J Chem Inf Model - Structural similarity based kriging for quantitative structure activity and property relationship modeling.

Tópicos

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

Resumo

Structurally similar molecules tend to have similar properties, i.e. closer molecules in the molecular space are more likely to yield similar property values while distant molecules are more likely to yield different values. Based on this principle, we propose the use of a new method that takes into account the high dimensionality of the molecular space, predicting chemical, physical, or biological properties based on the most similar compounds with measured properties. This methodology uses ordinary kriging coupled with three different molecular similarity approaches (based on molecular descriptors, fingerprints, and atom matching) which creates an interpolation map over the molecular space that is capable of predicting properties/activities for diverse chemical data sets. The proposed method was tested in two data sets of diverse chemical compounds collected from the literature and preprocessed. One of the data sets contained dihydrofolate reductase inhibition activity data, and the second molecules for which aqueous solubility was known. The overall predictive results using kriging for both data sets comply with the results obtained in the literature using typical QSPR/QSAR approaches. However, the procedure did not involve any type of descriptor selection or even minimal information about each problem, suggesting that this approach is directly applicable to a large spectrum of problems in QSAR/QSPR. Furthermore, the predictive results improve significantly with the similarity threshold between the training and testing compounds, allowing the definition of a confidence threshold of similarity and error estimation for each case inferred. The use of kriging for interpolation over the molecular metric space is independent of the training data set size, and no reparametrizations are necessary when more compounds are added or removed from the set, and increasing the size of the database will consequentially improve the quality of the estimations. Finally it is shown that this model can be used for checking the consistency of measured data and for guiding an extension of the training set by determining the regions of the molecular space for which new experimental measurements could be used to maximize the model's predictive performance.

Resumo Limpo

structur similar molecul tend similar properti ie closer molecul molecular space like yield similar properti valu distant molecul like yield differ valu base principl propos use new method take account high dimension molecular space predict chemic physic biolog properti base similar compound measur properti methodolog use ordinari krige coupl three differ molecular similar approach base molecular descriptor fingerprint atom match creat interpol map molecular space capabl predict propertiesact divers chemic data set propos method test two data set divers chemic compound collect literatur preprocess one data set contain dihydrofol reductas inhibit activ data second molecul aqueous solubl known overal predict result use krige data set compli result obtain literatur use typic qsprqsar approach howev procedur involv type descriptor select even minim inform problem suggest approach direct applic larg spectrum problem qsarqspr furthermor predict result improv signific similar threshold train test compound allow definit confid threshold similar error estim case infer use krige interpol molecular metric space independ train data set size reparametr necessari compound ad remov set increas size databas will consequenti improv qualiti estim final shown model can use check consist measur data guid extens train set determin region molecular space new experiment measur use maxim model predict perform

