Tópico 53

Palavras

compound ( 1573 )
activ ( 1297 )
structur ( 1058 )
screen ( 1045 )
chemic ( 811 )
set ( 807 )
target ( 792 )
virtual ( 565 )
drug ( 511 )
molecul ( 478 )
similar ( 464 )
molecular ( 435 )
novel ( 408 )
librari ( 406 )
divers ( 383 )
select ( 381 )
descriptor ( 377 )
discoveri ( 338 )
predict ( 333 )
databas ( 301 )
identifi ( 279 )
fragment ( 260 )
known ( 249 )
hit ( 242 )
relationship ( 239 )
repres ( 232 )
space ( 228 )
biolog ( 210 )
enrich ( 208 )
larg ( 204 )
inhibitor ( 187 )
atom ( 182 )
lead ( 176 )
generat ( 175 )
small ( 169 )
properti ( 168 )
identif ( 167 )
candid ( 157 )
avail ( 155 )
class ( 152 )
thus ( 145 )
contain ( 101 )
profil ( 87 )
consensus ( 86 )
carri ( 83 )
attract ( 76 )
basi ( 73 )
commerci ( 72 )
inhibit ( 71 )
discov ( 70 )
therapeut ( 69 )
appli ( 68 )
experiment ( 66 )
core ( 60 )
subset ( 56 )
million ( 56 )
abil ( 50 )
unknown ( 48 )
far ( 47 )
synthesi ( 42 )
potenti ( 42 )
confirm ( 41 )
top ( 40 )
exhibit ( 40 )
agent ( 40 )
promis ( 38 )
around ( 38 )
desir ( 36 )
abl ( 35 )
synthet ( 33 )
systemat ( 32 )
highthroughput ( 30 )
distinct ( 30 )
serv ( 29 )
seri ( 27 )
organ ( 26 )
addit ( 26 )
develop ( 25 )
threedimension ( 24 )
freeli ( 24 )
start ( 23 )
better ( 23 )
like ( 22 )
valuabl ( 20 )
cover ( 19 )
three ( 17 )
princip ( 16 )
count ( 16 )
chosen ( 15 )
subsequ ( 14 )
number ( 14 )
establish ( 12 )
built ( 12 )
true ( 11 )
general ( 11 )
side ( 10 )
dock ( 10 )
recov ( 9 )
henc ( 9 )
mani ( 9 )

Resumos

J Chem Inf Model - How diverse are diversity assessment methods? A comparative analysis and benchmarking of molecular descriptor space. ( 77 )
J Chem Inf Model - Noncontiguous atom matching structural similarity function. ( 65 )
J Chem Inf Model - Locating sweet spots for screening hits and evaluating pan-assay interference filters from the performance analysis of two lead-like libraries. ( 53 )
J Chem Inf Model - Identification of 1,2,5-oxadiazoles as a new class of SENP2 inhibitors using structure based virtual screening. ( 52 )
J Chem Inf Model - FINDSITE(comb): a threading/structure-based, proteomic-scale virtual ligand screening approach. ( 50 )
J Chem Inf Model - Structural similarity based kriging for quantitative structure activity and property relationship modeling. ( 49 )
J Chem Inf Model - Novel inhibitor discovery through virtual screening against multiple protein conformations generated via ligand-directed modeling: a maternal embryonic leucine zipper kinase example. ( 49 )
J Chem Inf Model - Searching for recursively defined generic chemical patterns in nonenumerated fragment spaces. ( 47 )
J Chem Inf Model - Development of Ecom50 and retention index models for nontargeted metabolomics: identification of 1,3-dicyclohexylurea in human serum by HPLC/mass spectrometry. ( 46 )
J Chem Inf Model - Selection of in silico drug screening results for G-protein-coupled receptors by using universal active probes. ( 45 )
J Chem Inf Model - QSAR classification model for antibacterial compounds and its use in virtual screening. ( 45 )
J Chem Inf Model - Identifying compound-target associations by combining bioactivity profile similarity search and public databases mining. ( 44 )
J Chem Inf Model - Enrichment of chemical libraries docked to protein conformational ensembles and application to aldehyde dehydrogenase 2. ( 44 )
J Chem Inf Model - Introduction of target cliffs as a concept to identify and describe complex molecular selectivity patterns. ( 43 )
J Chem Inf Model - BioSM: metabolomics tool for identifying endogenous mammalian biochemical structures in chemical structure space. ( 43 )
J Chem Inf Model - Automatic tailoring and transplanting: a practical method that makes virtual screening more useful. ( 42 )
