J Chem Inf Model - Prediction of compounds with closely related activity profiles using weighted support vector machine linear combinations.

Tópicos

{ compound(1573) activ(1297) structur(1058) }
{ use(976) code(926) identifi(902) }
{ featur(3375) classif(2383) classifi(1994) }
{ model(2656) set(1616) predict(1553) }
{ gene(2352) biolog(1181) express(1162) }
{ activ(1452) weight(1219) physic(1104) }
{ bind(1733) structur(1185) ligand(1036) }
{ signal(2180) analysi(812) frequenc(800) }
{ imag(1947) propos(1133) code(1026) }
{ extract(1171) text(1153) clinic(932) }
{ method(1557) propos(1049) approach(1037) }
{ general(901) number(790) one(736) }
{ studi(1119) effect(1106) posit(819) }
{ analysi(2126) use(1163) compon(1037) }
{ imag(1057) registr(996) error(939) }
{ sequenc(1873) structur(1644) protein(1328) }
{ concept(1167) ontolog(924) domain(897) }
{ control(1307) perform(991) simul(935) }
{ care(1570) inform(1187) nurs(1089) }
{ howev(809) still(633) remain(590) }
{ studi(1410) differ(1259) use(1210) }
{ research(1085) discuss(1038) issu(1018) }
{ data(3008) multipl(1320) sourc(1022) }
{ use(2086) technolog(871) perceiv(783) }
{ 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) }
{ system(1976) rule(880) can(841) }
{ measur(2081) correl(1212) valu(896) }
{ 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) }
{ 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) }
{ data(1714) softwar(1251) tool(1186) }
{ design(1359) user(1324) use(1319) }
{ model(2220) cell(1177) simul(1124) }
{ method(984) reconstruct(947) comput(926) }
{ search(2224) databas(1162) retriev(909) }
{ featur(1941) imag(1645) propos(1176) }
{ case(1353) use(1143) diagnosi(1136) }
{ data(3963) clinic(1234) research(1004) }
{ 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) }
{ 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) }
{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ 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) }
{ health(1844) social(1437) communiti(874) }
{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ 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) }
{ method(1969) cluster(1462) data(1082) }
{ method(2212) result(1239) propos(1039) }

Resumo

Using support vector machine (SVM) ranking, a complex multi-class prediction task has been investigated involving sets of compounds that were active against related targets and represented all possible combinations of single-, dual-, and triple-target activities. Standard SVM models were not capable of differentiating compounds with overlapping yet distinct activity profiles. To address this problem, we designed differentially weighted SVM linear combinations that were found to preferentially detect compounds with desired activity profiles and deprioritize others. Hence, combining independently derived SVM models using negative and positive linear weighting factors balanced relative contributions from individual reference sets and successfully distinguished between compounds with overlapping activity profiles.

Resumo Limpo

use support vector machin svm rank complex multiclass predict task investig involv set compound activ relat target repres possibl combin singl dual tripletarget activ standard svm model capabl differenti compound overlap yet distinct activ profil address problem design differenti weight svm linear combin found preferenti detect compound desir activ profil depriorit other henc combin independ deriv svm model use negat posit linear weight factor balanc relat contribut individu refer set success distinguish compound overlap activ profil

