J Chem Inf Model - Noncontiguous atom matching structural similarity function.

Tópicos

{ compound(1573) activ(1297) structur(1058) }
{ method(1219) similar(1157) match(930) }
{ high(1669) rate(1365) level(1280) }
{ measur(2081) correl(1212) valu(896) }
{ error(1145) method(1030) estim(1020) }
{ case(1353) use(1143) diagnosi(1136) }
{ can(981) present(881) function(850) }
{ structur(1116) can(940) graph(676) }
{ bind(1733) structur(1185) ligand(1036) }
{ imag(1947) propos(1133) code(1026) }
{ import(1318) role(1303) understand(862) }
{ method(1969) cluster(1462) data(1082) }
{ data(2317) use(1299) case(1017) }
{ detect(2391) sensit(1101) algorithm(908) }
{ sequenc(1873) structur(1644) protein(1328) }
{ gene(2352) biolog(1181) express(1162) }
{ health(1844) social(1437) communiti(874) }
{ can(774) often(719) complex(702) }
{ featur(3375) classif(2383) classifi(1994) }
{ studi(2440) review(1878) systemat(933) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ problem(2511) optim(1539) algorithm(950) }
{ learn(2355) train(1041) set(1003) }
{ clinic(1479) use(1117) guidelin(835) }
{ extract(1171) text(1153) clinic(932) }
{ featur(1941) imag(1645) propos(1176) }
{ studi(1410) differ(1259) use(1210) }
{ record(1888) medic(1808) patient(1693) }
{ model(3480) simul(1196) paramet(876) }
{ ehr(2073) health(1662) electron(1139) }
{ patient(2837) hospit(1953) medic(668) }
{ model(2656) set(1616) predict(1553) }
{ cost(1906) reduc(1198) effect(832) }
{ sampl(1606) size(1419) use(1276) }
{ patient(1821) servic(1111) care(1106) }
{ analysi(2126) use(1163) compon(1037) }
{ activ(1452) weight(1219) physic(1104) }
{ method(2212) result(1239) propos(1039) }
{ model(3404) distribut(989) bayesian(671) }
{ data(1737) use(1416) pattern(1282) }
{ inform(2794) health(2639) internet(1427) }
{ system(1976) rule(880) can(841) }
{ imag(1057) registr(996) error(939) }
{ imag(2830) propos(1344) filter(1198) }
{ network(2748) neural(1063) input(814) }
{ imag(2675) segment(2577) method(1081) }
{ patient(2315) diseas(1263) diabet(1191) }
{ take(945) account(800) differ(722) }
{ motion(1329) object(1292) video(1091) }
{ assess(1506) score(1403) qualiti(1306) }
{ treatment(1704) effect(941) patient(846) }
{ framework(1458) process(801) describ(734) }
{ chang(1828) time(1643) increas(1301) }
{ concept(1167) ontolog(924) domain(897) }
{ algorithm(1844) comput(1787) effici(935) }
{ method(1557) propos(1049) approach(1037) }
{ data(1714) softwar(1251) tool(1186) }
{ 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) }
{ method(984) reconstruct(947) comput(926) }
{ search(2224) databas(1162) retriev(909) }
{ howev(809) still(633) remain(590) }
{ data(3963) clinic(1234) research(1004) }
{ risk(3053) factor(974) diseas(938) }
{ perform(999) metric(946) measur(919) }
{ research(1085) discuss(1038) issu(1018) }
{ system(1050) medic(1026) inform(1018) }
{ 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) }
{ health(3367) inform(1360) care(1135) }
{ monitor(1329) mobil(1314) devic(1160) }
{ state(1844) use(1261) util(961) }
{ research(1218) medic(880) student(794) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ 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) }
{ use(2086) technolog(871) perceiv(783) }
{ 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) }

Resumo

Measuring similarity between molecules is a fundamental problem in cheminformatics. Given that similar molecules tend to have similar physical, chemical, and biological properties, the notion of molecular similarity plays an important role in the exploration of molecular data sets, query-retrieval in molecular databases, and in structure-property/activity modeling. Various methods to define structural similarity between molecules are available in the literature, but so far none has been used with consistent and reliable results for all situations. We propose a new similarity method based on atom alignment for the analysis of structural similarity between molecules. This method is based on the comparison of the bonding profiles of atoms on comparable molecules, including features that are seldom found in other structural or graph matching approaches like chirality or double bond stereoisomerism. The similarity measure is then defined on the annotated molecular graph, based on an iterative directed graph