J Chem Inf Model - Comparative analysis of pharmacophore screening tools.

Tópicos

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

Resumo

The pharmacophore concept is of central importance in computer-aided drug design (CADD) mainly because of its successful application in medicinal chemistry and, in particular, high-throughput virtual screening (HTVS). The simplicity of the pharmacophore definition enables the complexity of molecular interactions between ligand and receptor to be reduced to a handful set of features. With many pharmacophore screening softwares available, it is of the utmost interest to explore the behavior of these tools when applied to different biological systems. In this work, we present a comparative analysis of eight pharmacophore screening algorithms (Catalyst, Unity, LigandScout, Phase, Pharao, MOE, Pharmer, and POT) for their use in typical HTVS campaigns against four different biological targets by using default settings. The results herein presented show how the performance of each pharmacophore screening tool might be specifically related to factors such as the characteristics of the binding pocket, the use of specific pharmacophore features, and the use of these techniques in specific steps/contexts of the drug discovery pipeline. Algorithms with rmsd-based scoring functions are able to predict more compound poses correctly as overlay-based scoring functions. However, the ratio of correctly predicted compound poses versus incorrectly predicted poses is better for overlay-based scoring functions that also ensure better performances in compound library enrichments. While the ensemble of these observations can be used to choose the most appropriate class of algorithm for specific virtual screening projects, we remarked that pharmacophore algorithms are often equally good, and in this respect, we also analyzed how pharmacophore algorithms can be combined together in order to increase the success of hit compound identification. This study provides a valuable benchmark set for further developments in the field of pharmacophore search algorithms, e.g., by using pose predictions and compound library enrichment criteria.

Resumo Limpo

pharmacophor concept central import computeraid drug design cadd main success applic medicin chemistri particular highthroughput virtual screen htvs simplic pharmacophor definit enabl complex molecular interact ligand receptor reduc hand set featur mani pharmacophor screen softwar avail utmost interest explor behavior tool appli differ biolog system work present compar analysi eight pharmacophor screen algorithm catalyst uniti ligandscout phase pharao moe pharmer pot use typic htvs campaign four differ biolog target use default set result herein present show perform pharmacophor screen tool might specif relat factor characterist bind pocket use specif pharmacophor featur use techniqu specif stepscontext drug discoveri pipelin algorithm rmsdbase score function abl predict compound pose correct overlaybas score function howev ratio correct predict compound pose versus incorrect predict pose better overlaybas score function also ensur better perform compound librari enrich ensembl observ can use choos appropri class algorithm specif virtual screen project remark pharmacophor algorithm often equal good respect also analyz pharmacophor algorithm can combin togeth order increas success hit compound identif studi provid valuabl benchmark set develop field pharmacophor search algorithm eg use pose predict compound librari enrich criteria

