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The alignment between the query conformer and database conformer is determined using the retained rotational matrices and translational vectors relative to that reference shape.
Instead, finding a common reference shape triggers computing, via Eq. 2, the shape Tanimoto between the query conformer and database conformer, as may be performed by a typical brute-force ROCS approach.
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Q is the query conformer.
In contrast, ROCS requires optimizing the overlay of the query conformer to all fifteen million database conformers.
The procedure to align the query conformer, Q, and database conformer, D, is the following (as depicted in Figure 5).
In contrast to ROCS, AR can only occur when the query conformer structure is found to have a reference shape in common with a database conformer.
The alignment-recycling (AR) methodology is intended to obviate performing the optimization required to maximize the volume overlap of the query conformer to each and every conformer in a 3D conformer dataset.
Innovative overlay-based approaches [14, 15] have been created to avoid brute-force comparison between a query conformer and each and every conformer in a 3D database.
This trivial transformation, while specific to alignment of a reference conformer, when applied, can yield a relatively accurate shape overlay between the query and database conformers without the need to perform the conformer overlay alignment optimization.
As shown in Table 2, the average conformer counts between the Query set and Search set are similar, with the query set being slightly less flexible.
C Conformer Multiple conformer, All-conformer-pair approach Similarity scores that one may expect when a single "query" conformer is compared to a set of multiple diverse conformers of the "database" compounds, using the "all-conformer-pair" approach.
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