J Chem Inf Model - Improvement of virtual screening results by docking data feature analysis.

Tópicos

{ bind(1733) structur(1185) ligand(1036) }
{ perform(1367) use(1326) method(1137) }
{ first(2504) two(1366) second(1323) }
{ compound(1573) activ(1297) structur(1058) }
{ network(2748) neural(1063) input(814) }
{ method(1219) similar(1157) match(930) }
{ perform(999) metric(946) measur(919) }
{ system(1976) rule(880) can(841) }
{ take(945) account(800) differ(722) }
{ featur(1941) imag(1645) propos(1176) }
{ featur(3375) classif(2383) classifi(1994) }
{ studi(2440) review(1878) systemat(933) }
{ data(1714) softwar(1251) tool(1186) }
{ data(3008) multipl(1320) sourc(1022) }
{ imag(1947) propos(1133) code(1026) }
{ extract(1171) text(1153) clinic(932) }
{ method(984) reconstruct(947) comput(926) }
{ studi(1410) differ(1259) use(1210) }
{ health(3367) inform(1360) care(1135) }
{ cost(1906) reduc(1198) effect(832) }
{ sampl(1606) size(1419) use(1276) }
{ intervent(3218) particip(2042) group(1664) }
{ can(981) present(881) function(850) }
{ high(1669) rate(1365) level(1280) }
{ imag(1057) registr(996) error(939) }
{ patient(2315) diseas(1263) diabet(1191) }
{ motion(1329) object(1292) video(1091) }
{ model(3480) simul(1196) paramet(876) }
{ data(2317) use(1299) case(1017) }
{ patient(1821) servic(1111) care(1106) }
{ structur(1116) can(940) graph(676) }
{ detect(2391) sensit(1101) algorithm(908) }
{ model(3404) distribut(989) bayesian(671) }
{ can(774) often(719) complex(702) }
{ data(1737) use(1416) pattern(1282) }
{ inform(2794) health(2639) internet(1427) }
{ measur(2081) correl(1212) valu(896) }
{ sequenc(1873) structur(1644) protein(1328) }
{ imag(2830) propos(1344) filter(1198) }
{ imag(2675) segment(2577) method(1081) }
{ assess(1506) score(1403) qualiti(1306) }
{ treatment(1704) effect(941) patient(846) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ framework(1458) process(801) describ(734) }
{ problem(2511) optim(1539) algorithm(950) }
{ error(1145) method(1030) estim(1020) }
{ chang(1828) time(1643) increas(1301) }
{ learn(2355) train(1041) set(1003) }
{ concept(1167) ontolog(924) domain(897) }
{ clinic(1479) use(1117) guidelin(835) }
{ algorithm(1844) comput(1787) effici(935) }
{ method(1557) propos(1049) approach(1037) }
{ design(1359) user(1324) use(1319) }
{ control(1307) perform(991) simul(935) }
{ model(2220) cell(1177) simul(1124) }
{ care(1570) inform(1187) nurs(1089) }
{ general(901) number(790) one(736) }
{ search(2224) databas(1162) retriev(909) }
{ case(1353) use(1143) diagnosi(1136) }
{ howev(809) still(633) remain(590) }
{ data(3963) clinic(1234) research(1004) }
{ risk(3053) factor(974) diseas(938) }
{ research(1085) discuss(1038) issu(1018) }
{ system(1050) medic(1026) inform(1018) }
{ import(1318) role(1303) understand(862) }
{ model(2341) predict(2261) use(1141) }
{ visual(1396) interact(850) tool(830) }
{ studi(1119) effect(1106) posit(819) }
{ blood(1257) pressur(1144) flow(957) }
{ spatial(1525) area(1432) region(1030) }
{ record(1888) medic(1808) patient(1693) }
{ monitor(1329) mobil(1314) devic(1160) }
{ ehr(2073) health(1662) electron(1139) }
{ state(1844) use(1261) util(961) }
{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ model(2656) set(1616) predict(1553) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ group(2977) signific(1463) compar(1072) }
{ gene(2352) biolog(1181) express(1162) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ use(2086) technolog(871) perceiv(783) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ cancer(2502) breast(956) screen(824) }
{ use(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ result(1111) use(1088) new(759) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ process(1125) use(805) approach(778) }
{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }
{ method(2212) result(1239) propos(1039) }

Resumo

In this study, we propose a novel approach to evaluate virtual screening (VS) experiments based on the analysis of docking output data. This approach, which we refer to as docking data feature analysis (DDFA), consists of two steps. First, a set of features derived from the docking output data is computed and assigned to each molecule in the virtually screened library. Second, an artificial neural network (ANN) analyzes the molecule's docking features and estimates its activity. Given the simple architecture of the ANN, DDFA can be easily adapted to deal with information from several docking programs simultaneously. We tested our approach on the Directory of Useful Decoys (DUD), a well-established and highly accepted VS benchmark. Outstanding results were obtained by DDFA not only in comparison with the conventional rankings of the docking programs used in this work but also with respect to other methods found in the literature. Our approach performs with similar good results as the best available methods, which, however, also require substantially more computing time, economic resources, and/or expert intervention. Taken together, DDFA represents an automatic and highly attractive methodology for VS.