similarity procedure and optimal atom alignment between atoms using a pairwise matching algorithm. With the proposed approach the similarities detected are more intuitively understood because similar atoms in the molecules are explicitly shown. This noncontiguous atom matching structural similarity method (NAMS) was tested and compared with one of the most widely used similarity methods (fingerprint-based similarity) using three difficult data sets with different characteristics. Despite having a higher computational cost, the method performed well being able to distinguish either different or very similar hydrocarbons that were indistinguishable using a fingerprint-based approach. NAMS also verified the similarity principle using a data set of structurally similar steroids with differences in the binding affinity to the corticosteroid binding globulin receptor by showing that pairs of steroids with a high degree of similarity (>80%) tend to have smaller differences in the absolute value of binding activity. Using a highly diverse set of compounds with information about the monoamine oxidase inhibition level, the method was also able to recover a significantly higher average fraction of active compounds when the seed is active for different cutoff threshold values of similarity. Particularly, for the cutoff threshold values of 86%, 93%, and 96.5%, NAMS was able to recover a fraction of actives of 0.57, 0.63, and 0.83, respectively, while the fingerprint-based approach was able to recover a fraction of actives of 0.41, 0.40, and 0.39, respectively. NAMS is made available freely for the whole community in a simple Web based tool as well as the Python source code at http://nams.lasige.di.fc.ul.pt/.

Resumo Limpo

measur similar molecul fundament problem cheminformat given similar molecul tend similar physic chemic biolog properti notion molecular similar play import role explor molecular data set queryretriev molecular databas structurepropertyact model various method defin structur similar molecul avail literatur far none use consist reliabl result situat propos new similar method base atom align analysi structur similar molecul method base comparison bond profil atom compar molecul includ featur seldom found structur graph match approach like chiral doubl bond stereoisomer similar measur defin annot molecular graph base iter direct graph similar procedur optim atom align atom use pairwis match algorithm propos approach similar detect intuit understood similar atom molecul explicit shown noncontigu atom match structur similar method nam test compar one wide use similar method fingerprintbas similar use three difficult data set differ characterist despit higher comput cost method perform well abl distinguish either differ similar hydrocarbon indistinguish use fingerprintbas approach nam also verifi similar principl use data set structur similar steroid differ bind affin corticosteroid bind globulin receptor show pair steroid high degre similar tend smaller differ absolut valu bind activ use high divers set compound inform monoamin oxidas inhibit level method also abl recov signific higher averag fraction activ compound seed activ differ cutoff threshold valu similar particular cutoff threshold valu nam abl recov fraction activ respect fingerprintbas approach abl recov fraction activ respect nam made avail freeli whole communiti simpl web base tool well python sourc code httpnamslasigedifculpt

Resumos Similares

J Chem Inf Model - Ligand- and structure-based virtual screening for clathrodin-derived human voltage-gated sodium channel modulators. ( 0,881631167589578 )
J Chem Inf Model - Structural similarity based kriging for quantitative structure activity and property relationship modeling. ( 0,873118924663696 )
J Chem Inf Model - MMP-Cliffs: systematic identification of activity cliffs on the basis of matched molecular pairs. ( 0,867253382278736 )
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. ( 0,860733717721074 )
J Chem Inf Model - SimG: an alignment based method for evaluating the similarity of small molecules and binding sites. ( 0,856593695328507 )
J Chem Inf Model - Systematic assessment of compound series with SAR transfer potential. ( 0,851919668738192 )
J Chem Inf Model - From activity cliffs to activity ridges: informative data structures for SAR analysis. ( 0,848834185618091 )
