New strategy for Turbo Similarity Searching: Implementation and testing

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.