Resumos Similares

J Chem Inf Model - Compound optimization through data set-dependent chemical transformations. ( 0,900637409234932 )
J Chem Inf Model - From activity cliffs to activity ridges: informative data structures for SAR analysis. ( 0,891269026321658 )
J Chem Inf Model - Automated recycling of chemistry for virtual screening and library design. ( 0,888725954438086 )
J Chem Inf Model - G-protein coupled receptors virtual screening using genetic algorithm focused chemical space. ( 0,883895404074461 )
J Chem Inf Model - Ligand- and structure-based virtual screening for clathrodin-derived human voltage-gated sodium channel modulators. ( 0,88275751938344 )
J Chem Inf Model - Design of multitarget activity landscapes that capture hierarchical activity cliff distributions. ( 0,880514615876842 )
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. ( 0,87927335974765 )
J Chem Inf Model - Mining the ChEMBL database: an efficient chemoinformatics workflow for assembling an ion channel-focused screening library. ( 0,879156414568845 )
J Chem Inf Model - In silico enzymatic synthesis of a 400,000 compound biochemical database for nontargeted metabolomics. ( 0,875880514051877 )
J Chem Inf Model - Noncontiguous atom matching structural similarity function. ( 0,873118924663696 )
J Chem Inf Model - TIN-a combinatorial compound collection of synthetically feasible multicomponent synthesis products. ( 0,869893368310194 )
J Chem Inf Model - Optimization of molecular representativeness. ( 0,868677356794213 )
J Chem Inf Model - De novo design of drug-like molecules by a fragment-based molecular evolutionary approach. ( 0,868661390278908 )
J Chem Inf Model - Application of computer-aided drug repurposing in the search of new cruzipain inhibitors: discovery of amiodarone and bromocriptine inhibitory effects. ( 0,868571726112286 )
J Chem Inf Model - Capturing structure-activity relationships from chemogenomic spaces. ( 0,867906490379888 )
Curr Comput Aided Drug Des - Development of Chemical Compound Libraries for In Silico Drug Screening. ( 0,867549719901485 )
J Chem Inf Model - Conditional probabilistic analysis for prediction of the activity landscape and relative compound activities. ( 0,866880289697427 )
J Chem Inf Model - Systematic assessment of compound series with SAR transfer potential. ( 0,863528282313552 )
J Chem Inf Model - Visual characterization and diversity quantification of chemical libraries: 1. creation of delimited reference chemical subspaces. ( 0,863250932609172 )
J Chem Inf Model - QSAR classification model for antibacterial compounds and its use in virtual screening. ( 0,863074411238919 )
J Chem Inf Model - Polypharmacology directed compound data mining: identification of promiscuous chemotypes with different activity profiles and comparison to approved drugs. ( 0,862566849999153 )
J Chem Inf Model - Natural product-like virtual libraries: recursive atom-based enumeration. ( 0,861110749166289 )
J Chem Inf Model - Combining horizontal and vertical substructure relationships in scaffold hierarchies for activity prediction. ( 0,859890639931513 )
J Chem Inf Model - Identification of 1,2,5-oxadiazoles as a new class of SENP2 inhibitors using structure based virtual screening. ( 0,859260088578055 )
J Chem Inf Model - Visualization and virtual screening of the chemical universe database GDB-17. ( 0,85881260997832 )
J Chem Inf Model - Increasing the coverage of medicinal chemistry-relevant space in commercial fragments screening. ( 0,858574219282626 )
J Chem Inf Model - Novel mycosin protease MycP1 inhibitors identified by virtual screening and 4D fingerprints. ( 0,856771866804049 )
J Chem Inf Model - Identifying compound-target associations by combining bioactivity profile similarity search and public databases mining. ( 0,853738803901713 )
J Chem Inf Model - Similarity boosted quantitative structure-activity relationship--a systematic study of enhancing structural descriptors by molecular similarity. ( 0,852158509790308 )
J Chem Inf Model - SABRE: ligand/structure-based virtual screening approach using consensus molecular-shape pattern recognition. ( 0,851170470034716 )
J Chem Inf Model - Target-independent prediction of drug synergies using only drug lipophilicity. ( 0,850490782702112 )
J Chem Inf Model - Identification of novel liver X receptor activators by structure-based modeling. ( 0,85005559757939 )
J Chem Inf Model - ColBioS-FlavRC: a collection of bioselective flavonoids and related compounds filtered from high-throughput screening outcomes. ( 0,848307482832408 )
