The ligands of the top ranked templates are used as template ligands for searching against compound library.
Similar as in FINDSITEfilt, the ligands of the top ranked templates are used as reference small molecules for ligand based virtual screening, where the appropriate template ligands are obtained from the DrugBank [48] and ChEMBL [49] databases.
To identify and rank templates, we used profile profile alignments, profile sequence alignments and a machine learning approach (Cheng and Baldi, 2006).
This is to say that for easy targets, MULTICOM-CMFR (or MULTICOM-RANK) used only PSI-BLAST (or PSI-BLAST and then hhsearch) to identify and rank templates according to E-value as in MULTICOM-CLUSTER.
To speed-up, our method first aligns a target to all the templates using only the local alignment potential and then ranks all the templates using both the local and global alignment potentials described in Section 2. After ranking, only the first-ranked templates are used to build a 3D model by MODELLER for each target.
CNFpred first generates target template alignments and then ranks the templates using a neural network, which predicts the quality (TM-score) of an alignment.
In the initialization period, the first stage initializes, without priori knowledge of signal power ranking, the template of the stronger source, which is canceled from the composite ECG signal prior to initialization of the weaker source's template in the second stage.
To rank the templates of a query sequence, if the maximal Sn1 is greater than or equal to the maximal Sn2, they will be ranked by Sn1, and otherwise by Sn2.
The rationale behind VORESCORE is the following: if the prediction method mistakenly ranks a certain template best, e.g. because of chance similarities with the wrong and missing sequence similarities with the correct template, chances are high that the VORPSI rescoring will also be mistaken.
This template ranks dissemination activities or tools from mostly effective (for example, educational outreach, combined interventions) to unknown effects (for example, arts-based KT, social media).
