Evaluation of various inverse docking schemes in multiple targets identification

The lack of accurate and efficient methods for target identification has been the bottleneck in drug discovery. In recent years, inverse docking has been applied as an efficient method in target identification, and several specific inverse docking strategies have been employed in academic and industrial researches. However, the effectiveness of these docking strategies in multiple targets identification is unclear. In this study, five inverse docking schemes were evaluated to find out the most effective approach in multiple targets identification. A target database containing a highly qualified dataset that is composed of 1714 entries from 1594 known drug targets covering 18 biochemical functions was collected as a testing pool for inverse docking. The inverse docking engines including GOLD, FlexX, Tarfisdock and two in-house target search schemes TarSearch-X and TarSearch-M were evaluated by eight multiple target systems in the dataset. The results show that TarSearch-X is the most effective method in multiple targets identification and validation among these five schemes, and the effectiveness of GOLD in multiple targets identification is also acceptable. Moreover, these two inverse docking strategies will also be helpful in predicting the undesirable effects of drugs, such as toxicity.