J Chem Inf Model - Structure based model for the prediction of phospholipidosis induction potential of small molecules.

Tópicos

{ compound(1573) activ(1297) structur(1058) }
{ featur(3375) classif(2383) classifi(1994) }
{ model(2656) set(1616) predict(1553) }
{ cost(1906) reduc(1198) effect(832) }
{ model(2341) predict(2261) use(1141) }
{ perform(1367) use(1326) method(1137) }
{ data(1714) softwar(1251) tool(1186) }
{ estim(2440) model(1874) function(577) }
{ model(2220) cell(1177) simul(1124) }
{ howev(809) still(633) remain(590) }
{ data(3008) multipl(1320) sourc(1022) }
{ high(1669) rate(1365) level(1280) }
{ method(1219) similar(1157) match(930) }
{ error(1145) method(1030) estim(1020) }
{ blood(1257) pressur(1144) flow(957) }
{ research(1218) medic(880) student(794) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ detect(2391) sensit(1101) algorithm(908) }
{ can(774) often(719) complex(702) }
{ network(2748) neural(1063) input(814) }
{ imag(2675) segment(2577) method(1081) }
{ studi(2440) review(1878) systemat(933) }
{ extract(1171) text(1153) clinic(932) }
{ control(1307) perform(991) simul(935) }
{ research(1085) discuss(1038) issu(1018) }
{ visual(1396) interact(850) tool(830) }
{ ehr(2073) health(1662) electron(1139) }
{ signal(2180) analysi(812) frequenc(800) }
{ group(2977) signific(1463) compar(1072) }
{ gene(2352) biolog(1181) express(1162) }
{ activ(1138) subject(705) human(624) }
{ patient(1821) servic(1111) care(1106) }
{ structur(1116) can(940) graph(676) }
{ cancer(2502) breast(956) screen(824) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ decis(3086) make(1611) patient(1517) }
{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }
{ method(2212) result(1239) propos(1039) }
{ 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) }
{ imag(1057) registr(996) error(939) }
{ bind(1733) structur(1185) ligand(1036) }
{ sequenc(1873) structur(1644) protein(1328) }
{ imag(2830) propos(1344) filter(1198) }
{ 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) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ framework(1458) process(801) describ(734) }
{ problem(2511) optim(1539) algorithm(950) }
{ chang(1828) time(1643) increas(1301) }
{ learn(2355) train(1041) set(1003) }
{ concept(1167) ontolog(924) domain(897) }
{ clinic(1479) use(1117) guidelin(835) }
{ algorithm(1844) comput(1787) effici(935) }
{ method(1557) propos(1049) approach(1037) }
{ design(1359) user(1324) use(1319) }
{ care(1570) inform(1187) nurs(1089) }
{ general(901) number(790) one(736) }
{ 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) }
{ 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) }
{ studi(1119) effect(1106) posit(819) }
{ 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) }
{ state(1844) use(1261) util(961) }
{ patient(2837) hospit(1953) medic(668) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ sampl(1606) size(1419) use(1276) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ time(1939) patient(1703) rate(768) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
{ use(976) code(926) identifi(902) }
{ result(1111) use(1088) new(759) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ process(1125) use(805) approach(778) }

Resumo

Drug-induced phospholipidosis (PLD), characterized by an intracellular accumulation of phospholipids and formation of concentric lamellar bodies, has raised concerns in the drug discovery community, due to its potential adverse effects. To evaluate the PLD induction potential, 4,161 nonredundant drug-like molecules from the National Institutes of Health Chemical Genomics Center (NCGC) Pharmaceutical Collection (NPC), the Library of Pharmacologically Active Compounds (LOPAC), and the Tocris Biosciences collection were screened in a quantitative high-throughput screening (qHTS) format. The potential of drug-lipid complex formation can be linked directly to the structures of drug molecules, and many PLD inducing drugs were found to share common structural features. Support vector machine (SVM) models were constructed by using customized atom types or Molecular Operating Environment (MOE) 2D descriptors as structural descriptors. Either the compounds from LOPAC or randomly selected from the entire data set were used as the training set. The impact of training data with biased structural features and the impact of molecule descriptors emphasizing whole-molecule properties or detailed functional groups at the atom level on model performance were analyzed and discussed. Rebalancing strategies were applied to improve the predictive power of the SVM models. Using the undersampling method, the consensus model using one-third of the compounds randomly selected from the data set as the training set achieved high accuracy of 0.90 in predicting the remaining two-thirds of the compounds constituting the test set, as measured by the area under the receiver operator characteristic curve (AUC-ROC).