Resumos Similares

AMIA Annu Symp Proc - An approximate matching method for clinical drug names. ( 0,710892640691208 )
J Chem Inf Model - Conditional probabilities of activity landscape features for individual compounds. ( 0,677812001909739 )
Methods Inf Med - Development of ICF code selection tools for mental health care. ( 0,661190614333607 )
J Chem Inf Model - Structure based model for the prediction of phospholipidosis induction potential of small molecules. ( 0,659070069990217 )
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. ( 0,654632501719136 )
J Chem Inf Model - Scanning structure-activity relationships with structure-activity similarity and related maps: from consensus activity cliffs to selectivity switches. ( 0,654328084435624 )
J Chem Inf Model - Binary classification of a large collection of environmental chemicals from estrogen receptor assays by quantitative structure-activity relationship and machine learning methods. ( 0,650687258851788 )
J Chem Inf Model - Bioturbo similarity searching: combining chemical and biological similarity to discover structurally diverse bioactive molecules. ( 0,649663995154234 )
J Chem Inf Model - Predictions of BuChE inhibitors using support vector machine and naive Bayesian classification techniques in drug discovery. ( 0,645455334171214 )
J Chem Inf Model - Application of the 4D fingerprint method with a robust scoring function for scaffold-hopping and drug repurposing strategies. ( 0,64480005816439 )
J Chem Inf Model - Profile-QSAR and Surrogate AutoShim protein-family modeling of proteases. ( 0,643466208857134 )
J Chem Inf Model - DiSCuS: an open platform for (not only) virtual screening results management. ( 0,64304094194901 )
J Chem Inf Model - Chemical data visualization and analysis with incremental generative topographic mapping: big data challenge. ( 0,642794049037448 )
J Chem Inf Model - Compound set enrichment: a novel approach to analysis of primary HTS data. ( 0,634429141079124 )
J Am Med Inform Assoc - Using FDA reports to inform a classification for health information technology safety problems. ( 0,632468702637229 )
J Chem Inf Model - From activity cliffs to activity ridges: informative data structures for SAR analysis. ( 0,62975287259411 )
J Chem Inf Model - Visualization and virtual screening of the chemical universe database GDB-17. ( 0,626693098345976 )
J Chem Inf Model - Oversampling to overcome overfitting: exploring the relationship between data set composition, molecular descriptors, and predictive modeling methods. ( 0,62663229118821 )
J Chem Inf Model - Modeling drug-induced anorexia by molecular topology. ( 0,625596479109881 )
J Chem Inf Model - Multiple e-pharmacophore modeling, 3D-QSAR, and high-throughput virtual screening of hepatitis C virus NS5B polymerase inhibitors. ( 0,623959171215659 )
J Chem Inf Model - BioSM: metabolomics tool for identifying endogenous mammalian biochemical structures in chemical structure space. ( 0,621824025912714 )
J Chem Inf Model - De novo design of drug-like molecules by a fragment-based molecular evolutionary approach. ( 0,621547699364605 )
J Chem Inf Model - Unique ring families: a chemically meaningful description of molecular ring topologies. ( 0,619680521660393 )
J Chem Inf Model - Hsp90 inhibitors, part 1: definition of 3-D QSAutogrid/R models as a tool for virtual screening. ( 0,619668330791488 )
J Chem Inf Model - Exploring the biologically relevant chemical space for drug discovery. ( 0,618867386275688 )
J Chem Inf Model - Utility-aware screening with clique-oriented prioritization. ( 0,618766140414882 )
J Chem Inf Model - A new protocol for predicting novel GSK-3? ATP competitive inhibitors. ( 0,618204391646866 )
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. ( 0,617801732616178 )
J Chem Inf Model - QSAR classification model for antibacterial compounds and its use in virtual screening. ( 0,617766356048107 )
Comput. Biol. Med. - Computational identification of novel histone deacetylase inhibitors by docking based QSAR. ( 0,616814631619735 )
J Chem Inf Model - Construction and use of fragment-augmented molecular Hasse diagrams. ( 0,61566422210544 )
J Chem Inf Model - Chemical name to structure: OPSIN, an open source solution. ( 0,614316351854125 )
J Chem Inf Model - Feasibility of using molecular docking-based virtual screening for searching dual target kinase inhibitors. ( 0,610499118584685 )
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. ( 0,609235088038799 )
Comput Biol Chem - Circular code motifs in transfer RNAs. ( 0,606949676101651 )