J Chem Inf Model - Identification of multitarget activity ridges in high-dimensional bioactivity spaces. ( 41 )
J Chem Inf Model - Profile-QSAR: a novel meta-QSAR method that combines activities across the kinase family to accurately predict affinity, selectivity, and cellular activity. ( 41 )
J Chem Inf Model - In silico enzymatic synthesis of a 400,000 compound biochemical database for nontargeted metabolomics. ( 40 )
J Chem Inf Model - Multitarget structure-activity relationships characterized by activity-difference maps and consensus similarity measure. ( 40 )
J Chem Inf Model - Automated recycling of chemistry for virtual screening and library design. ( 39 )
J Chem Inf Model - Optimization of molecular representativeness. ( 39 )
J Chem Inf Model - Novel mycosin protease MycP1 inhibitors identified by virtual screening and 4D fingerprints. ( 38 )
J Chem Inf Model - Identification of sumoylation activating enzyme 1 inhibitors by structure-based virtual screening. ( 38 )
J Chem Inf Model - Structure-based virtual screening of the nociceptin receptor: hybrid docking and shape-based approaches for improved hit identification. ( 38 )
J Chem Inf Model - Discovery of novel checkpoint kinase 1 inhibitors by virtual screening based on multiple crystal structures. ( 38 )
J Chem Inf Model - Binary classification of a large collection of environmental chemicals from estrogen receptor assays by quantitative structure-activity relationship and machine learning methods. ( 38 )
J Chem Inf Model - Prediction of new bioactive molecules using a Bayesian belief network. ( 37 )
J Chem Inf Model - Design of a three-dimensional multitarget activity landscape. ( 37 )
J Chem Inf Model - Identification of novel malarial cysteine protease inhibitors using structure-based virtual screening of a focused cysteine protease inhibitor library. ( 37 )
J Chem Inf Model - Conditional probabilistic analysis for prediction of the activity landscape and relative compound activities. ( 36 )
J Chem Inf Model - Normalizing molecular docking rankings using virtually generated decoys. ( 36 )
J Chem Inf Model - De novo design of drug-like molecules by a fragment-based molecular evolutionary approach. ( 36 )
J Chem Inf Model - SABRE: ligand/structure-based virtual screening approach using consensus molecular-shape pattern recognition. ( 36 )
J Chem Inf Model - Compound set enrichment: a novel approach to analysis of primary HTS data. ( 36 )
J Chem Inf Model - Polypharmacology directed compound data mining: identification of promiscuous chemotypes with different activity profiles and comparison to approved drugs. ( 36 )
J Chem Inf Model - Visual characterization and diversity quantification of chemical libraries: 1. creation of delimited reference chemical subspaces. ( 36 )
J Am Med Inform Assoc - Drug repurposing: mining protozoan proteomes for targets of known bioactive compounds. ( 35 )
J Chem Inf Model - Identification of a novel inhibitor of dengue virus protease through use of a virtual screening drug discovery Web portal. ( 35 )
J Chem Inf Model - Natural product-like virtual libraries: recursive atom-based enumeration. ( 35 )
J Chem Inf Model - Target-independent prediction of drug synergies using only drug lipophilicity. ( 35 )
J Chem Inf Model - Harvesting classification trees for drug discovery. ( 35 )
J Chem Inf Model - From activity cliffs to activity ridges: informative data structures for SAR analysis. ( 35 )
J Chem Inf Model - Predictions of BuChE inhibitors using support vector machine and naive Bayesian classification techniques in drug discovery. ( 34 )
J Chem Inf Model - Design of multitarget activity landscapes that capture hierarchical activity cliff distributions. ( 34 )