Resumo Limpo

druginduc phospholipidosi pld character intracellular accumul phospholipid format concentr lamellar bodi rais concern drug discoveri communiti due potenti advers effect evalu pld induct potenti nonredund druglik molecul nation institut health chemic genom center ncgc pharmaceut collect npc librari pharmacolog activ compound lopac tocri bioscienc collect screen quantit highthroughput screen qhts format potenti druglipid complex format can link direct structur drug molecul mani pld induc drug found share common structur featur support vector machin svm model construct use custom atom type molecular oper environ moe d descriptor structur descriptor either compound lopac random select entir data set use train set impact train data bias structur featur impact molecul descriptor emphas wholemolecul properti detail function group atom level model perform analyz discuss rebalanc strategi appli improv predict power svm model use undersampl method consensus model use onethird compound random select data set train set achiev high accuraci predict remain twothird compound constitut test set measur area receiv oper characterist curv aucroc

Resumos Similares

J Chem Inf Model - QSAR classification model for antibacterial compounds and its use in virtual screening. ( 0,905727961824345 )
J Chem Inf Model - Compound set enrichment: a novel approach to analysis of primary HTS data. ( 0,896091152999984 )
J Chem Inf Model - Combining horizontal and vertical substructure relationships in scaffold hierarchies for activity prediction. ( 0,865784033146203 )
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. ( 0,860305931578214 )
J Chem Inf Model - In silico enzymatic synthesis of a 400,000 compound biochemical database for nontargeted metabolomics. ( 0,855720193176379 )
J Chem Inf Model - Predictions of BuChE inhibitors using support vector machine and naive Bayesian classification techniques in drug discovery. ( 0,855667189537832 )
J Am Med Inform Assoc - Drug repurposing: mining protozoan proteomes for targets of known bioactive compounds. ( 0,854787869817154 )
J Chem Inf Model - A new protocol for predicting novel GSK-3? ATP competitive inhibitors. ( 0,849284760358259 )
J Chem Inf Model - TIN-a combinatorial compound collection of synthetically feasible multicomponent synthesis products. ( 0,847793913799185 )
J Chem Inf Model - Target-independent prediction of drug synergies using only drug lipophilicity. ( 0,843562143055138 )
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. ( 0,837157227519369 )
J Chem Inf Model - Polypharmacology directed compound data mining: identification of promiscuous chemotypes with different activity profiles and comparison to approved drugs. ( 0,834578677350403 )
J Chem Inf Model - Identification of novel malarial cysteine protease inhibitors using structure-based virtual screening of a focused cysteine protease inhibitor library. ( 0,833546734955186 )
J Chem Inf Model - Identification of a novel inhibitor of dengue virus protease through use of a virtual screening drug discovery Web portal. ( 0,833280254829297 )
J Chem Inf Model - Visual characterization and diversity quantification of chemical libraries: 1. creation of delimited reference chemical subspaces. ( 0,831036656153704 )
J Chem Inf Model - Profile-QSAR and Surrogate AutoShim protein-family modeling of proteases. ( 0,829840722943055 )
J Chem Inf Model - Ligand- and structure-based virtual screening for clathrodin-derived human voltage-gated sodium channel modulators. ( 0,82910042913658 )
J Chem Inf Model - Automated recycling of chemistry for virtual screening and library design. ( 0,828455440064197 )
J Chem Inf Model - Fighting high molecular weight in bioactive molecules with sub-pharmacophore-based virtual screening. ( 0,828213324934406 )
J Chem Inf Model - A multivariate chemical similarity approach to search for drugs of potential environmental concern. ( 0,825131086421164 )
J Chem Inf Model - Increasing the coverage of medicinal chemistry-relevant space in commercial fragments screening. ( 0,82481124437666 )
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. ( 0,824686012908325 )
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. ( 0,823992114115724 )
J Chem Inf Model - How diverse are diversity assessment methods? A comparative analysis and benchmarking of molecular descriptor space. ( 0,82303334344396 )
J Chem Inf Model - Identification of 1,2,5-oxadiazoles as a new class of SENP2 inhibitors using structure based virtual screening. ( 0,822415223680395 )
J Chem Inf Model - Mining the ChEMBL database: an efficient chemoinformatics workflow for assembling an ion channel-focused screening library. ( 0,820375136700749 )
J Chem Inf Model - Construction and use of fragment-augmented molecular Hasse diagrams. ( 0,820299304463946 )
J Chem Inf Model - Compound optimization through data set-dependent chemical transformations. ( 0,82023563497981 )