Resumo Limpo

studi propos novel approach evalu virtual screen vs experi base analysi dock output data approach refer dock data featur analysi ddfa consist two step first set featur deriv dock output data comput assign molecul virtual screen librari second artifici neural network ann analyz molecul dock featur estim activ given simpl architectur ann ddfa can easili adapt deal inform sever dock program simultan test approach directori use decoy dud wellestablish high accept vs benchmark outstand result obtain ddfa comparison convent rank dock program use work also respect method found literatur approach perform similar good result best avail method howev also requir substanti comput time econom resourc andor expert intervent taken togeth ddfa repres automat high attract methodolog vs

Resumos Similares

J Chem Inf Model - Inhibitor design strategy based on an enzyme structural flexibility: a case of bacterial MurD ligase. ( 0,679957476988867 )
J Chem Inf Model - Ligand-based target prediction with signature fingerprints. ( 0,669844055587009 )
J Chem Inf Model - Structure-based fragment hopping for lead optimization using predocked fragment database. ( 0,666229416357553 )
J Chem Inf Model - Improving VEGFR-2 docking-based screening by pharmacophore postfiltering and similarity search postprocessing. ( 0,653040327904596 )
J Chem Inf Model - ALiBERO: evolving a team of complementary pocket conformations rather than a single leader. ( 0,652870910854838 )
J Chem Inf Model - ReverseScreen3D: a structure-based ligand matching method to identify protein targets. ( 0,635850765192996 )
J Chem Inf Model - Multiple structures for virtual ligand screening: defining binding site properties-based criteria to optimize the selection of the query. ( 0,634726975194079 )
J Chem Inf Model - Multi-objective evolutionary design of adenosine receptor ligands. ( 0,627133532012032 )
J Chem Inf Model - Computer-aided structure-based design of multitarget leads for Alzheimer's disease. ( 0,623978469501763 )
J Chem Inf Model - Discovery of new inhibitors of Mycobacterium tuberculosis InhA enzyme using virtual screening and a 3D-pharmacophore-based approach. ( 0,623866438877072 )
J Chem Inf Model - Virtual screening of PRK1 inhibitors: ensemble docking, rescoring using binding free energy calculation and QSAR model development. ( 0,621293703585158 )
J Chem Inf Model - Virtual screening for ligands of the insect molting hormone receptor. ( 0,620644499477366 )
J Chem Inf Model - Discovery of novel checkpoint kinase 1 inhibitors by virtual screening based on multiple crystal structures. ( 0,61874650406612 )
J Chem Inf Model - SABRE: ligand/structure-based virtual screening approach using consensus molecular-shape pattern recognition. ( 0,617657873504573 )
J Chem Inf Model - Fast protein binding site comparison via an index-based screening technology. ( 0,61564726120233 )
J Chem Inf Model - Molecular topology applied to the discovery of 1-benzyl-2-(3-fluorophenyl)-4-hydroxy-3-(3-phenylpropanoyl)-2H-pyrrole-5-one as a non-ligand-binding-pocket antiandrogen. ( 0,614660659928981 )
J Chem Inf Model - Use of experimental design to optimize docking performance: the case of LiGenDock, the docking module of LiGen, a new de novo design program. ( 0,614317028164836 )
J Chem Inf Model - Effective virtual screening strategy toward covalent ligands: identification of novel NEDD8-activating enzyme inhibitors. ( 0,612236860367977 )
J Chem Inf Model - Rapid shape-based ligand alignment and virtual screening method based on atom/feature-pair similarities and volume overlap scoring. ( 0,603738182033403 )
J Chem Inf Model - In silico fragment-based drug discovery: setup and validation of a fragment-to-lead computational protocol using S4MPLE. ( 0,602804444579996 )