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. ( 0,8486469982925 )
J Chem Inf Model - In silico enzymatic synthesis of a 400,000 compound biochemical database for nontargeted metabolomics. ( 0,846371015467012 )
J Chem Inf Model - Hit expansion approaches using multiple similarity methods and virtualized query structures. ( 0,845752289229286 )
J Chem Inf Model - G-protein coupled receptors virtual screening using genetic algorithm focused chemical space. ( 0,84061697610792 )
J Chem Inf Model - SHAFTS: a hybrid approach for 3D molecular similarity calculation. 1. Method and assessment of virtual screening. ( 0,838804218729686 )
J Chem Inf Model - COSMOsim3D: 3D-similarity and alignment based on COSMO polarization charge densities. ( 0,83678955993516 )
J Chem Inf Model - Compound optimization through data set-dependent chemical transformations. ( 0,834832371260798 )
J Chem Inf Model - Design of multitarget activity landscapes that capture hierarchical activity cliff distributions. ( 0,83456485424293 )
J Chem Inf Model - Navigating high-dimensional activity landscapes: design and application of the ligand-target differentiation map. ( 0,830041081192149 )
J Chem Inf Model - Identification of 1,2,5-oxadiazoles as a new class of SENP2 inhibitors using structure based virtual screening. ( 0,825448736497343 )
J Chem Inf Model - SABRE: ligand/structure-based virtual screening approach using consensus molecular-shape pattern recognition. ( 0,823788134538929 )
J Chem Inf Model - Visualization and virtual screening of the chemical universe database GDB-17. ( 0,823005929202348 )
J Chem Inf Model - Polypharmacology directed compound data mining: identification of promiscuous chemotypes with different activity profiles and comparison to approved drugs. ( 0,822158978128652 )
J Chem Inf Model - Application of computer-aided drug repurposing in the search of new cruzipain inhibitors: discovery of amiodarone and bromocriptine inhibitory effects. ( 0,822087792822859 )
J Chem Inf Model - Mining the ChEMBL database: an efficient chemoinformatics workflow for assembling an ion channel-focused screening library. ( 0,821965279808192 )
J Chem Inf Model - Discovery of novel histamine H4 and serotonin transporter ligands using the topological feature tree descriptor. ( 0,821526959497913 )
J Chem Inf Model - Automated recycling of chemistry for virtual screening and library design. ( 0,818529317622025 )
J Chem Inf Model - Conditional probabilistic analysis for prediction of the activity landscape and relative compound activities. ( 0,817425550956281 )
J Chem Inf Model - TIN-a combinatorial compound collection of synthetically feasible multicomponent synthesis products. ( 0,815763737894812 )
J Chem Inf Model - Prediction of new bioactive molecules using a Bayesian belief network. ( 0,815294669886102 )
J Chem Inf Model - Combining horizontal and vertical substructure relationships in scaffold hierarchies for activity prediction. ( 0,815169653286987 )
J Chem Inf Model - Systematic identification of scaffolds representing compounds active against individual targets and single or multiple target families. ( 0,814671766958288 )
J Chem Inf Model - Identification of novel liver X receptor activators by structure-based modeling. ( 0,814337723195232 )
J Chem Inf Model - Neighborhood-based prediction of novel active compounds from SAR matrices. ( 0,813295864188336 )
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. ( 0,812402306328186 )
Curr Comput Aided Drug Des - Development of Chemical Compound Libraries for In Silico Drug Screening. ( 0,808257141783155 )
J Chem Inf Model - Natural product-like virtual libraries: recursive atom-based enumeration. ( 0,808253248589545 )
J Chem Inf Model - Discovery of new selective human aldose reductase inhibitors through virtual screening multiple binding pocket conformations. ( 0,806164129833197 )
J Chem Inf Model - Ligand and decoy sets for docking to G protein-coupled receptors. ( 0,805145441123676 )
J Chem Inf Model - Extending the activity cliff concept: structural categorization of activity cliffs and systematic identification of different types of cliffs in the ChEMBL database. ( 0,804121716521183 )
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. ( 0,803789933381065 )
