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  • inhibitor
  • These plots are of VM2 calculated binding free energy predictions versus experimental binding affinities for the protein receptors P38A kinase, HIV-1 protease, PDE10A, and BRCA1, and corresponding inhibitor series ranging between 16 and 38 ligands. (verachem.com)
  • protein
  • Mouse RANK-Ligand is a recombinant protein optimized for use in cell culture, differentiation studies, and functional assays. (miltenyibiotec.com)
  • Several mitogen activated protein kinases (MAPK's) have been shown to be activated downstream of RANK. (wikipathways.org)
  • VM2 can provide accurate protein-ligand binding affinities, and recent advances, such as parallel processor implementations for CPU and GPU clusters, as well as ports to Cloud platforms, now allow routine use of VM2 for rank-ordering of ligand series in pharmaceutical industry drug development settings. (verachem.com)
  • Accurate predictions of protein-ligand binding affinities, therefore, provide the potential for tremendous savings with respect to cost and time in the drug development process. (verachem.com)
  • VeraChem's state of the art computational chemistry software is capable of protein-ligand and host-guest binding affinity prediction, fast calculation of accurate partial atomic charges for drug-like compounds, computation of energies and forces with empirical force fields, automatic generation of alternate resonance forms of drug-like compounds, conformational search with the powerful Tork distort-minimize algorithm, and automatic detection of topological and 3D molecular symmetries. (verachem.com)
  • The ligand doesn't "see" the protein, but the exclusion map prevents it from sampling the region where no probe or water molecules visited during the SILCS simulations, i.e., interior of the protein. (silcsbio.com)
  • molecule
  • The pocket here is predefined as a 10 Å sphere, and the center of the pocket is either defined as the center of the supplied ligand molecule or explicitly given by the user. (silcsbio.com)
  • Binding
  • RANK-Ligand is expressed in several tissues and cell types, including fibroblasts, T cells, and osteoblasts, and functions by binding its cell surface signaling receptor RANK. (miltenyibiotec.com)
  • Six Learning to Rank algorithms were investigated based on two public datasets collected from Binding Database and the newly-published Community Structure-Activity Resource benchmark dataset. (springeropen.com)
  • Generally, the task of ligand-based VS is to output a ranking list of a set of molecules in terms of their binding affinities for a given drug target, so that the top- k molecules can be further examined through in - vivo or in - vitro test. (springeropen.com)
  • It recruits adaptor molecules to transduce the signal after ligand binding. (wikipathways.org)
  • As the name suggests, this approach enables an exhaustive sampling of a ligand conformation in a given pocket to determine a most favorable binding pose based on LGFE scoring. (silcsbio.com)
  • This protocol is recommended to sample and score ligands with diverse chemotype and unknown binding poses. (silcsbio.com)
  • Circulating RANK
  • Differences in Volumetric Percent Density by Tertiles of Circulating RANK and sRANKL. (aacrjournals.org)
  • Least Square Means of Non-Dense Volume by Tertiles of Circulating RANK and sRANKL. (aacrjournals.org)
  • We used linear regression models adjusted for confounders, to compare the least-square means of volumetric percent density across tertiles of circulating RANK and sRANKL. (aacrjournals.org)
  • The mean volumetric percent density increased across tertiles of circulating RANK from 8.6% in tertile 1, to 8.8% in tertile 2, and 9.5% in tertile 3 ( P trend = 0.02). (aacrjournals.org)
  • Circulating RANK was positively associated with volumetric percent density, while circulating sRANKL was positively associated with volumetric percent density among women with higher progesterone levels. (aacrjournals.org)
  • computational
  • The results have demonstrated that Learning to rank is an efficient computational strategy for drug virtual screening, particularly due to its novel use in cross-target virtual screening and heterogeneous data integration. (springeropen.com)
  • Particularly, computational methods that "rank" chemical structures based on their likelihood of clinical success are useful for large-scale compounds screening. (springeropen.com)
  • Recently, a new emerging computational strategy called Learning to Rank ( LOR ) [ 6 , 7 ] that was firstly utilized in information retrieval field especially for the web search, has gained much attention. (springeropen.com)
  • known
  • When the pose of a parent ligand is known and MC- SILCS evaluations are to be performed over a congeneric series, another procedure called "local search" is recommended (see below). (silcsbio.com)
  • Drug
  • In this study, the idea of Learning to Rank in web search was presented in drug virtual screening, which has the following unique capabilities of 1). (springeropen.com)
  • cycle
  • At the beginning of each cycle, the ligand will be reoriented within the predefined sphere. (silcsbio.com)
  • When no sphere center is defined by the user, ligand orientation defined in the Mol2/sd file will be used instead as a starting pose at the start of every cycle. (silcsbio.com)
  • system
  • This procedure of exhaustive sampling is spawned as five independent single- core serial jobs per ligand and take an average between 30 minutes to one hour to complete for a single ligand (depending on system architecture). (silcsbio.com)