J Chem Inf Model - Accurate specification of molecular structures: the case for zero-order bonds and explicit hydrogen counting. ( 0,606012988777142 )
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. ( 0,604896119331368 )
J Chem Inf Model - Discovery of a7-nicotinic receptor ligands by virtual screening of the chemical universe database GDB-13. ( 0,604610197153698 )
J Am Med Inform Assoc - Drug repurposing: mining protozoan proteomes for targets of known bioactive compounds. ( 0,604209049090296 )
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. ( 0,603630134180225 )
J Chem Inf Model - Identification of novel serotonin transporter compounds by virtual screening. ( 0,60340446720399 )
J Chem Inf Model - Pharmacophore-based virtual screening and experimental validation of novel inhibitors against cyanobacterial fructose-1,6-/sedoheptulose-1,7-bisphosphatase. ( 0,60273817602575 )
J Chem Inf Model - Hsp90 inhibitors, part 2: combining ligand-based and structure-based approaches for virtual screening application. ( 0,602646860312431 )
J Chem Inf Model - Structural similarity based kriging for quantitative structure activity and property relationship modeling. ( 0,601846491297718 )
J Chem Inf Model - Discovery of novel histamine H4 and serotonin transporter ligands using the topological feature tree descriptor. ( 0,60029147880288 )
J Chem Inf Model - AlzPlatform: an Alzheimer's disease domain-specific chemogenomics knowledgebase for polypharmacology and target identification research. ( 0,599244702363937 )
J Chem Inf Model - A multivariate chemical similarity approach to search for drugs of potential environmental concern. ( 0,598745701799493 )
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. ( 0,59835821485634 )
J Chem Inf Model - Selection of in silico drug screening results for G-protein-coupled receptors by using universal active probes. ( 0,59787279058169 )
J Chem Inf Model - Mining the ChEMBL database: an efficient chemoinformatics workflow for assembling an ion channel-focused screening library. ( 0,597598310367555 )
J Chem Inf Model - Freely available conformer generation methods: how good are they? ( 0,597074192476612 )
J Chem Inf Model - Exploring uncharted territories: predicting activity cliffs in structure-activity landscapes. ( 0,59439223602776 )
J Chem Inf Model - Classification of compounds with distinct or overlapping multi-target activities and diverse molecular mechanisms using emerging chemical patterns. ( 0,59318771308481 )
J Chem Inf Model - Polypharmacology directed compound data mining: identification of promiscuous chemotypes with different activity profiles and comparison to approved drugs. ( 0,591895401582626 )
Methods Inf Med - Automated classification of free-text pathology reports for registration of incident cases of cancer. ( 0,591639723497251 )
J Chem Inf Model - Synthesis, bioassay, and molecular field topology analysis of diverse vasodilatory heterocycles. ( 0,591109722246895 )
J Chem Inf Model - Kinase-kernel models: accurate in silico screening of 4 million compounds across the entire human kinome. ( 0,590854562978335 )
J Chem Inf Model - Mining chemical reactions using neighborhood behavior and condensed graphs of reactions approaches. ( 0,590338951865491 )
J Chem Inf Model - Characterizing the diversity and biological relevance of the MLPCN assay manifold and screening set. ( 0,590197198906617 )
J Chem Inf Model - Identification of novel liver X receptor activators by structure-based modeling. ( 0,589644925312143 )
J Chem Inf Model - Jointly handling potency and toxicity of antimicrobial peptidomimetics by simple rules from desirability theory and chemoinformatics. ( 0,588845369109151 )
Comput Methods Programs Biomed - Drug/nondrug classification using Support Vector Machines with various feature selection strategies. ( 0,587789500728149 )
J Chem Inf Model - Prediction of activity cliffs using support vector machines. ( 0,586551382774677 )
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. ( 0,585389547670395 )
J Chem Inf Model - Predictive models for cytochrome p450 isozymes based on quantitative high throughput screening data. ( 0,58535937642832 )
J Chem Inf Model - GA(M)E-QSAR: a novel, fully automatic genetic-algorithm-(meta)-ensembles approach for binary classification in ligand-based drug design. ( 0,585183046282704 )
J Chem Inf Model - Design and synthesis of new antioxidants predicted by the model developed on a set of pulvinic acid derivatives. ( 0,584026465931778 )