Resumos Similares

J Chem Inf Model - Selection of in silico drug screening results for G-protein-coupled receptors by using universal active probes. ( 0,81885412883694 )
J Chem Inf Model - Structure-based virtual screening approach for discovery of covalently bound ligands. ( 0,808125042181792 )
J Chem Inf Model - Virtual screening yields inhibitors of novel antifungal drug target, benzoate 4-monooxygenase. ( 0,793966842102086 )
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. ( 0,78961877931066 )
J Chem Inf Model - Validation of the AmpC ?-lactamase binding site and identification of inhibitors with novel scaffolds. ( 0,785960112731289 )
J Chem Inf Model - FINDSITE(comb): a threading/structure-based, proteomic-scale virtual ligand screening approach. ( 0,784662496029876 )
J Chem Inf Model - Discovery of a7-nicotinic receptor ligands by virtual screening of the chemical universe database GDB-13. ( 0,77898739537441 )
J Chem Inf Model - Knowledge-based libraries for predicting the geometric preferences of druglike molecules. ( 0,776208722951041 )
J Chem Inf Model - Identification of multitarget activity ridges in high-dimensional bioactivity spaces. ( 0,774550667410937 )
J Chem Inf Model - G-protein coupled receptors virtual screening using genetic algorithm focused chemical space. ( 0,773908979064599 )
J Chem Inf Model - An integrated virtual screening approach for VEGFR-2 inhibitors. ( 0,771946914086941 )
J Chem Inf Model - Discovery and design of tricyclic scaffolds as protein kinase CK2 (CK2) inhibitors through a combination of shape-based virtual screening and structure-based molecular modification. ( 0,770475834615561 )
J Chem Inf Model - Identification of 1,2,5-oxadiazoles as a new class of SENP2 inhibitors using structure based virtual screening. ( 0,770418632672946 )
J Chem Inf Model - SABRE: ligand/structure-based virtual screening approach using consensus molecular-shape pattern recognition. ( 0,76990070983973 )
J Chem Inf Model - Detecting drug promiscuity using Gaussian ensemble screening. ( 0,769323003915405 )
J Chem Inf Model - Virtual fragment screening: discovery of histamine H3 receptor ligands using ligand-based and protein-based molecular fingerprints. ( 0,76875817759917 )
J Chem Inf Model - Discovery of novel checkpoint kinase 1 inhibitors by virtual screening based on multiple crystal structures. ( 0,768203066083909 )
J Chem Inf Model - Identification of a novel inhibitor of dengue virus protease through use of a virtual screening drug discovery Web portal. ( 0,767860276987119 )
J Chem Inf Model - Visualization and virtual screening of the chemical universe database GDB-17. ( 0,765876782192598 )
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. ( 0,765301090860893 )
J Chem Inf Model - Identification of sumoylation activating enzyme 1 inhibitors by structure-based virtual screening. ( 0,764900506739678 )
J Chem Inf Model - Polypharmacology directed compound data mining: identification of promiscuous chemotypes with different activity profiles and comparison to approved drugs. ( 0,764528194252509 )
J Chem Inf Model - Identification of novel liver X receptor activators by structure-based modeling. ( 0,763831222266827 )
J Chem Inf Model - Multiple e-pharmacophore modeling, 3D-QSAR, and high-throughput virtual screening of hepatitis C virus NS5B polymerase inhibitors. ( 0,761419325998829 )
J Chem Inf Model - Exploration of 3D activity cliffs on the basis of compound binding modes and comparison of 2D and 3D cliffs. ( 0,760650769318842 )
J Chem Inf Model - Compound set enrichment: a novel approach to analysis of primary HTS data. ( 0,759136950523592 )
J Chem Inf Model - Chemical data visualization and analysis with incremental generative topographic mapping: big data challenge. ( 0,758086719225281 )
J Chem Inf Model - Atom pair 2D-fingerprints perceive 3D-molecular shape and pharmacophores for very fast virtual screening of ZINC and GDB-17. ( 0,758019085824948 )
J Chem Inf Model - Ligand and decoy sets for docking to G protein-coupled receptors. ( 0,757432134514648 )
J Chem Inf Model - Hsp90 inhibitors, part 2: combining ligand-based and structure-based approaches for virtual screening application. ( 0,757305351563421 )
J Chem Inf Model - Identification of novel S-adenosyl-L-homocysteine hydrolase inhibitors through homology-model-based virtual screening, synthesis, and biological evaluation. ( 0,755940870937719 )
J Chem Inf Model - Discovery of novel histamine H4 and serotonin transporter ligands using the topological feature tree descriptor. ( 0,755300952882181 )
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. ( 0,754447770916288 )
