For each optimization task, we run the PSO algorithm 100 times for 100 steps each. Table shows the summarized results with the average, highest and lowest scores. For the drug-likeness and the penalized logP tasks, we compare our method to the best results of state-of-the-art optimization models as reported by You et al. . Our proposed method achieves the same performance on the drug-likeness task as the best state-of-the-art approach and outperforms all other approaches on the penalized logP task. Moreover, our method consistently generated molecules with very high predicted binding-affinities (IC50 < 1 nM) for both biological targets respectively. Figure depicts the average scores during the optimization process for the different tasks. To better understand the impact of the starting point for the optimization, we show Table : Results for the different single-objective optimization tasks. Our Method is compared to the results of three recently published optimizations methods as reported in .