J Chem Inf Model - A Bayesian approach to in silico blood-brain barrier penetration modeling.


{ compound(1573) activ(1297) structur(1058) }
{ result(1111) use(1088) new(759) }
{ model(2656) set(1616) predict(1553) }
{ group(2977) signific(1463) compar(1072) }
{ use(2086) technolog(871) perceiv(783) }
{ assess(1506) score(1403) qualiti(1306) }
{ learn(2355) train(1041) set(1003) }
{ data(3008) multipl(1320) sourc(1022) }
{ take(945) account(800) differ(722) }
{ control(1307) perform(991) simul(935) }
{ research(1085) discuss(1038) issu(1018) }
{ model(3480) simul(1196) paramet(876) }
{ estim(2440) model(1874) function(577) }
{ studi(1119) effect(1106) posit(819) }
{ howev(809) still(633) remain(590) }
{ perform(999) metric(946) measur(919) }
{ analysi(2126) use(1163) compon(1037) }
{ can(774) often(719) complex(702) }
{ system(1976) rule(880) can(841) }
{ method(1219) similar(1157) match(930) }
{ clinic(1479) use(1117) guidelin(835) }
{ system(1050) medic(1026) inform(1018) }
{ model(2341) predict(2261) use(1141) }
{ visual(1396) interact(850) tool(830) }
{ can(981) present(881) function(850) }
{ health(1844) social(1437) communiti(874) }
{ cancer(2502) breast(956) screen(824) }
{ implement(1333) system(1263) develop(1122) }
{ model(3404) distribut(989) bayesian(671) }
{ data(1737) use(1416) pattern(1282) }
{ bind(1733) structur(1185) ligand(1036) }
{ featur(3375) classif(2383) classifi(1994) }
{ network(2748) neural(1063) input(814) }
{ motion(1329) object(1292) video(1091) }
{ problem(2511) optim(1539) algorithm(950) }
{ data(1714) softwar(1251) tool(1186) }
{ monitor(1329) mobil(1314) devic(1160) }
{ patient(2837) hospit(1953) medic(668) }
{ signal(2180) analysi(812) frequenc(800) }
{ gene(2352) biolog(1181) express(1162) }
{ first(2504) two(1366) second(1323) }
{ activ(1138) subject(705) human(624) }
{ structur(1116) can(940) graph(676) }
{ activ(1452) weight(1219) physic(1104) }
{ method(2212) result(1239) propos(1039) }
{ imag(1947) propos(1133) code(1026) }
{ inform(2794) health(2639) internet(1427) }
{ measur(2081) correl(1212) valu(896) }
{ imag(1057) registr(996) error(939) }
{ sequenc(1873) structur(1644) protein(1328) }
{ imag(2830) propos(1344) filter(1198) }
{ imag(2675) segment(2577) method(1081) }
{ patient(2315) diseas(1263) diabet(1191) }
{ studi(2440) review(1878) systemat(933) }
{ treatment(1704) effect(941) patient(846) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ framework(1458) process(801) describ(734) }
{ error(1145) method(1030) estim(1020) }
{ chang(1828) time(1643) increas(1301) }
{ concept(1167) ontolog(924) domain(897) }
{ algorithm(1844) comput(1787) effici(935) }
{ extract(1171) text(1153) clinic(932) }
{ method(1557) propos(1049) approach(1037) }
{ design(1359) user(1324) use(1319) }
{ 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) }
{ 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) }
{ import(1318) role(1303) understand(862) }
{ 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) }
{ ehr(2073) health(1662) electron(1139) }
{ state(1844) use(1261) util(961) }
{ research(1218) medic(880) student(794) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ cost(1906) reduc(1198) effect(832) }
{ sampl(1606) size(1419) use(1276) }
{ intervent(3218) particip(2042) group(1664) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ high(1669) rate(1365) level(1280) }
{ use(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ survey(1388) particip(1329) question(1065) }
{ decis(3086) make(1611) patient(1517) }
{ process(1125) use(805) approach(778) }
{ method(1969) cluster(1462) data(1082) }
{ detect(2391) sensit(1101) algorithm(908) }


