used in the optimisation process are applied as either objectives or constraints to the problem. Finally, we discuss future research in many-objective optimisation for dnDD, highlighting two important possible impacts: i) its integration with the development of multi-target approaches to accelerate the discovery of innovative and more efficacious drug therapies and ii) its role as a catalyst for new developments in more fundamental and general methodological frameworks in the field.