Brief. Bioinformatics - State-of-the-art technology in modern computer-aided drug design.

Tópicos

{ compound(1573) activ(1297) structur(1058) }
{ bind(1733) structur(1185) ligand(1036) }
{ process(1125) use(805) approach(778) }
{ drug(1928) target(777) effect(648) }
{ design(1359) user(1324) use(1319) }
{ medic(1828) order(1363) alert(1069) }
{ cost(1906) reduc(1198) effect(832) }
{ can(774) often(719) complex(702) }
{ imag(2830) propos(1344) filter(1198) }
{ data(1714) softwar(1251) tool(1186) }
{ control(1307) perform(991) simul(935) }
{ research(1085) discuss(1038) issu(1018) }
{ use(2086) technolog(871) perceiv(783) }
{ implement(1333) system(1263) develop(1122) }
{ imag(1057) registr(996) error(939) }
{ concept(1167) ontolog(924) domain(897) }
{ search(2224) databas(1162) retriev(909) }
{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ data(2317) use(1299) case(1017) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ cancer(2502) breast(956) screen(824) }
{ method(1969) cluster(1462) data(1082) }
{ model(3404) distribut(989) bayesian(671) }
{ imag(1947) propos(1133) code(1026) }
{ data(1737) use(1416) pattern(1282) }
{ inform(2794) health(2639) internet(1427) }
{ system(1976) rule(880) can(841) }
{ measur(2081) correl(1212) valu(896) }
{ sequenc(1873) structur(1644) protein(1328) }
{ method(1219) similar(1157) match(930) }
{ featur(3375) classif(2383) classifi(1994) }
{ network(2748) neural(1063) input(814) }
{ imag(2675) segment(2577) method(1081) }
{ patient(2315) diseas(1263) diabet(1191) }
{ take(945) account(800) differ(722) }
{ studi(2440) review(1878) systemat(933) }
{ motion(1329) object(1292) video(1091) }
{ 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) }
{ clinic(1479) use(1117) guidelin(835) }
{ algorithm(1844) comput(1787) effici(935) }
{ 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) }
{ method(984) reconstruct(947) comput(926) }
{ 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) }
{ perform(999) metric(946) measur(919) }
{ system(1050) medic(1026) inform(1018) }
{ import(1318) role(1303) understand(862) }
{ model(2341) predict(2261) use(1141) }
{ visual(1396) interact(850) tool(830) }
{ perform(1367) use(1326) method(1137) }
{ studi(1119) effect(1106) posit(819) }
{ 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) }
{ monitor(1329) mobil(1314) devic(1160) }
{ ehr(2073) health(1662) electron(1139) }
{ state(1844) use(1261) util(961) }
{ model(2656) set(1616) predict(1553) }
{ age(1611) year(1155) adult(843) }
{ signal(2180) analysi(812) frequenc(800) }
{ group(2977) signific(1463) compar(1072) }
{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ can(981) present(881) function(850) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
{ use(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
{ result(1111) use(1088) new(759) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ activ(1452) weight(1219) physic(1104) }
{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

The quest for small drug-like compounds that selectively inhibit the function of biological targets has always been a major focus in the pharmaceutical industry and in academia as well. High-throughput screening of compound libraries requires time, cost and resources. Therefore, the use of alternative methods is necessary for facilitating lead discovery. Computational techniques that dock small molecules into macromolecular targets and predict the affinity and activity of the small molecule are widely used in drug design and discovery, and have become an integral part of the industrial and academic research. In this review, we present an overview of some state-of-the-art technologies in modern drug design that have been developed for expediting the search for novel drug candidates.

Resumo Limpo

quest small druglik compound select inhibit function biolog target alway major focus pharmaceut industri academia well highthroughput screen compound librari requir time cost resourc therefor use altern method necessari facilit lead discoveri comput techniqu dock small molecul macromolecular target predict affin activ small molecul wide use drug design discoveri becom integr part industri academ research review present overview stateoftheart technolog modern drug design develop expedit search novel drug candid