J Chem Inf Model - Novel method for pharmacophore analysis by examining the joint pharmacophore space. ( 0,75421733211489 )
J Chem Inf Model - Freely available conformer generation methods: how good are they? ( 0,753926158353253 )
J Chem Inf Model - Automatic tailoring and transplanting: a practical method that makes virtual screening more useful. ( 0,753890207648311 )
J Chem Inf Model - Combining horizontal and vertical substructure relationships in scaffold hierarchies for activity prediction. ( 0,753629595370468 )
J Chem Inf Model - Computational repositioning and experimental validation of approved drugs for HIF-prolyl hydroxylase inhibition. ( 0,753570204267626 )
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. ( 0,753021033549243 )
Comput Biol Chem - The optimization of running time for a maximum common substructure-based algorithm and its application in drug design. ( 0,752870012842539 )
Sci Data - Quantum chemistry structures and properties of 134 kilo molecules. ( 0,752815796906354 )
J Chem Inf Model - Identification of novel serotonin transporter compounds by virtual screening. ( 0,751989363709185 )
J Chem Inf Model - Molecular topology analysis of the differences between drugs, clinical candidate compounds, and bioactive molecules. ( 0,751332154157918 )
J Chem Inf Model - Best of both worlds: on the complementarity of ligand-based and structure-based virtual screening. ( 0,749525841396822 )
J Chem Inf Model - Visual characterization and diversity quantification of chemical libraries: 1. creation of delimited reference chemical subspaces. ( 0,749506256712093 )
J Chem Inf Model - Feasibility of using molecular docking-based virtual screening for searching dual target kinase inhibitors. ( 0,749372968483255 )
J Chem Inf Model - Rapid shape-based ligand alignment and virtual screening method based on atom/feature-pair similarities and volume overlap scoring. ( 0,748984284995317 )
J Chem Inf Model - Searching for recursively defined generic chemical patterns in nonenumerated fragment spaces. ( 0,748060508305076 )
J Chem Inf Model - Automated recycling of chemistry for virtual screening and library design. ( 0,747844286959226 )
J Chem Inf Model - Molecular modeling of potential anticancer agents from African medicinal plants. ( 0,746598764737376 )
J Chem Inf Model - Target-independent prediction of drug synergies using only drug lipophilicity. ( 0,746333166944578 )
J Chem Inf Model - Identification of novel androgen receptor antagonists using structure- and ligand-based methods. ( 0,744985673352435 )
J Chem Inf Model - A searchable map of PubChem. ( 0,744905395749111 )
J Chem Inf Model - Scaffold-focused virtual screening: prospective application to the discovery of TTK inhibitors. ( 0,743632475338046 )
J Chem Inf Model - Computer-aided structure-based design of multitarget leads for Alzheimer's disease. ( 0,742862310973198 )
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. ( 0,741991715704079 )
J Chem Inf Model - Complementarity between in silico and biophysical screening approaches in fragment-based lead discovery against the A(2A) adenosine receptor. ( 0,741701461695918 )
J Chem Inf Model - Novel mycosin protease MycP1 inhibitors identified by virtual screening and 4D fingerprints. ( 0,741106863898231 )
J Chem Inf Model - SimG: an alignment based method for evaluating the similarity of small molecules and binding sites. ( 0,74017371984982 )
J Chem Inf Model - Comparison of virtual high-throughput screening methods for the identification of phosphodiesterase-5 inhibitors. ( 0,739138441884672 )
J Chem Inf Model - Structure-based design and screen of novel inhibitors for class II 3-hydroxy-3-methylglutaryl coenzyme A reductase from Streptococcus pneumoniae. ( 0,739115393605742 )
J Chem Inf Model - Identification of novel malarial cysteine protease inhibitors using structure-based virtual screening of a focused cysteine protease inhibitor library. ( 0,738824905093783 )
J Chem Inf Model - TIN-a combinatorial compound collection of synthetically feasible multicomponent synthesis products. ( 0,734913178030266 )
J Chem Inf Model - Compound optimization through data set-dependent chemical transformations. ( 0,734739146598652 )
J Chem Inf Model - From activity cliffs to activity ridges: informative data structures for SAR analysis. ( 0,734410739421993 )
J Chem Inf Model - Bioturbo similarity searching: combining chemical and biological similarity to discover structurally diverse bioactive molecules. ( 0,733377648874014 )
J Chem Inf Model - Design of a three-dimensional multitarget activity landscape. ( 0,732062166906524 )
J Chem Inf Model - Identifying compound-target associations by combining bioactivity profile similarity search and public databases mining. ( 0,731435673233316 )