The human blood-brain barrier (BBB) is a membrane that protects the central nervous system (CNS) by restricting the passage of solutes. The development of any new drug must take into account its existence whether for designing new molecules that target components of the CNS or, on the other hand, to find new substances that should not penetrate the barrier. Several studies in the literature have attempted to predict BBB penetration, so far with limited success and few, if any, application to real world drug discovery and development programs. Part of the reason is due to the fact that only about 2% of small molecules can cross the BBB, and the available data sets are not representative of that reality, being generally biased with an over-representation of molecules that show an ability to permeate the BBB (BBB positives). To circumvent this limitation, the current study aims to devise and use a new approach based on Bayesian statistics, coupled with state-of-the-art machine learning methods to produce a robust model capable of being applied in real-world drug research scenarios. The data set used, gathered from the literature, totals 1970 curated molecules, one of the largest for similar studies. Random Forests and Support Vector Machines were tested in various configurations against several chemical descriptor set combinations. Models were tested in a 5-fold cross-validation process, and the best one tested over an independent validation set. The best fitted model produced an overall accuracy of 95%, with a mean square contingency coefficient () of 0.74, and showing an overall capacity for predicting BBB positives of 83% and 96% for determining BBB negatives. This model was adapted into a Web based tool made available for the whole community at http://b3pp.lasige.di.fc.ul.pt.

Resumo Limpo

human bloodbrain barrier bbb membran protect central nervous system cns restrict passag solut develop new drug must take account exist whether design new molecul target compon cns hand find new substanc penetr barrier sever studi literatur attempt predict bbb penetr far limit success applic real world drug discoveri develop program part reason due fact small molecul can cross bbb avail data set repres realiti general bias overrepresent molecul show abil permeat bbb bbb posit circumv limit current studi aim devis use new approach base bayesian statist coupl stateoftheart machin learn method produc robust model capabl appli realworld drug research scenario data set use gather literatur total curat molecul one largest similar studi random forest support vector machin test various configur sever chemic descriptor set combin model test fold crossvalid process best one test independ valid set best fit model produc overal accuraci mean squar conting coeffici show overal capac predict bbb posit determin bbb negat model adapt web base tool made avail whole communiti httpbpplasigedifculpt

Resumos Similares

J Chem Inf Model - An integrated virtual screening approach for VEGFR-2 inhibitors. ( 0,655497754069259 )
J Chem Inf Model - Profile-QSAR and Surrogate AutoShim protein-family modeling of proteases. ( 0,640003234563148 )
J Chem Inf Model - QSAR modeling of imbalanced high-throughput screening data in PubChem. ( 0,638111740699917 )
J Chem Inf Model - Prediction of new bioactive molecules using a Bayesian belief network. ( 0,63751137100251 )
J Chem Inf Model - On the value of homology models for virtual screening: discovering hCXCR3 antagonists by pharmacophore-based and structure-based approaches. ( 0,634155698235639 )
J Chem Inf Model - Evaluation and optimization of virtual screening workflows with DEKOIS 2.0--a public library of challenging docking benchmark sets. ( 0,629378112287496 )
J Chem Inf Model - Predictions of BuChE inhibitors using support vector machine and naive Bayesian classification techniques in drug discovery. ( 0,623558540754891 )
J Chem Inf Model - Discovery of novel antimalarial compounds enabled by QSAR-based virtual screening. ( 0,620182600905863 )
J Chem Inf Model - Discovering new agents active against methicillin-resistant Staphylococcus aureus with ligand-based approaches. ( 0,617881095026245 )
J Chem Inf Model - Quantitative structure-activity relationship models for ready biodegradability of chemicals. ( 0,617228457259687 )
J Chem Inf Model - Four-dimensional structure-activity relationship model to predict HIV-1 integrase strand transfer inhibition using LQTA-QSAR methodology. ( 0,607659904977572 )