J Chem Inf Model - Structure based model for the prediction of phospholipidosis induction potential of small molecules. ( 33 )
J Chem Inf Model - Exploring polypharmacology using a ROCS-based target fishing approach. ( 33 )
J Chem Inf Model - G-protein coupled receptors virtual screening using genetic algorithm focused chemical space. ( 33 )
J Chem Inf Model - SAR monitoring of evolving compound data sets using activity landscapes. ( 33 )
J Chem Inf Model - Compound optimization through data set-dependent chemical transformations. ( 33 )
J Chem Inf Model - Identification of compounds with potential antibacterial activity against Mycobacterium through structure-based drug screening. ( 33 )
J Chem Inf Model - Capturing structure-activity relationships from chemogenomic spaces. ( 33 )
J Chem Inf Model - Bioturbo similarity searching: combining chemical and biological similarity to discover structurally diverse bioactive molecules. ( 33 )
J Chem Inf Model - Ligand- and structure-based virtual screening for clathrodin-derived human voltage-gated sodium channel modulators. ( 33 )
J Chem Inf Model - Feasibility of using molecular docking-based virtual screening for searching dual target kinase inhibitors. ( 32 )
J Chem Inf Model - Integrating medicinal chemistry, organic/combinatorial chemistry, and computational chemistry for the discovery of selective estrogen receptor modulators with Forecaster, a novel platform for drug discovery. ( 32 )
J Chem Inf Model - Structure based design, synthesis, pharmacophore modeling, virtual screening, and molecular docking studies for identification of novel cyclophilin D inhibitors. ( 32 )
Comput Biol Chem - The optimization of running time for a maximum common substructure-based algorithm and its application in drug design. ( 31 )
Curr Comput Aided Drug Des - Development of Chemical Compound Libraries for In Silico Drug Screening. ( 31 )
J Chem Inf Model - Target-specific support vector machine scoring in structure-based virtual screening: computational validation, in vitro testing in kinases, and effects on lung cancer cell proliferation. ( 31 )
J Chem Inf Model - A new protocol for predicting novel GSK-3? ATP competitive inhibitors. ( 31 )
J Chem Inf Model - Docking ligands into flexible and solvated macromolecules. 7. Impact of protein flexibility and water molecules on docking-based virtual screening accuracy. ( 31 )
J Chem Inf Model - Identification of novel potential antibiotics against Staphylococcus using structure-based drug screening targeting dihydrofolate reductase. ( 31 )
J Chem Inf Model - Application of quantitative structure-activity relationship models of 5-HT1A receptor binding to virtual screening identifies novel and potent 5-HT1A ligands. ( 31 )
J Chem Inf Model - Structure-based virtual screening approach for discovery of covalently bound ligands. ( 30 )
J Chem Inf Model - Neighborhood-based prediction of novel active compounds from SAR matrices. ( 30 )
J Chem Inf Model - How do 2D fingerprints detect structurally diverse active compounds? Revealing compound subset-specific fingerprint features through systematic selection. ( 30 )
J Chem Inf Model - ToxAlerts: a Web server of structural alerts for toxic chemicals and compounds with potential adverse reactions. ( 30 )
J Chem Inf Model - Freely available conformer generation methods: how good are they? ( 30 )
J Chem Inf Model - Fighting high molecular weight in bioactive molecules with sub-pharmacophore-based virtual screening. ( 30 )
J Chem Inf Model - TIN-a combinatorial compound collection of synthetically feasible multicomponent synthesis products. ( 30 )
J Chem Inf Model - Scanning structure-activity relationships with structure-activity similarity and related maps: from consensus activity cliffs to selectivity switches. ( 30 )