J Chem Inf Model - ColBioS-FlavRC: a collection of bioselective flavonoids and related compounds filtered from high-throughput screening outcomes. ( 0,731161258790008 )
J Chem Inf Model - Design of multitarget activity landscapes that capture hierarchical activity cliff distributions. ( 0,730459442985684 )
J Chem Inf Model - Biologically relevant chemical space navigator: from patent and structure-activity relationship analysis to library acquisition and design. ( 0,730313834205106 )
J Chem Inf Model - Effective screening strategy using ensembled pharmacophore models combined with cascade docking: application to p53-MDM2 interaction inhibitors. ( 0,729460869322114 )
Brief. Bioinformatics - State-of-the-art technology in modern computer-aided drug design. ( 0,729280769477599 )
J Chem Inf Model - Application of computer-aided drug repurposing in the search of new cruzipain inhibitors: discovery of amiodarone and bromocriptine inhibitory effects. ( 0,728710325176048 )
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. ( 0,728559355386597 )
J Chem Inf Model - Conditional probabilities of activity landscape features for individual compounds. ( 0,728517963723373 )
J Chem Inf Model - Capturing structure-activity relationships from chemogenomic spaces. ( 0,728206618945866 )
J Chem Inf Model - Identification of a new class of FtsZ inhibitors by structure-based design and in vitro screening. ( 0,727809249546149 )
J Chem Inf Model - Identification of novel potential antibiotics against Staphylococcus using structure-based drug screening targeting dihydrofolate reductase. ( 0,727499571730045 )
J Chem Inf Model - Enrichment of chemical libraries docked to protein conformational ensembles and application to aldehyde dehydrogenase 2. ( 0,727473874221372 )
J Chem Inf Model - In silico enzymatic synthesis of a 400,000 compound biochemical database for nontargeted metabolomics. ( 0,726819879770074 )
J Chem Inf Model - QSAR classification model for antibacterial compounds and its use in virtual screening. ( 0,724992889464069 )
J Chem Inf Model - EMBM - a new enzyme mechanism-based method for rational design of chemical sites of covalent inhibitors. ( 0,724620979361612 )
J Chem Inf Model - Discovery of inhibitors of Schistosoma mansoni HDAC8 by combining homology modeling, virtual screening, and in vitro validation. ( 0,724555164601868 )
J Chem Inf Model - Identification of non-macrocyclic small molecule inhibitors against the NS3/4A serine protease of hepatitis C virus through in silico screening. ( 0,724310404475193 )
J Chem Inf Model - Development of a minimal kinase ensemble receptor (MKER) for surrogate AutoShim. ( 0,724261509612245 )
J Chem Inf Model - Optimization of molecular representativeness. ( 0,724252992675916 )
J Chem Inf Model - Discovery of new selective human aldose reductase inhibitors through virtual screening multiple binding pocket conformations. ( 0,72375027594956 )
J Chem Inf Model - Natural product-like virtual libraries: recursive atom-based enumeration. ( 0,723421236731901 )
J Chem Inf Model - Introduction of target cliffs as a concept to identify and describe complex molecular selectivity patterns. ( 0,722726769490822 )
J Chem Inf Model - 3D flexible alignment using 2D maximum common substructure: dependence of prediction accuracy on target-reference chemical similarity. ( 0,722554211555438 )
J Chem Inf Model - Mechanism-based discovery of novel substrates of haloalkane dehalogenases using in silico screening. ( 0,722474818284765 )
J Chem Inf Model - Discovery of novel acetohydroxyacid synthase inhibitors as active agents against Mycobacterium tuberculosis by virtual screening and bioassay. ( 0,722377932998398 )
J Chem Inf Model - Chemoisosterism in the proteome. ( 0,722143840978595 )
J Chem Inf Model - Neighborhood-based prediction of novel active compounds from SAR matrices. ( 0,721531831248087 )
J Chem Inf Model - Mining the ChEMBL database: an efficient chemoinformatics workflow for assembling an ion channel-focused screening library. ( 0,721156197189614 )
J Chem Inf Model - Similarity boosted quantitative structure-activity relationship--a systematic study of enhancing structural descriptors by molecular similarity. ( 0,720336960949909 )
J Chem Inf Model - PyDPI: freely available python package for chemoinformatics, bioinformatics, and chemogenomics studies. ( 0,719514869081255 )
J Chem Inf Model - Increasing the coverage of medicinal chemistry-relevant space in commercial fragments screening. ( 0,719427793839386 )
J Chem Inf Model - A multivariate chemical similarity approach to search for drugs of potential environmental concern. ( 0,718668672943474 )