J Chem Inf Model - Conditional probabilistic analysis for prediction of the activity landscape and relative compound activities. ( 0,818866084656037 )
J Chem Inf Model - Discovery of novel antimalarial compounds enabled by QSAR-based virtual screening. ( 0,817762639502133 )
J Chem Inf Model - Application of computer-aided drug repurposing in the search of new cruzipain inhibitors: discovery of amiodarone and bromocriptine inhibitory effects. ( 0,817668111283222 )
J Chem Inf Model - G-protein coupled receptors virtual screening using genetic algorithm focused chemical space. ( 0,817533907743548 )
J Chem Inf Model - Discovery of a7-nicotinic receptor ligands by virtual screening of the chemical universe database GDB-13. ( 0,816706528070844 )
J Chem Inf Model - Natural product-like virtual libraries: recursive atom-based enumeration. ( 0,816181347598011 )
Curr Comput Aided Drug Des - Development of Chemical Compound Libraries for In Silico Drug Screening. ( 0,815212563916844 )
J Chem Inf Model - Identification of multitarget activity ridges in high-dimensional bioactivity spaces. ( 0,815002291300884 )
J Chem Inf Model - Design of multitarget activity landscapes that capture hierarchical activity cliff distributions. ( 0,814949088576001 )
J Chem Inf Model - Capturing structure-activity relationships from chemogenomic spaces. ( 0,814741857459927 )
J Chem Inf Model - Identification of novel serotonin transporter compounds by virtual screening. ( 0,814514117376595 )
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. ( 0,813458767061687 )
J Chem Inf Model - De novo design of drug-like molecules by a fragment-based molecular evolutionary approach. ( 0,813139048286058 )
J Chem Inf Model - Similarity boosted quantitative structure-activity relationship--a systematic study of enhancing structural descriptors by molecular similarity. ( 0,812360046172192 )
J Chem Inf Model - From activity cliffs to activity ridges: informative data structures for SAR analysis. ( 0,812252460478353 )
J Chem Inf Model - Hsp90 inhibitors, part 2: combining ligand-based and structure-based approaches for virtual screening application. ( 0,810690404511091 )
J Chem Inf Model - Identifying compound-target associations by combining bioactivity profile similarity search and public databases mining. ( 0,810547605903677 )
J Chem Inf Model - Identification of novel liver X receptor activators by structure-based modeling. ( 0,809959707326783 )
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. ( 0,809268852089446 )
J Chem Inf Model - How do 2D fingerprints detect structurally diverse active compounds? Revealing compound subset-specific fingerprint features through systematic selection. ( 0,805971744341521 )
J Chem Inf Model - ColBioS-FlavRC: a collection of bioselective flavonoids and related compounds filtered from high-throughput screening outcomes. ( 0,805900326561613 )
J Chem Inf Model - Selection of in silico drug screening results for G-protein-coupled receptors by using universal active probes. ( 0,805634583212657 )
J Chem Inf Model - Discovering new agents active against methicillin-resistant Staphylococcus aureus with ligand-based approaches. ( 0,80411183574661 )
J Chem Inf Model - Design of a three-dimensional multitarget activity landscape. ( 0,803351602215523 )
J Chem Inf Model - Introduction of target cliffs as a concept to identify and describe complex molecular selectivity patterns. ( 0,80247237937897 )
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. ( 0,801141318561436 )
J Chem Inf Model - Prediction of individual compounds forming activity cliffs using emerging chemical patterns. ( 0,800328773867915 )
J Chem Inf Model - Neighborhood-based prediction of novel active compounds from SAR matrices. ( 0,799460142912336 )
J Chem Inf Model - Coping with unbalanced class data sets in oral absorption models. ( 0,7989231241081 )
J Chem Inf Model - BioSM: metabolomics tool for identifying endogenous mammalian biochemical structures in chemical structure space. ( 0,798471854363192 )
J Chem Inf Model - Multitarget structure-activity relationships characterized by activity-difference maps and consensus similarity measure. ( 0,797617927044225 )
J Chem Inf Model - Identification of a new class of FtsZ inhibitors by structure-based design and in vitro screening. ( 0,797611671806074 )
J Chem Inf Model - Searching for recursively defined generic chemical patterns in nonenumerated fragment spaces. ( 0,796769258772838 )
J Chem Inf Model - Novel method for pharmacophore analysis by examining the joint pharmacophore space. ( 0,795844354080655 )
J Chem Inf Model - Novel mycosin protease MycP1 inhibitors identified by virtual screening and 4D fingerprints. ( 0,794118928905168 )
J Chem Inf Model - Structural similarity based kriging for quantitative structure activity and property relationship modeling. ( 0,794040150472351 )