J Chem Inf Model - Selecting an optimal number of binding site waters to improve virtual screening enrichments against the adenosine A2A receptor. ( 0,602613552341527 )
J Chem Inf Model - Large-scale comparison of four binding site detection algorithms. ( 0,601950904438726 )
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. ( 0,601209499748155 )
J Chem Inf Model - Exploration of 3D activity cliffs on the basis of compound binding modes and comparison of 2D and 3D cliffs. ( 0,600003759139408 )
J Chem Inf Model - Development and evaluation of an integrated virtual screening strategy by combining molecular docking and pharmacophore searching based on multiple protein structures. ( 0,599724537437954 )
J Chem Inf Model - Improved machine learning models for predicting selective compounds. ( 0,598326570572884 )
J Chem Inf Model - Ligand-optimized homology models of D1 and D2 dopamine receptors: application for virtual screening. ( 0,595773079721315 )
J Chem Inf Model - RS-predictor: a new tool for predicting sites of cytochrome P450-mediated metabolism applied to CYP 3A4. ( 0,593347017082038 )
J Chem Inf Model - Validation of the AmpC ?-lactamase binding site and identification of inhibitors with novel scaffolds. ( 0,593197278911565 )
Int J Neural Syst - Multi-strategy coevolving aging particle optimization. ( 0,593122096596679 )
J Chem Inf Model - Virtual drug screen schema based on multiview similarity integration and ranking aggregation. ( 0,59145549851088 )
J Chem Inf Model - Assessing an ensemble docking-based virtual screening strategy for kinase targets by considering protein flexibility. ( 0,589676321543228 )
J Chem Inf Model - Discovery of novel Pim-1 kinase inhibitors by a hierarchical multistage virtual screening approach based on SVM model, pharmacophore, and molecular docking. ( 0,588650106198968 )
J Chem Inf Model - PLS-DA - Docking Optimized Combined Energetic Terms (PLSDA-DOCET) protocol: a brief evaluation. ( 0,586354967104834 )
J Chem Inf Model - Identification of novel amino acid derived CCK-2R antagonists as potential antiulcer agent: homology modeling, design, synthesis, and pharmacology. ( 0,585419101901927 )
J Chem Inf Model - Predicting binding affinity of CSAR ligands using both structure-based and ligand-based approaches. ( 0,585077233586709 )
J Chem Inf Model - Ligand and decoy sets for docking to G protein-coupled receptors. ( 0,58482394583246 )
Comput Biol Chem - Potential drug-like inhibitors of Group 1 influenza neuraminidase identified through computer-aided drug design. ( 0,583517446900638 )
J Chem Inf Model - Protein pharmacophore selection using hydration-site analysis. ( 0,582150832548348 )
J Chem Inf Model - Estimation of the intramolecular O-H???O-C hydrogen bond energy via the molecular tailoring approach. Part I: aliphatic structures. ( 0,581465924103312 )
J Chem Inf Model - An automated docking protocol for hERG channel blockers. ( 0,577416489093406 )
J Chem Inf Model - Potential and limitations of ensemble docking. ( 0,57661959902875 )
J Chem Inf Model - Feasibility of using molecular docking-based virtual screening for searching dual target kinase inhibitors. ( 0,57648990853077 )
J Chem Inf Model - Numerical errors and chaotic behavior in docking simulations. ( 0,576208131040061 )
J Chem Inf Model - Hot spot analysis for driving the development of hits into leads in fragment-based drug discovery. ( 0,574040155114326 )
J Chem Inf Model - Comparing neural-network scoring functions and the state of the art: applications to common library screening. ( 0,573990117883042 )
J Chem Inf Model - Multiple e-pharmacophore modeling, 3D-QSAR, and high-throughput virtual screening of hepatitis C virus NS5B polymerase inhibitors. ( 0,573595128092757 )
J Chem Inf Model - CSBB-ConeExclusion, adapting structure based solution virtual screening to libraries on solid support. ( 0,573479790863248 )