J Chem Inf Model - Activity-aware clustering of high throughput screening data and elucidation of orthogonal structure-activity relationships. ( 30 )
J Chem Inf Model - Virtual fragment screening: discovery of histamine H3 receptor ligands using ligand-based and protein-based molecular fingerprints. ( 29 )
J Chem Inf Model - Combining horizontal and vertical substructure relationships in scaffold hierarchies for activity prediction. ( 29 )
J Chem Inf Model - Benchmark data sets for structure-based computational target prediction. ( 29 )
J Chem Inf Model - Exploration of 3D activity cliffs on the basis of compound binding modes and comparison of 2D and 3D cliffs. ( 28 )
J Chem Inf Model - Complementarity between in silico and biophysical screening approaches in fragment-based lead discovery against the A(2A) adenosine receptor. ( 28 )
J Chem Inf Model - Molecular topology analysis of the differences between drugs, clinical candidate compounds, and bioactive molecules. ( 28 )
J Chem Inf Model - Rationalizing the role of SAR tolerance for ligand-based virtual screening. ( 28 )
J Chem Inf Model - Automated selection of compounds with physicochemical properties to maximize bioavailability and druglikeness. ( 28 )
J Chem Inf Model - SymDex: increasing the efficiency of chemical fingerprint similarity searches for comparing large chemical libraries by using query set indexing. ( 28 )
J Chem Inf Model - Application of support vector machine to three-dimensional shape-based virtual screening using comprehensive three-dimensional molecular shape overlay with known inhibitors. ( 28 )
J Chem Inf Model - Similarity boosted quantitative structure-activity relationship--a systematic study of enhancing structural descriptors by molecular similarity. ( 27 )
J Chem Inf Model - Novel method for pharmacophore analysis by examining the joint pharmacophore space. ( 27 )
J Chem Inf Model - Predicting myelosuppression of drugs from in silico models. ( 27 )
J Chem Inf Model - Comparative analysis of pharmacophore screening tools. ( 27 )
J Chem Inf Model - Chemical data visualization and analysis with incremental generative topographic mapping: big data challenge. ( 27 )
J Chem Inf Model - Discovery of novel histamine H4 and serotonin transporter ligands using the topological feature tree descriptor. ( 26 )
J Chem Inf Model - Sharing chemical relationships does not reveal structures. ( 26 )
J Chem Inf Model - Probing the bioactivity-relevant chemical space of robust reactions and common molecular building blocks. ( 26 )
J Chem Inf Model - Improving the use of ranking in virtual screening against HIV-1 integrase with triangular numbers and including ligand profiling with antitargets. ( 26 )
J Chem Inf Model - Scaffold diversity of exemplified medicinal chemistry space. ( 26 )
J Chem Inf Model - Multiple e-pharmacophore modeling, 3D-QSAR, and high-throughput virtual screening of hepatitis C virus NS5B polymerase inhibitors. ( 26 )
J Chem Inf Model - Predictive toxicology modeling: protocols for exploring hERG classification and Tetrahymena pyriformis end point predictions. ( 26 )
J Chem Inf Model - Prediction of individual compounds forming activity cliffs using emerging chemical patterns. ( 26 )
J Chem Inf Model - Using information from historical high-throughput screens to predict active compounds. ( 26 )
J Chem Inf Model - Computational repositioning and experimental validation of approved drugs for HIF-prolyl hydroxylase inhibition. ( 26 )
J Chem Inf Model - Scaffold-focused virtual screening: prospective application to the discovery of TTK inhibitors. ( 26 )
J Chem Inf Model - PyDPI: freely available python package for chemoinformatics, bioinformatics, and chemogenomics studies. ( 25 )