We have shown in a previous study that protein-based pharmacophore models can be applied for ligand pose prediction and pose ranking.
Consequently, a direct application of the protein-based pharmacophore models is to use them for ligand pose prediction and pose ranking.
Inspired by the promising results in ligand pose prediction and pose ranking using protein-based pharmacophore models, we extended the pharmacophore-based matching and scoring scheme into a docking program, named PharmDock (Pharmacophore-based Docking).
Third, while aggregating a bunch of polls for each election reduces the variance, it does not eliminate the bias, so these overconfident predictions pose a problem for aggregate forecasts as well.
Effects of nongenetic inheritance on offspring fitness may translate into long-term consequences for population persistence and adaptation (Vandegehuchte and Janssen 2011), but long-term predictions pose a challenge.
A large model uncertainty leads to a large variation in the model prediction, which poses a significant challenge in a geotechnical design.
In the light of the new experiments, however, success of the energy-minimization prediction method poses a new question.
However, a substantial source of error may lie in the high false positive rate of computational miRNA target prediction, which poses a challenge for understanding the impact of miRNAs on gene expression regulation (Alexiou et al. 2009).
The average RMSD of the predicted pose to the experiment was 2.0 Å (diverse set), 1.8 Å (phenol-derived predictions), and 1.2 Å (catechol-derived predictions).
