Virtual screening is one of the most vital methods applied in Chemoinformatics, the field that contributes to drug discovery process. Turbo Similarity Searching (TSS) and data fusion are two of the latest chemical similarity searching strategies, which has evolved from the conventional similarity searching (SS) that apply the concept of multi-target searching instead of just an individual target search. The indirect relationship exists in TSS, with the inclusion of Nearest Neighbours (NN) has been proven to have better performance than the direct relationship (i.e. between query structure and database structures) that exists in similarity searching process. In this paper, we will focus on the implementation and improvement of the existing TSS. By adding in another layer of indirect relationship between the reference compound and the database compounds, along with an additional fusion layer, the performance of the new TSS strategy can be observed. The initial results indicated that there is an obvious increment in the recall value when applying the new strategy. The results are also evaluated with the significance test to show that the result produced by the new strategy is true and does not occurred by chance. Further work on different activity classes and different descriptors on the new strategy are expected to generate a better performance than the existing TSS.

Background:Turbo Similarity Searching (TSS) has been proved as one of the effective and simple searching method in Cheminformatics. Emerging from the conventional similarity searching, TSS depended on the concept of fusion where relationship between the target being sought and the compound in the database are indirect. Previous works has looked at only one level of indirect relationship and indicates that there are further potential that more levels of such relationship be added to TSS to increase its ability to recover more actives. Hence, in this work, we aimed to investigate the impact of the indirect relationship on TSS. Method:This study has further investigated the enhancement of TSS using additional layers of indirect relationship and fusion process. We implemented TSS by adding another layer of fusion between the target and database compound. Results:The experiments with MDDR database showed that the proposed new strategy described in this paper provide a way of enhancing the effectiveness of the TSS process in chemical databases. The experiments also showed that the increases in performance are particularly better when the sought actives are structurally diverse. Conclusion:We may conclude that the additional layers do increase the recall of TSS. Hence, the new TSS strategy could be used as an alternative to the old TSS.