J Chem Inf Model - Characterization of small molecule binding. I. Accurate identification of strong inhibitors in virtual screening. ( 0,570713838726805 )
J Chem Inf Model - Hot spots and transient pockets: predicting the determinants of small-molecule binding to a protein-protein interface. ( 0,569571638148085 )
J Chem Inf Model - CRDOCK: an ultrafast multipurpose protein-ligand docking tool. ( 0,568010883378447 )
J Chem Inf Model - Combining ligand- and structure-based approaches for the discovery of new inhibitors of the EPHA2-ephrin-A1 interaction. ( 0,566756590240867 )
J Chem Inf Model - Importance of the pharmacological profile of the bound ligand in enrichment on nuclear receptors: toward the use of experimentally validated decoy ligands. ( 0,566490409822901 )
J Chem Inf Model - Discovery of novel tubulin inhibitors via structure-based hierarchical virtual screening. ( 0,566125975148421 )
J Chem Inf Model - Large-scale mining for similar protein binding pockets: with RAPMAD retrieval on the fly becomes real. ( 0,566078280661681 )
J Chem Inf Model - SimG: an alignment based method for evaluating the similarity of small molecules and binding sites. ( 0,565861563646435 )
J Chem Inf Model - Comparative assessment of scoring functions on an updated benchmark: 1. Compilation of the test set. ( 0,565608979939504 )
J. Comput. Biol. - Protein-specific scoring method for ligand discovery. ( 0,563459882963518 )
Artif Intell Med - Quantitative prediction of MHC-II binding affinity using particle swarm optimization. ( 0,563260336033243 )
J Chem Inf Model - Constraint Network Analysis (CNA): a Python software package for efficiently linking biomacromolecular structure, flexibility, (thermo-)stability, and function. ( 0,5611629731168 )
Comput Math Methods Med - patGPCR: a multitemplate approach for improving 3D structure prediction of transmembrane helices of G-protein-coupled receptors. ( 0,561149390911311 )
J Chem Inf Model - Structure-based virtual screening approach for discovery of covalently bound ligands. ( 0,559968065022757 )
Comput Biol Chem - Homology modeling, binding site identification and docking in flavone hydroxylase CYP105P2 in Streptomyces peucetius ATCC 27952. ( 0,559516041823826 )
J Chem Inf Model - Conformer generation with OMEGA: learning from the data set and the analysis of failures. ( 0,559336991325894 )
J Chem Inf Model - Structure-based virtual screening of MT2 melatonin receptor: influence of template choice and structural refinement. ( 0,558730090535379 )
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. ( 0,558072383448557 )
J Chem Inf Model - Modeling flexible pharmacophores with distance geometry, scoring, and bound stretching. ( 0,557349122298059 )
J Chem Inf Model - Molecular docking and pharmacophore filtering in the discovery of dual-inhibitors for human leukotriene A4 hydrolase and leukotriene C4 synthase. ( 0,55718689649113 )
J Chem Inf Model - Knowledge-based scoring functions in drug design: 2. Can the knowledge base be enriched? ( 0,556073942221922 )
J Chem Inf Model - Nonlinear scoring functions for similarity-based ligand docking and binding affinity prediction. ( 0,554769547606093 )
J Chem Inf Model - Virtual target screening: validation using kinase inhibitors. ( 0,554374865094821 )
J Chem Inf Model - How to improve docking accuracy of AutoDock4.2: a case study using different electrostatic potentials. ( 0,554003994629247 )
J Chem Inf Model - Experimental-like affinity constants and enantioselectivity estimates from flexible docking. ( 0,55379466327346 )
J Chem Inf Model - Fragment-based drug discovery using a multidomain, parallel MD-MM/PBSA screening protocol. ( 0,553676806226012 )