J Chem Inf Model - How diverse are diversity assessment methods? A comparative analysis and benchmarking of molecular descriptor space. ( 0,848035515275896 )
J Chem Inf Model - Selection of in silico drug screening results for G-protein-coupled receptors by using universal active probes. ( 0,847625087564665 )
J Chem Inf Model - Novel method for pharmacophore analysis by examining the joint pharmacophore space. ( 0,845588799713102 )
J Chem Inf Model - Identification of novel malarial cysteine protease inhibitors using structure-based virtual screening of a focused cysteine protease inhibitor library. ( 0,84513726721687 )
J Chem Inf Model - Hit expansion approaches using multiple similarity methods and virtualized query structures. ( 0,844858615109681 )
J Chem Inf Model - Identification of a novel inhibitor of dengue virus protease through use of a virtual screening drug discovery Web portal. ( 0,844205502720813 )
J Chem Inf Model - Searching for recursively defined generic chemical patterns in nonenumerated fragment spaces. ( 0,843107549273698 )
J Chem Inf Model - Identification of novel serotonin transporter compounds by virtual screening. ( 0,842326413369865 )
J Chem Inf Model - Identification of multitarget activity ridges in high-dimensional bioactivity spaces. ( 0,841957814028328 )
J Chem Inf Model - Introduction of target cliffs as a concept to identify and describe complex molecular selectivity patterns. ( 0,841738811080089 )
J Chem Inf Model - Discovery of new selective human aldose reductase inhibitors through virtual screening multiple binding pocket conformations. ( 0,841716492887368 )
J Chem Inf Model - Scanning structure-activity relationships with structure-activity similarity and related maps: from consensus activity cliffs to selectivity switches. ( 0,840905324228663 )
J Chem Inf Model - Compound set enrichment: a novel approach to analysis of primary HTS data. ( 0,84016384045604 )
J Chem Inf Model - Enumeration of 166 billion organic small molecules in the chemical universe database GDB-17. ( 0,839824129765774 )
J Chem Inf Model - Chemical data visualization and analysis with incremental generative topographic mapping: big data challenge. ( 0,839077508670104 )
J Chem Inf Model - Fragment-based lead discovery and design. ( 0,837015032713479 )
J Chem Inf Model - Identification of a new class of FtsZ inhibitors by structure-based design and in vitro screening. ( 0,836794261074227 )
J Chem Inf Model - Fighting high molecular weight in bioactive molecules with sub-pharmacophore-based virtual screening. ( 0,83652709163421 )
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. ( 0,836459505282321 )
J Chem Inf Model - Characterizing the diversity and biological relevance of the MLPCN assay manifold and screening set. ( 0,836281381807691 )
J Chem Inf Model - Large-scale assessment of activity landscape feature probabilities of bioactive compounds. ( 0,835776430006368 )
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. ( 0,835404874390015 )
J Chem Inf Model - Navigating high-dimensional activity landscapes: design and application of the ligand-target differentiation map. ( 0,834592637083916 )
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. ( 0,834240887973692 )
J Chem Inf Model - Discovery of novel histamine H4 and serotonin transporter ligands using the topological feature tree descriptor. ( 0,832094814805961 )
J Chem Inf Model - Hsp90 inhibitors, part 2: combining ligand-based and structure-based approaches for virtual screening application. ( 0,831815503382025 )
J Chem Inf Model - Discovery of a7-nicotinic receptor ligands by virtual screening of the chemical universe database GDB-13. ( 0,831588372597298 )
J Chem Inf Model - Multitarget structure-activity relationships characterized by activity-difference maps and consensus similarity measure. ( 0,830712101806324 )
J Chem Inf Model - Scaffold diversity of exemplified medicinal chemistry space. ( 0,830363855404023 )
J Chem Inf Model - A multivariate chemical similarity approach to search for drugs of potential environmental concern. ( 0,830093553472055 )
J Chem Inf Model - Molecular topology analysis of the differences between drugs, clinical candidate compounds, and bioactive molecules. ( 0,829471673802314 )
J Chem Inf Model - Design of a three-dimensional multitarget activity landscape. ( 0,827947481734661 )
J Chem Inf Model - Construction and use of fragment-augmented molecular Hasse diagrams. ( 0,826805815649671 )