J Chem Inf Model - Design and synthesis of new antioxidants predicted by the model developed on a set of pulvinic acid derivatives. ( 0,607620112217442 )
J Chem Inf Model - Dual histamine H3R/serotonin 5-HT4R ligands with antiamnesic properties: pharmacophore-based virtual screening and polypharmacology. ( 0,607396298294581 )
J Chem Inf Model - Systematic identification of scaffolds representing compounds active against individual targets and single or multiple target families. ( 0,605228053989507 )
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. ( 0,604866935081646 )
J Chem Inf Model - How accurately can we predict the melting points of drug-like compounds? ( 0,602627172748618 )
J Chem Inf Model - Analysis and study of molecule data sets using snowflake diagrams of weighted maximum common subgraph trees. ( 0,602285492682934 )
J Chem Inf Model - Applicability Domain ANalysis (ADAN): a robust method for assessing the reliability of drug property predictions. ( 0,598878382085188 )
J Chem Inf Model - Maximum-score diversity selection for early drug discovery. ( 0,598657579931082 )
J Chem Inf Model - Oversampling to overcome overfitting: exploring the relationship between data set composition, molecular descriptors, and predictive modeling methods. ( 0,596238029383002 )
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. ( 0,595125748497719 )
J Chem Inf Model - New Group IV chemical motifs for improved dielectric permittivity of polyethylene. ( 0,59257058157656 )
J Chem Inf Model - Prediction of aquatic toxicity mode of action using linear discriminant and random forest models. ( 0,589886398087796 )
J Chem Inf Model - Prediction of compound potency changes in matched molecular pairs using support vector regression. ( 0,588403047182549 )
J Chem Inf Model - Kernel-based partial least squares: application to fingerprint-based QSAR with model visualization. ( 0,58779104268564 )
J Am Med Inform Assoc - Drug repurposing: mining protozoan proteomes for targets of known bioactive compounds. ( 0,586818925601756 )
J Chem Inf Model - BioSM: metabolomics tool for identifying endogenous mammalian biochemical structures in chemical structure space. ( 0,586287090000982 )
J Chem Inf Model - Revisiting the general solubility equation: in silico prediction of aqueous solubility incorporating the effect of topographical polar surface area. ( 0,585766970766769 )
Comput. Biol. Med. - Cholesteryl ester transfer protein inhibitors in coronary heart disease: Validated comparative QSAR modeling of N, N-disubstituted trifluoro-3-amino-2-propanols. ( 0,58531790150391 )
Comput. Biol. Med. - Three dimensional quantitative structure-toxicity relationship modeling and prediction of acute toxicity for organic contaminants to algae. ( 0,585136529608051 )
J Chem Inf Model - Classification of compounds with distinct or overlapping multi-target activities and diverse molecular mechanisms using emerging chemical patterns. ( 0,584380337523052 )
J Chem Inf Model - Application of the 4D fingerprint method with a robust scoring function for scaffold-hopping and drug repurposing strategies. ( 0,584123124950957 )
J Chem Inf Model - A new protocol for predicting novel GSK-3? ATP competitive inhibitors. ( 0,58407667377862 )
J Chem Inf Model - Hsp90 inhibitors, part 1: definition of 3-D QSAutogrid/R models as a tool for virtual screening. ( 0,584051572187595 )
J Chem Inf Model - Exploring uncharted territories: predicting activity cliffs in structure-activity landscapes. ( 0,582594006822813 )
J Chem Inf Model - In silico prediction of total human plasma clearance. ( 0,580377091382121 )
J Chem Inf Model - Structure based model for the prediction of phospholipidosis induction potential of small molecules. ( 0,579677064223793 )
J Chem Inf Model - ColBioS-FlavRC: a collection of bioselective flavonoids and related compounds filtered from high-throughput screening outcomes. ( 0,579645105785945 )
J Chem Inf Model - The valence state combination model: a generic framework for handling tautomers and protonation states. ( 0,578727109532358 )