J Chem Inf Model - In silico comparison of antimycobacterial natural products with known antituberculosis drugs. ( 0,553616866155317 )
J Chem Inf Model - Comparison of virtual high-throughput screening methods for the identification of phosphodiesterase-5 inhibitors. ( 0,55207950820513 )
J Chem Inf Model - Detailed computational study of the active site of the hepatitis C viral RNA polymerase to aid novel drug design. ( 0,552005898087446 )
J Chem Inf Model - Identification of ligand templates using local structure alignment for structure-based drug design. ( 0,551976221528841 )
J Chem Inf Model - Probing the dynamic nature of water molecules and their influences on ligand binding in a model binding site. ( 0,551221327188416 )
J Chem Inf Model - Sampling multiple scoring functions can improve protein loop structure prediction accuracy. ( 0,549952050077065 )
J Chem Inf Model - Molecular binding sites are located near the interface of intrinsic dynamics domains (IDDs). ( 0,549711162096906 )
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. ( 0,548948612494901 )
J Chem Inf Model - Bridging molecular docking to membrane molecular dynamics to investigate GPCR-ligand recognition: the human A2A adenosine receptor as a key study. ( 0,548555578634972 )
J Chem Inf Model - Evaluation and application of MD-PB/SA in structure-based hierarchical virtual screening. ( 0,547852027441381 )
J Chem Inf Model - A molecular mechanics approach to modeling protein-ligand interactions: relative binding affinities in congeneric series. ( 0,547542605941817 )
J Chem Inf Model - Virtual fragment screening: discovery of histamine H3 receptor ligands using ligand-based and protein-based molecular fingerprints. ( 0,546788604286682 )
Comput Biol Chem - In silico study of anti-carcinogenic lysyl oxidase-like 2 inhibitors. ( 0,545787993954348 )
J Chem Inf Model - Structure-based virtual screening of the nociceptin receptor: hybrid docking and shape-based approaches for improved hit identification. ( 0,545137130284268 )
J Chem Inf Model - Integrating ligand-based and protein-centric virtual screening of kinase inhibitors using ensembles of multiple protein kinase genes and conformations. ( 0,545109503918266 )
J Chem Inf Model - Extended template-based modeling and evaluation method using consensus of binding mode of GPCRs for virtual screening. ( 0,544984673346391 )
J Chem Inf Model - Docking covalent inhibitors: a parameter free approach to pose prediction and scoring. ( 0,544885598363904 )
J Chem Inf Model - Beware of machine learning-based scoring functions-on the danger of developing black boxes. ( 0,544841047766227 )
J Chem Inf Model - Evaluation and optimization of virtual screening workflows with DEKOIS 2.0--a public library of challenging docking benchmark sets. ( 0,544680455293676 )
J Chem Inf Model - Definition of drug-likeness for compound affinity. ( 0,544235603805868 )
J Chem Inf Model - Investigation of the differences in activity between hydroxycycloalkyl N1 substituted pyrazole derivatives as inhibitors of B-Raf kinase by using docking, molecular dynamics, QM/MM, and fragment-based de novo design: study of binding mode of diastereomer compounds. ( 0,54371421409631 )
J Chem Inf Model - Targeting dynamic pockets of HIV-1 protease by structure-based computational screening for allosteric inhibitors. ( 0,543161477135393 )
J Chem Inf Model - Which three-dimensional characteristics make efficient inhibitors of protein-protein interactions? ( 0,542645385050005 )
J Chem Inf Model - Importance of receptor flexibility in binding of cyclam compounds to the chemokine receptor CXCR4. ( 0,542137476548394 )
J Chem Inf Model - Freely available conformer generation methods: how good are they? ( 0,541889202535823 )
J Chem Inf Model - Identification of sumoylation inhibitors targeting a predicted pocket in Ubc9. ( 0,54141069542958 )