J Chem Inf Model - Prediction of new bioactive molecules using a Bayesian belief network. ( 0,825374225827471 )
J Chem Inf Model - A new protocol for predicting novel GSK-3? ATP competitive inhibitors. ( 0,824999688638034 )
J Chem Inf Model - Neighborhood-based prediction of novel active compounds from SAR matrices. ( 0,824013863253281 )
J Chem Inf Model - Activity-aware clustering of high throughput screening data and elucidation of orthogonal structure-activity relationships. ( 0,822317808681184 )
J Chem Inf Model - Discovery of chemical compound groups with common structures by a network analysis approach (affinity prediction method). ( 0,821393112457318 )
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. ( 0,818691973162287 )
Comput Biol Chem - The optimization of running time for a maximum common substructure-based algorithm and its application in drug design. ( 0,817035545175914 )
J Chem Inf Model - Identification of compounds with potential antibacterial activity against Mycobacterium through structure-based drug screening. ( 0,816988041582011 )
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. ( 0,816918231804539 )
J Integr Bioinform - Database supported candidate search for metabolite identification. ( 0,816498802495315 )
J Chem Inf Model - Library enhancement through the wisdom of crowds. ( 0,815636536353703 )
J Chem Inf Model - Prediction of individual compounds forming activity cliffs using emerging chemical patterns. ( 0,81506200040751 )
J Chem Inf Model - BioSM: metabolomics tool for identifying endogenous mammalian biochemical structures in chemical structure space. ( 0,814706669223683 )
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. ( 0,812342923984187 )
J Chem Inf Model - Bioturbo similarity searching: combining chemical and biological similarity to discover structurally diverse bioactive molecules. ( 0,811736094265419 )
J Chem Inf Model - Identification of descriptors capturing compound class-specific features by mutual information analysis. ( 0,809690272030499 )
J Chem Inf Model - Multiple e-pharmacophore modeling, 3D-QSAR, and high-throughput virtual screening of hepatitis C virus NS5B polymerase inhibitors. ( 0,809667735950104 )
J Am Med Inform Assoc - Drug repurposing: mining protozoan proteomes for targets of known bioactive compounds. ( 0,809584295567973 )
J Chem Inf Model - A searchable map of PubChem. ( 0,809322318214369 )
J Chem Inf Model - Rationalizing the role of SAR tolerance for ligand-based virtual screening. ( 0,80920688975864 )
J Chem Inf Model - MMP-Cliffs: systematic identification of activity cliffs on the basis of matched molecular pairs. ( 0,808666946190128 )
J Chem Inf Model - Similarity searching for potent compounds using feature selection. ( 0,808284596112539 )
J Chem Inf Model - Atom pair 2D-fingerprints perceive 3D-molecular shape and pharmacophores for very fast virtual screening of ZINC and GDB-17. ( 0,8076311808266 )
J Chem Inf Model - Automatic tailoring and transplanting: a practical method that makes virtual screening more useful. ( 0,807616469575929 )
J Chem Inf Model - SimG: an alignment based method for evaluating the similarity of small molecules and binding sites. ( 0,806677817909931 )
J Chem Inf Model - Identification of sumoylation activating enzyme 1 inhibitors by structure-based virtual screening. ( 0,805883624216359 )
J Chem Inf Model - Prediction of activity cliffs using support vector machines. ( 0,802735321946028 )
J Chem Inf Model - Identification of novel potential antibiotics against Staphylococcus using structure-based drug screening targeting dihydrofolate reductase. ( 0,802582485308644 )
J Chem Inf Model - SMIfp (SMILES fingerprint) chemical space for virtual screening and visualization of large databases of organic molecules. ( 0,801963887568152 )
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. ( 0,799828166353198 )
J Chem Inf Model - Enrichment of chemical libraries docked to protein conformational ensembles and application to aldehyde dehydrogenase 2. ( 0,799629849317751 )
J Chem Inf Model - Feasibility of using molecular docking-based virtual screening for searching dual target kinase inhibitors. ( 0,798691300945926 )
J Chem Inf Model - FINDSITE(comb): a threading/structure-based, proteomic-scale virtual ligand screening approach. ( 0,797384407496209 )
J Chem Inf Model - Boosting virtual screening enrichments with data fusion: coalescing hits from two-dimensional fingerprints, shape, and docking. ( 0,797164164842817 )