Resumos Similares

J Chem Inf Model - Molecular modeling of potential anticancer agents from African medicinal plants. ( 0,883118869643387 )
J Chem Inf Model - FINDSITE(comb): a threading/structure-based, proteomic-scale virtual ligand screening approach. ( 0,870150083830265 )
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. ( 0,856513591451186 )
J Chem Inf Model - Virtual fragment screening: discovery of histamine H3 receptor ligands using ligand-based and protein-based molecular fingerprints. ( 0,846862028423872 )
J Chem Inf Model - Identification of novel liver X receptor activators by structure-based modeling. ( 0,846662280821966 )
J Chem Inf Model - Structure-based virtual screening approach for discovery of covalently bound ligands. ( 0,842717107692794 )
J Chem Inf Model - Effective screening strategy using ensembled pharmacophore models combined with cascade docking: application to p53-MDM2 interaction inhibitors. ( 0,840142760130529 )
J Chem Inf Model - Discovery of a7-nicotinic receptor ligands by virtual screening of the chemical universe database GDB-13. ( 0,835248378987207 )
J Chem Inf Model - Identification of novel S-adenosyl-L-homocysteine hydrolase inhibitors through homology-model-based virtual screening, synthesis, and biological evaluation. ( 0,834678981061959 )
J Chem Inf Model - Identification of sumoylation activating enzyme 1 inhibitors by structure-based virtual screening. ( 0,83347799518511 )
J Chem Inf Model - Complementarity between in silico and biophysical screening approaches in fragment-based lead discovery against the A(2A) adenosine receptor. ( 0,832547397183061 )
J Chem Inf Model - Identification of a novel inhibitor of dengue virus protease through use of a virtual screening drug discovery Web portal. ( 0,830289744579124 )
J Chem Inf Model - Virtual screening yields inhibitors of novel antifungal drug target, benzoate 4-monooxygenase. ( 0,828866735008868 )
J Chem Inf Model - Polypharmacology directed compound data mining: identification of promiscuous chemotypes with different activity profiles and comparison to approved drugs. ( 0,824058710539643 )
J Chem Inf Model - Identification of novel malarial cysteine protease inhibitors using structure-based virtual screening of a focused cysteine protease inhibitor library. ( 0,822838302859869 )
J Chem Inf Model - In silico enzymatic synthesis of a 400,000 compound biochemical database for nontargeted metabolomics. ( 0,821601880137126 )
J Chem Inf Model - G-protein coupled receptors virtual screening using genetic algorithm focused chemical space. ( 0,815517585289366 )
J Chem Inf Model - Probing the bioactivity-relevant chemical space of robust reactions and common molecular building blocks. ( 0,815455377036176 )
J Chem Inf Model - Best of both worlds: on the complementarity of ligand-based and structure-based virtual screening. ( 0,814681693444954 )
Curr Comput Aided Drug Des - Development of Chemical Compound Libraries for In Silico Drug Screening. ( 0,811966367265065 )
J Chem Inf Model - Selection of in silico drug screening results for G-protein-coupled receptors by using universal active probes. ( 0,809531247924299 )
J Chem Inf Model - Identification of novel serotonin transporter compounds by virtual screening. ( 0,809490317157696 )
J Chem Inf Model - Multiple e-pharmacophore modeling, 3D-QSAR, and high-throughput virtual screening of hepatitis C virus NS5B polymerase inhibitors. ( 0,809093852968043 )
J Chem Inf Model - Searching for recursively defined generic chemical patterns in nonenumerated fragment spaces. ( 0,808610465349389 )
J Chem Inf Model - Novel mycosin protease MycP1 inhibitors identified by virtual screening and 4D fingerprints. ( 0,808362616337265 )
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. ( 0,808030048222722 )
Sci Data - Quantum chemistry structures and properties of 134 kilo molecules. ( 0,807534508469175 )
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. ( 0,806654626621901 )
J Chem Inf Model - Identification of novel potential antibiotics against Staphylococcus using structure-based drug screening targeting dihydrofolate reductase. ( 0,806376080895394 )
J Chem Inf Model - Discovery of inhibitors of Schistosoma mansoni HDAC8 by combining homology modeling, virtual screening, and in vitro validation. ( 0,803609458401628 )
J Chem Inf Model - SABRE: ligand/structure-based virtual screening approach using consensus molecular-shape pattern recognition. ( 0,800835146668916 )
J Chem Inf Model - Enrichment of chemical libraries docked to protein conformational ensembles and application to aldehyde dehydrogenase 2. ( 0,800359587888225 )