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. ( 0,575969819792972 )
J Chem Inf Model - Normalizing molecular docking rankings using virtually generated decoys. ( 0,573231442998529 )
J Chem Inf Model - A searchable map of PubChem. ( 0,5726372397548 )
Brief. Bioinformatics - Toward more realistic drug-target interaction predictions. ( 0,571651242321545 )
J Chem Inf Model - Reranking docking poses using molecular simulations and approximate free energy methods. ( 0,570650101023762 )
J Chem Inf Model - Prediction of activity cliffs using support vector machines. ( 0,56980472996925 )
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. ( 0,569346132887523 )
J Chem Inf Model - Molecular modeling of the 3D structure of 5-HT(1A)R: discovery of novel 5-HT(1A)R agonists via dynamic pharmacophore-based virtual screening. ( 0,567467092454136 )
J Chem Inf Model - Novel method for pharmacophore analysis by examining the joint pharmacophore space. ( 0,567243678731212 )
J Chem Inf Model - Introduction of a methodology for visualization and graphical interpretation of Bayesian classification models. ( 0,566533211749318 )
J Chem Inf Model - Identification of novel androgen receptor antagonists using structure- and ligand-based methods. ( 0,565254749859043 )
J Chem Inf Model - De novo design of drug-like molecules by a fragment-based molecular evolutionary approach. ( 0,564929792242109 )
J Chem Inf Model - Freely available conformer generation methods: how good are they? ( 0,564601250025562 )
J Chem Inf Model - Compound set enrichment: a novel approach to analysis of primary HTS data. ( 0,563046311334689 )
J Chem Inf Model - Dependence of QSAR models on the selection of trial descriptor sets: a demonstration using nanotoxicity endpoints of decorated nanotubes. ( 0,563004991625336 )
J Chem Inf Model - Automatic tailoring and transplanting: a practical method that makes virtual screening more useful. ( 0,562724562569989 )
J Chem Inf Model - Statistical analysis and compound selection of combinatorial libraries for soluble epoxide hydrolase. ( 0,562320901676383 )
J Chem Inf Model - A comparison of different QSAR approaches to modeling CYP450 1A2 inhibition. ( 0,561621110232178 )
J Chem Inf Model - A new approach to radial basis function approximation and its application to QSAR. ( 0,561003634858888 )
J Chem Inf Model - Hsp90 inhibitors, part 2: combining ligand-based and structure-based approaches for virtual screening application. ( 0,560383809639402 )
J Chem Inf Model - Synthesis, bioassay, and molecular field topology analysis of diverse vasodilatory heterocycles. ( 0,5602678063521 )
J Chem Inf Model - Identification of a new class of FtsZ inhibitors by structure-based design and in vitro screening. ( 0,559041872192292 )
J Chem Inf Model - Visual characterization and diversity quantification of chemical libraries: 2. Analysis and selection of size-independent, subspace-specific diversity indices. ( 0,558921583088593 )
BMC Med Inform Decis Mak - Measuring preferences for analgesic treatment for cancer pain: how do African-Americans and Whites perform on choice-based conjoint (CBC) analysis experiments? ( 0,557233378428087 )
J Chem Inf Model - Target-independent prediction of drug synergies using only drug lipophilicity. ( 0,556833159373438 )
J Chem Inf Model - QSAR classification model for antibacterial compounds and its use in virtual screening. ( 0,555313065579596 )
J Chem Inf Model - DrugLogit: logistic discrimination between drugs and nondrugs including disease-specificity by assigning probabilities based on molecular properties. ( 0,55454243497699 )
J Chem Inf Model - Computational prediction and validation of an expert's evaluation of chemical probes. ( 0,554165069199921 )
J Chem Inf Model - Scaffold diversity of exemplified medicinal chemistry space. ( 0,553942674292414 )
J Chem Inf Model - Identification of multitarget activity ridges in high-dimensional bioactivity spaces. ( 0,55361524226291 )
J Chem Inf Model - SAR monitoring of evolving compound data sets using activity landscapes. ( 0,553260501726539 )