J Chem Inf Model - Navigating high-dimensional activity landscapes: design and application of the ligand-target differentiation map. ( 0,793852732933582 )
J Chem Inf Model - Atom pair 2D-fingerprints perceive 3D-molecular shape and pharmacophores for very fast virtual screening of ZINC and GDB-17. ( 0,791990141449745 )
J Chem Inf Model - Discovery of novel histamine H4 and serotonin transporter ligands using the topological feature tree descriptor. ( 0,791232940814873 )
J Chem Inf Model - Prediction of new bioactive molecules using a Bayesian belief network. ( 0,789664919548475 )
J Chem Inf Model - Identification of compounds with potential antibacterial activity against Mycobacterium through structure-based drug screening. ( 0,78930242269227 )
J Chem Inf Model - Discovery of chemical compound groups with common structures by a network analysis approach (affinity prediction method). ( 0,789091365119809 )
J Chem Inf Model - Identification of sumoylation activating enzyme 1 inhibitors by structure-based virtual screening. ( 0,788742842552263 )
J Chem Inf Model - Visualization and virtual screening of the chemical universe database GDB-17. ( 0,788662404019888 )
J Chem Inf Model - Enumeration of 166 billion organic small molecules in the chemical universe database GDB-17. ( 0,788457082310003 )
J Integr Bioinform - Database supported candidate search for metabolite identification. ( 0,787763160852456 )
J Chem Inf Model - Chemical data visualization and analysis with incremental generative topographic mapping: big data challenge. ( 0,786782387008504 )
J Chem Inf Model - Enrichment of chemical libraries docked to protein conformational ensembles and application to aldehyde dehydrogenase 2. ( 0,785980085633015 )
J Chem Inf Model - Discovery of new selective human aldose reductase inhibitors through virtual screening multiple binding pocket conformations. ( 0,78570986928269 )
J Chem Inf Model - Scaffold diversity of exemplified medicinal chemistry space. ( 0,784047138597812 )
J Chem Inf Model - Molecular topology analysis of the differences between drugs, clinical candidate compounds, and bioactive molecules. ( 0,783988935879596 )
J Chem Inf Model - Systematic assessment of compound series with SAR transfer potential. ( 0,783837243438999 )
J Chem Inf Model - A searchable map of PubChem. ( 0,782457585306228 )
J Chem Inf Model - Optimization of molecular representativeness. ( 0,782340734950798 )
J Chem Inf Model - Feasibility of using molecular docking-based virtual screening for searching dual target kinase inhibitors. ( 0,782329552151441 )
J Chem Inf Model - Freely available conformer generation methods: how good are they? ( 0,781937830650253 )
J Chem Inf Model - Hsp90 inhibitors, part 1: definition of 3-D QSAutogrid/R models as a tool for virtual screening. ( 0,781653558800226 )
J Chem Inf Model - Exploring uncharted territories: predicting activity cliffs in structure-activity landscapes. ( 0,781443877571558 )
J Chem Inf Model - Jointly handling potency and toxicity of antimicrobial peptidomimetics by simple rules from desirability theory and chemoinformatics. ( 0,780128224160881 )
J Chem Inf Model - An integrated virtual screening approach for VEGFR-2 inhibitors. ( 0,779233806101729 )
J Chem Inf Model - Virtual screening yields inhibitors of novel antifungal drug target, benzoate 4-monooxygenase. ( 0,778623533894922 )
J Chem Inf Model - Scanning structure-activity relationships with structure-activity similarity and related maps: from consensus activity cliffs to selectivity switches. ( 0,775957567598349 )
J Chem Inf Model - Prediction of activity cliffs using support vector machines. ( 0,775784097422022 )
J Chem Inf Model - Characterizing the diversity and biological relevance of the MLPCN assay manifold and screening set. ( 0,775344095190773 )
J Chem Inf Model - Harvesting classification trees for drug discovery. ( 0,77493184782299 )
Comput Biol Chem - The optimization of running time for a maximum common substructure-based algorithm and its application in drug design. ( 0,772586030796347 )
J Chem Inf Model - Exploring the biologically relevant chemical space for drug discovery. ( 0,77233706788518 )
J Chem Inf Model - Knowledge-based libraries for predicting the geometric preferences of druglike molecules. ( 0,772291359329422 )
J Chem Inf Model - Fragment-based lead discovery and design. ( 0,77048398418666 )
J Chem Inf Model - Rationalizing the role of SAR tolerance for ligand-based virtual screening. ( 0,769552176180422 )
J Chem Inf Model - Identification of novel potential antibiotics against Staphylococcus using structure-based drug screening targeting dihydrofolate reductase. ( 0,768920722662637 )
J Chem Inf Model - Application of support vector machine to three-dimensional shape-based virtual screening using comprehensive three-dimensional molecular shape overlay with known inhibitors. ( 0,768146722464195 )