Abstract

Three stochastic optimization algorithms (Simulated Annealing (SA), Evolution Strategy (ES), and Particle Swarm Optimization (PSO)) and a Random Search were assessed for their ability to generate small activity-enriched subsets of molecular compound libraries. The optimization algorithms were employed to perform an "intelligent" iterative sampling of library molecules avoiding the biological testing of the full library. This study was performed to find a suitable optimization algorithm along with suitable parametrization. Particularly, the optimal number of iterations and population size were of interest. Optimizations were performed with limited resources as the maximal number of compound evaluations was restricted to 300. Results show that all three optimization algorithms are able to produce comparably good results, clearly outperforming a Random Search. While ES was able to come up with good solutions after a few optimization cycles, SA favored high numbers of iterations and was therefore less suited for library design. We introduce PSOs as an alternative approach to focused library design. PSO was able to produce high quality solutions while exhibiting marked autoadaptivity. Its implicit step size control makes it a straightforward out-of-the-box optimization algorithm. We further demonstrate that a nearest neighbor algorithm can successfully be applied to map from continuous search space to discrete chemical space.