J Chem Inf Model - Visual characterization and diversity quantification of chemical libraries: 1. creation of delimited reference chemical subspaces. ( 0,80297134911053 )
J Chem Inf Model - Fragment-based lead discovery and design. ( 0,802573208092051 )
J Chem Inf Model - An integrated virtual screening approach for VEGFR-2 inhibitors. ( 0,802230759286703 )
J Chem Inf Model - Characterizing the diversity and biological relevance of the MLPCN assay manifold and screening set. ( 0,801257716976513 )
J Chem Inf Model - Increasing the coverage of medicinal chemistry-relevant space in commercial fragments screening. ( 0,80006990461501 )
J Chem Inf Model - Boosting virtual screening enrichments with data fusion: coalescing hits from two-dimensional fingerprints, shape, and docking. ( 0,799872112256694 )
J Chem Inf Model - In silico target predictions: defining a benchmarking data set and comparison of performance of the multiclass Na?ve Bayes and Parzen-Rosenblatt window. ( 0,79939689576024 )
J Chem Inf Model - Identifying compound-target associations by combining bioactivity profile similarity search and public databases mining. ( 0,798454497620606 )
J Chem Inf Model - Identification of a novel inhibitor of dengue virus protease through use of a virtual screening drug discovery Web portal. ( 0,798067643700806 )
J Chem Inf Model - Large-scale assessment of activity landscape feature probabilities of bioactive compounds. ( 0,798053063008874 )
J Chem Inf Model - Prediction of individual compounds forming activity cliffs using emerging chemical patterns. ( 0,797971553517123 )
J Chem Inf Model - Identification of multitarget activity ridges in high-dimensional bioactivity spaces. ( 0,796004173972086 )
J Chem Inf Model - Target-independent prediction of drug synergies using only drug lipophilicity. ( 0,795751797608685 )
J Chem Inf Model - Bioturbo similarity searching: combining chemical and biological similarity to discover structurally diverse bioactive molecules. ( 0,795523710589693 )
J Chem Inf Model - Identification of novel serotonin transporter compounds by virtual screening. ( 0,79453763243506 )
J Chem Inf Model - Selection of in silico drug screening results for G-protein-coupled receptors by using universal active probes. ( 0,792103023499757 )
J Chem Inf Model - Quantitative structure-activity relationship models of chemical transformations from matched pairs analyses. ( 0,790660802379695 )
J Chem Inf Model - QSAR classification model for antibacterial compounds and its use in virtual screening. ( 0,790272200119953 )
J Chem Inf Model - Novel method for pharmacophore analysis by examining the joint pharmacophore space. ( 0,788126711648734 )
J Chem Inf Model - How diverse are diversity assessment methods? A comparative analysis and benchmarking of molecular descriptor space. ( 0,787941049703582 )
J Chem Inf Model - Scaffold diversity of exemplified medicinal chemistry space. ( 0,78739235605929 )
J Chem Inf Model - Atom pair 2D-fingerprints perceive 3D-molecular shape and pharmacophores for very fast virtual screening of ZINC and GDB-17. ( 0,786428369795335 )
J Chem Inf Model - Searching for recursively defined generic chemical patterns in nonenumerated fragment spaces. ( 0,785900314352497 )
J Chem Inf Model - Compound set enrichment: a novel approach to analysis of primary HTS data. ( 0,785660263185117 )
J Chem Inf Model - Capturing structure-activity relationships from chemogenomic spaces. ( 0,785279232138423 )
J Chem Inf Model - Discovery of a7-nicotinic receptor ligands by virtual screening of the chemical universe database GDB-13. ( 0,784860682443892 )
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. ( 0,784843558412757 )
J Chem Inf Model - Structure-based virtual screening approach for discovery of covalently bound ligands. ( 0,784629371763918 )
J Chem Inf Model - Multiple e-pharmacophore modeling, 3D-QSAR, and high-throughput virtual screening of hepatitis C virus NS5B polymerase inhibitors. ( 0,784503150227556 )
J Chem Inf Model - Fighting high molecular weight in bioactive molecules with sub-pharmacophore-based virtual screening. ( 0,783932310989435 )
J Chem Inf Model - Enumeration of 166 billion organic small molecules in the chemical universe database GDB-17. ( 0,782920121656573 )