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. ( 0,799677634166301 )
J Chem Inf Model - Rationalizing the role of SAR tolerance for ligand-based virtual screening. ( 0,7986781462613 )
J Chem Inf Model - TIN-a combinatorial compound collection of synthetically feasible multicomponent synthesis products. ( 0,798544572927917 )
J Chem Inf Model - Identification of multitarget activity ridges in high-dimensional bioactivity spaces. ( 0,798004142368435 )
J Chem Inf Model - Novel strategy for three-dimensional fragment-based lead discovery. ( 0,797596164088133 )
J Chem Inf Model - Visualization and virtual screening of the chemical universe database GDB-17. ( 0,797472523577186 )
J Chem Inf Model - Validation of the AmpC ?-lactamase binding site and identification of inhibitors with novel scaffolds. ( 0,797361680424654 )
J Chem Inf Model - Identification of 1,2,5-oxadiazoles as a new class of SENP2 inhibitors using structure based virtual screening. ( 0,796674305841781 )
J Chem Inf Model - Freely available conformer generation methods: how good are they? ( 0,795163541702089 )
J Chem Inf Model - Visual characterization and diversity quantification of chemical libraries: 1. creation of delimited reference chemical subspaces. ( 0,795054927587811 )
J Chem Inf Model - Chemoisosterism in the proteome. ( 0,793369063763025 )
J Chem Inf Model - Novel method for pharmacophore analysis by examining the joint pharmacophore space. ( 0,79098099891473 )
J Chem Inf Model - Discovery of novel checkpoint kinase 1 inhibitors by virtual screening based on multiple crystal structures. ( 0,789988959106686 )
J Chem Inf Model - Identification of compounds with potential antibacterial activity against Mycobacterium through structure-based drug screening. ( 0,788869600000558 )
J Chem Inf Model - Structure-based fragment hopping for lead optimization using predocked fragment database. ( 0,78882222444204 )
J Chem Inf Model - Compound optimization through data set-dependent chemical transformations. ( 0,787994990386086 )
J Chem Inf Model - Target-independent prediction of drug synergies using only drug lipophilicity. ( 0,787113234388438 )
J Chem Inf Model - Development of a minimal kinase ensemble receptor (MKER) for surrogate AutoShim. ( 0,786938203697056 )
J Chem Inf Model - Discovery of new selective human aldose reductase inhibitors through virtual screening multiple binding pocket conformations. ( 0,785840632210071 )
J Chem Inf Model - Feasibility of using molecular docking-based virtual screening for searching dual target kinase inhibitors. ( 0,783212187139386 )
J Chem Inf Model - Searching for substructures in fragment spaces. ( 0,782577997139725 )
J Chem Inf Model - Discovery of novel histamine H4 and serotonin transporter ligands using the topological feature tree descriptor. ( 0,781788092830663 )
J Chem Inf Model - Identification of inhibitors against p90 ribosomal S6 kinase 2 (RSK2) through structure-based virtual screening with the inhibitor-constrained refined homology model. ( 0,781300292161458 )
J Chem Inf Model - Structure based design, synthesis, pharmacophore modeling, virtual screening, and molecular docking studies for identification of novel cyclophilin D inhibitors. ( 0,78122152427197 )
J Chem Inf Model - How to improve docking accuracy of AutoDock4.2: a case study using different electrostatic potentials. ( 0,78116278374495 )
J Chem Inf Model - Identifying compound-target associations by combining bioactivity profile similarity search and public databases mining. ( 0,780881876990021 )
J Chem Inf Model - Automatic tailoring and transplanting: a practical method that makes virtual screening more useful. ( 0,779709107694043 )
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. ( 0,779489346356565 )
J Chem Inf Model - Ligand- and structure-based virtual screening for clathrodin-derived human voltage-gated sodium channel modulators. ( 0,77829969310666 )
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. ( 0,778191032891935 )
J Chem Inf Model - Natural product-like virtual libraries: recursive atom-based enumeration. ( 0,777902431641512 )
J Chem Inf Model - Combining horizontal and vertical substructure relationships in scaffold hierarchies for activity prediction. ( 0,775314755226941 )
J Chem Inf Model - Discovery of novel tubulin inhibitors via structure-based hierarchical virtual screening. ( 0,772943116591128 )
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. ( 0,771393350833139 )
J Chem Inf Model - Identification of sumoylation inhibitors targeting a predicted pocket in Ubc9. ( 0,770725525090489 )