J Chem Inf Model - SHAFTS: a hybrid approach for 3D molecular similarity calculation. 1. Method and assessment of virtual screening. ( 0,553181894496356 )
J Chem Inf Model - Binary classification of aqueous solubility using support vector machines with reduction and recombination feature selection. ( 0,552954388052642 )
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. ( 0,552803269955685 )
J Chem Inf Model - Automated selection of compounds with physicochemical properties to maximize bioavailability and druglikeness. ( 0,552631679767812 )
J Chem Inf Model - Knowledge-based libraries for predicting the geometric preferences of druglike molecules. ( 0,552078144245471 )
J Chem Inf Model - Jointly handling potency and toxicity of antimicrobial peptidomimetics by simple rules from desirability theory and chemoinformatics. ( 0,55166316436523 )
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. ( 0,550734312416555 )
J Chem Inf Model - Best of both worlds: on the complementarity of ligand-based and structure-based virtual screening. ( 0,550723891783259 )
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. ( 0,549240729006206 )
J Chem Inf Model - FAst MEtabolizer (FAME): A rapid and accurate predictor of sites of metabolism in multiple species by endogenous enzymes. ( 0,549100564558321 )
J Chem Inf Model - Prediction of individual compounds forming activity cliffs using emerging chemical patterns. ( 0,549082470397551 )
J Chem Inf Model - Rapid scanning structure-activity relationships in combinatorial data sets: identification of activity switches. ( 0,548875198480469 )
J Chem Inf Model - In silico assessment of chemical biodegradability. ( 0,548826216366863 )
J Chem Inf Model - Identification of a novel inhibitor of dengue virus protease through use of a virtual screening drug discovery Web portal. ( 0,547064329905617 )
J Chem Inf Model - Identification of 1,2,5-oxadiazoles as a new class of SENP2 inhibitors using structure based virtual screening. ( 0,546849106636746 )
J Chem Inf Model - Automated design of realistic organometallic molecules from fragments. ( 0,546451195966101 )
J Chem Inf Model - How diverse are diversity assessment methods? A comparative analysis and benchmarking of molecular descriptor space. ( 0,54618411065202 )
J Chem Inf Model - Construction and use of fragment-augmented molecular Hasse diagrams. ( 0,545876764175124 )
J Chem Inf Model - Design of multitarget activity landscapes that capture hierarchical activity cliff distributions. ( 0,545712347037758 )
J Chem Inf Model - Automated recycling of chemistry for virtual screening and library design. ( 0,544217488397192 )
J Chem Inf Model - Selection of in silico drug screening results for G-protein-coupled receptors by using universal active probes. ( 0,543909504733456 )
J Chem Inf Model - Prediction of synthetic accessibility based on commercially available compound databases. ( 0,543539610405367 )
J Chem Inf Model - Comparison of random forest and Pipeline Pilot Na?ve Bayes in prospective QSAR predictions. ( 0,542819635191584 )
J Chem Inf Model - Coping with unbalanced class data sets in oral absorption models. ( 0,542757815607039 )
J Chem Inf Model - Conditional probabilistic analysis for prediction of the activity landscape and relative compound activities. ( 0,542110355337908 )
J Chem Inf Model - Predicting myelosuppression of drugs from in silico models. ( 0,5414219549403 )
J Chem Inf Model - Computational repositioning and experimental validation of approved drugs for HIF-prolyl hydroxylase inhibition. ( 0,54140509427404 )
J Chem Inf Model - Quantifying the fingerprint descriptor dependence of structure-activity relationship information on a large scale. ( 0,539589160922487 )
J Chem Inf Model - Using information from historical high-throughput screens to predict active compounds. ( 0,538469830822121 )
J Chem Inf Model - Boosting virtual screening enrichments with data fusion: coalescing hits from two-dimensional fingerprints, shape, and docking. ( 0,537696702959162 )