J Chem Inf Model - Identification of a new class of FtsZ inhibitors by structure-based design and in vitro screening. ( 0,782095178650198 )
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. ( 0,781671777463609 )
J Chem Inf Model - Identification of novel malarial cysteine protease inhibitors using structure-based virtual screening of a focused cysteine protease inhibitor library. ( 0,781087863411725 )
J Chem Inf Model - De novo design of drug-like molecules by a fragment-based molecular evolutionary approach. ( 0,780702456811068 )
J Chem Inf Model - Rapid scanning structure-activity relationships in combinatorial data sets: identification of activity switches. ( 0,780589335126007 )
J Chem Inf Model - Design of a three-dimensional multitarget activity landscape. ( 0,780351732742252 )
J Chem Inf Model - Novel mycosin protease MycP1 inhibitors identified by virtual screening and 4D fingerprints. ( 0,778621110855232 )
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. ( 0,777235637743386 )
J Chem Inf Model - Introduction of target cliffs as a concept to identify and describe complex molecular selectivity patterns. ( 0,776705076611504 )
J Chem Inf Model - Identification of sumoylation activating enzyme 1 inhibitors by structure-based virtual screening. ( 0,776661774345731 )
J Chem Inf Model - Exploration of 3D activity cliffs on the basis of compound binding modes and comparison of 2D and 3D cliffs. ( 0,775907835083183 )
J Chem Inf Model - Identification of descriptors capturing compound class-specific features by mutual information analysis. ( 0,774615876185043 )
J Chem Inf Model - Scaffold-focused virtual screening: prospective application to the discovery of TTK inhibitors. ( 0,774351704447132 )
J Chem Inf Model - Identification of novel S-adenosyl-L-homocysteine hydrolase inhibitors through homology-model-based virtual screening, synthesis, and biological evaluation. ( 0,774264435295998 )
J Chem Inf Model - A new protocol for predicting novel GSK-3? ATP competitive inhibitors. ( 0,773401681435889 )
J Chem Inf Model - Similarity boosted quantitative structure-activity relationship--a systematic study of enhancing structural descriptors by molecular similarity. ( 0,772026184371036 )
J Chem Inf Model - FINDSITE(comb): a threading/structure-based, proteomic-scale virtual ligand screening approach. ( 0,771929433998677 )
J Chem Inf Model - Molecular topology analysis of the differences between drugs, clinical candidate compounds, and bioactive molecules. ( 0,771126617175878 )
Comput Biol Chem - The optimization of running time for a maximum common substructure-based algorithm and its application in drug design. ( 0,770715808849544 )
J Chem Inf Model - Enrichment of chemical libraries docked to protein conformational ensembles and application to aldehyde dehydrogenase 2. ( 0,769712938970947 )
J Chem Inf Model - A multivariate chemical similarity approach to search for drugs of potential environmental concern. ( 0,768228003359649 )
J Chem Inf Model - Virtual screening yields inhibitors of novel antifungal drug target, benzoate 4-monooxygenase. ( 0,767175527227988 )
J Chem Inf Model - ColBioS-FlavRC: a collection of bioselective flavonoids and related compounds filtered from high-throughput screening outcomes. ( 0,764659824328643 )
J Chem Inf Model - Identification of compounds with potential antibacterial activity against Mycobacterium through structure-based drug screening. ( 0,764101201293246 )
J Chem Inf Model - Freely available conformer generation methods: how good are they? ( 0,763553459432687 )
J Chem Inf Model - Similarity searching for potent compounds using feature selection. ( 0,763021054824832 )
J Chem Inf Model - Scanning structure-activity relationships with structure-activity similarity and related maps: from consensus activity cliffs to selectivity switches. ( 0,761307547955397 )
J Chem Inf Model - Optimization of molecular representativeness. ( 0,761042423631404 )
J Chem Inf Model - Identification of novel potential antibiotics against Staphylococcus using structure-based drug screening targeting dihydrofolate reductase. ( 0,760192298288562 )
J Chem Inf Model - Library enhancement through the wisdom of crowds. ( 0,757927152157677 )
J Chem Inf Model - Activity-aware clustering of high throughput screening data and elucidation of orthogonal structure-activity relationships. ( 0,757143336374579 )