J Chem Inf Model - Application of computer-aided drug repurposing in the search of new cruzipain inhibitors: discovery of amiodarone and bromocriptine inhibitory effects. ( 0,770386770126938 )
J Chem Inf Model - Capturing structure-activity relationships from chemogenomic spaces. ( 0,770206047010949 )
J Chem Inf Model - Prediction of synthetic accessibility based on commercially available compound databases. ( 0,77016560818034 )
J Chem Inf Model - Automated recycling of chemistry for virtual screening and library design. ( 0,769753758558623 )
J Chem Inf Model - Novel insights of structure-based modeling for RNA-targeted drug discovery. ( 0,76967506606652 )
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. ( 0,768609925329509 )
J Chem Inf Model - Identification of a new class of FtsZ inhibitors by structure-based design and in vitro screening. ( 0,768504687715613 )
J Chem Inf Model - Conditional probabilistic analysis for prediction of the activity landscape and relative compound activities. ( 0,76776605804759 )
J Chem Inf Model - Mining the ChEMBL database: an efficient chemoinformatics workflow for assembling an ion channel-focused screening library. ( 0,767445823914009 )
J Chem Inf Model - Detailed computational study of the active site of the hepatitis C viral RNA polymerase to aid novel drug design. ( 0,767401507208721 )
J Chem Inf Model - Ligand and decoy sets for docking to G protein-coupled receptors. ( 0,767333753928664 )
J Chem Inf Model - Increasing the coverage of medicinal chemistry-relevant space in commercial fragments screening. ( 0,766926395328812 )
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. ( 0,766753382391644 )
J Chem Inf Model - Characterizing the diversity and biological relevance of the MLPCN assay manifold and screening set. ( 0,766670615635685 )
J Chem Inf Model - Hot spot analysis for driving the development of hits into leads in fragment-based drug discovery. ( 0,766338161034609 )
J Chem Inf Model - QSAR classification model for antibacterial compounds and its use in virtual screening. ( 0,765997171196209 )
J Chem Inf Model - Computational repositioning and experimental validation of approved drugs for HIF-prolyl hydroxylase inhibition. ( 0,765057596726492 )
J Chem Inf Model - Development of a comprehensive, validated pharmacophore hypothesis for anthrax toxin lethal factor (LF) inhibitors using genetic algorithms, Pareto scoring, and structural biology. ( 0,764903520576981 )
J Chem Inf Model - How diverse are diversity assessment methods? A comparative analysis and benchmarking of molecular descriptor space. ( 0,764730345721288 )
J Chem Inf Model - Identification of novel phosphodiesterase-4D inhibitors prescreened by molecular dynamics-augmented modeling and validated by bioassay. ( 0,764415024101092 )
J Chem Inf Model - De novo design of drug-like molecules by a fragment-based molecular evolutionary approach. ( 0,76386674307206 )
J Chem Inf Model - Fragment-based lead discovery and design. ( 0,761875010812663 )
J Chem Inf Model - Novel kinase inhibitors by reshuffling ligand functionalities across the human kinome. ( 0,760661982329539 )
J Chem Inf Model - Pharmacophore-based virtual screening and experimental validation of novel inhibitors against cyanobacterial fructose-1,6-/sedoheptulose-1,7-bisphosphatase. ( 0,760414569124472 )
J Chem Inf Model - Fighting obesity with a sugar-based library: discovery of novel MCH-1R antagonists by a new computational-VAST approach for exploration of GPCR binding sites. ( 0,759465634636715 )
J Chem Inf Model - Prediction of individual compounds forming activity cliffs using emerging chemical patterns. ( 0,756987145281916 )
J Chem Inf Model - Computer-aided structure-based design of multitarget leads for Alzheimer's disease. ( 0,756728032624167 )
J Chem Inf Model - Fighting high molecular weight in bioactive molecules with sub-pharmacophore-based virtual screening. ( 0,756614910860679 )
J Chem Inf Model - Introduction of target cliffs as a concept to identify and describe complex molecular selectivity patterns. ( 0,755884006808528 )
J Chem Inf Model - A multivariate chemical similarity approach to search for drugs of potential environmental concern. ( 0,755483375348627 )
J Chem Inf Model - Localized heuristic inverse quantitative structure activity relationship with bulk descriptors using numerical gradients. ( 0,754423253148216 )
Comput. Biol. Med. - Computational identification of novel histone deacetylase inhibitors by docking based QSAR. ( 0,754138847395139 )
J Chem Inf Model - Compound set enrichment: a novel approach to analysis of primary HTS data. ( 0,753919738895517 )