J Chem Inf Model - Application of computer-aided drug repurposing in the search of new cruzipain inhibitors: discovery of amiodarone and bromocriptine inhibitory effects. ( 0,583200534915293 )
J Chem Inf Model - Accurate atom-mapping computation for biochemical reactions. ( 0,583094620505257 )
J Chem Inf Model - Discovery of novel antimalarial compounds enabled by QSAR-based virtual screening. ( 0,581856138187046 )
J Chem Inf Model - Identification of 1,2,5-oxadiazoles as a new class of SENP2 inhibitors using structure based virtual screening. ( 0,581217890708188 )
J Chem Inf Model - ColBioS-FlavRC: a collection of bioselective flavonoids and related compounds filtered from high-throughput screening outcomes. ( 0,581103403550691 )
J Chem Inf Model - LiCABEDS II. Modeling of ligand selectivity for G-protein-coupled cannabinoid receptors. ( 0,581072065809763 )
J Chem Inf Model - Atom pair 2D-fingerprints perceive 3D-molecular shape and pharmacophores for very fast virtual screening of ZINC and GDB-17. ( 0,580614267648324 )
J Chem Inf Model - Molecular modeling of potential anticancer agents from African medicinal plants. ( 0,578570262588618 )
J Chem Inf Model - Rapid scanning structure-activity relationships in combinatorial data sets: identification of activity switches. ( 0,577744779519973 )
J Chem Inf Model - Identification of multitarget activity ridges in high-dimensional bioactivity spaces. ( 0,576870561796687 )
J Chem Inf Model - Virtual fragment screening: discovery of histamine H3 receptor ligands using ligand-based and protein-based molecular fingerprints. ( 0,576629949860746 )
J Chem Inf Model - Discovering new agents active against methicillin-resistant Staphylococcus aureus with ligand-based approaches. ( 0,576572311457931 )
J Chem Inf Model - Discovery of novel checkpoint kinase 1 inhibitors by virtual screening based on multiple crystal structures. ( 0,576213571948238 )
J Chem Inf Model - Identification of a novel inhibitor of dengue virus protease through use of a virtual screening drug discovery Web portal. ( 0,57595645278456 )
J Chem Inf Model - Rationalizing the role of SAR tolerance for ligand-based virtual screening. ( 0,574370724462228 )
J Chem Inf Model - Enrichment of chemical libraries docked to protein conformational ensembles and application to aldehyde dehydrogenase 2. ( 0,573646169436532 )
Artif Intell Med - Resolution of redundant semantic type assignments for organic chemicals in the UMLS. ( 0,573295424051703 )
J Chem Inf Model - Discovery of new selective human aldose reductase inhibitors through virtual screening multiple binding pocket conformations. ( 0,573187805282574 )
J Chem Inf Model - Target-independent prediction of drug synergies using only drug lipophilicity. ( 0,572959288547016 )
J Chem Inf Model - Validation of the AmpC ?-lactamase binding site and identification of inhibitors with novel scaffolds. ( 0,572635337071688 )
J Chem Inf Model - Design of novel rho kinase inhibitors using energy based pharmacophore modeling, shape-based screening, in silico virtual screening, and biological evaluation. ( 0,571475060709266 )
J Chem Inf Model - Prediction of new bioactive molecules using a Bayesian belief network. ( 0,571283676651666 )
J Chem Inf Model - New fragment weighting scheme for the Bayesian inference network in ligand-based virtual screening. ( 0,571248877909566 )
J Chem Inf Model - Contribution of 2D and 3D structural features of drug molecules in the prediction of Drug Profile Matching. ( 0,570955319199839 )
J Chem Inf Model - Identification of sumoylation activating enzyme 1 inhibitors by structure-based virtual screening. ( 0,570870095658422 )
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. ( 0,570327386974941 )
J Chem Inf Model - Prediction of individual compounds forming activity cliffs using emerging chemical patterns. ( 0,569601274602565 )
J Chem Inf Model - Identification of compounds with potential antibacterial activity against Mycobacterium through structure-based drug screening. ( 0,569196516687905 )
J Chem Inf Model - Visual characterization and diversity quantification of chemical libraries: 1. creation of delimited reference chemical subspaces. ( 0,568549083079902 )
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. ( 0,567830945062515 )
J Chem Inf Model - Discovery of a novel selective kappa-opioid receptor agonist using crystal structure-based virtual screening. ( 0,567818752790141 )
J Chem Inf Model - Coping with unbalanced class data sets in oral absorption models. ( 0,567617372124758 )
Curr Comput Aided Drug Des - Development of Chemical Compound Libraries for In Silico Drug Screening. ( 0,567349276575574 )