Molecular optimization using computational multi-objective methods.

Improving the profile of a molecule for the drug-discovery process requires the simultaneous optimization of numerous, often competing objectives. Traditionally, standard chemoinformatics methods ignored this problem and focused on the sequential optimization of each single biological or chemical property (ie, a single objective). This approach, known as single-objective optimization (SOOP), strives to discover a single optimal solution to the optimization problem. Implicitly, SOOP-based methods assume that the optimal solution for an objective will also be the optimum for any other objectives involved in the profiling of a molecule. However, when these other objectives are conflicting, as is often the case in drug discovery, the individual optima corresponding to the numerous objectives may vary substantially. Multi-objective optimization (MOOP) methods introduce a new approach for gaining optimality based on compromises and trade-offs among the various objectives. MOOP aims to discover a set of satisfactory compromises that can in turn be used to discover the global optimal solution by optimizing numerous dependent properties simultaneously. MOOP methods have only recently been introduced to the field of chemoinformatics. This paper first presents a brief introduction to issues related to MOOP and then surveys the application of MOOP methods in the field of chemoinformatics.