Sentence examples for pose sampling from inspiring English sources

The pharmacophore-based pose sampling and ranking scheme was deployed to efficiently filter out ligand binding poses that are unlikely to be the native binding pose.

In our previous study [10], we have investigated the influence of clustering distance cutoff of each pharmacophore type on the ligand pose sampling accuracy and efficiency.

Starting from pre-generated ligand and protein-based pharmacophores, we extended our pharmacophore-based pose sampling and ranking into a docking program, named PharmDock.

We found that pharmacophore models comprised by only hydrophobic and hydrogen bond elements, which are generated using a distance cutoff of 1.5 Å and 2.0 Å respectively, provide the best compromise between pose sampling accuracy and efficiency.

In PharmDock simulations, the native ligand conformation is seeded within the Omega-generated low energy conformations, because PharmDock does not generate ligand conformations on-the-fly during pose sampling.

In this publication, we present a new pharmacophore-based docking program PharmDock that combines pose sampling and ranking based on optimized protein-based pharmacophore models with local optimization using an empirical scoring function.

The ligand poses sampled by PharmDock are initially scored and ranked using a simple geometric function based on the matching pharmacophore pairs formed by each ligand pharmacophore and its closest protein-based pharmacophore of the same type: S = ‒ 0.7 * ∑ hbond f r ‒ 0.4 * ∑ hphob f r ‒ 0.6 * ∑ arom f r ‒ 0.6 * ∑ ionic f r (1).

The overall docking process contains the following subsequent steps: Poses sampling: PharmDock samples binding poses by enumerating all possible multiple-points matches between the pre-generated ligand and protein-based pharmacophores and subsequent alignment of common features.

Then, a new particle pose is sampled; this is illustrated in Algorithm 2. The proposal distribution construction procedure can be parallelized on GPU pipelines, since each operation can be done independently for each particle.

Both Poses Sampling and Poses Ranking are based on the representation of potential interactions as potential pharmacophores.

The newly sampled pose is accepted based on several criteria: 1) If the RMSD of the current ligand pose compared to the starting pose is larger than 3.5 Å, the current pose is rejected and coordinates of the starting pose are reassigned to the ligand.