J Chem Inf Model - Harvesting classification trees for drug discovery.

Tópicos

{ compound(1573) activ(1297) structur(1058) }
{ structur(1116) can(940) graph(676) }
{ can(774) often(719) complex(702) }
{ general(901) number(790) one(736) }
{ imag(2830) propos(1344) filter(1198) }
{ concept(1167) ontolog(924) domain(897) }
{ import(1318) role(1303) understand(862) }
{ estim(2440) model(1874) function(577) }
{ measur(2081) correl(1212) valu(896) }
{ learn(2355) train(1041) set(1003) }
{ howev(809) still(633) remain(590) }
{ perform(1367) use(1326) method(1137) }
{ case(1353) use(1143) diagnosi(1136) }
{ risk(3053) factor(974) diseas(938) }
{ system(1050) medic(1026) inform(1018) }
{ model(2656) set(1616) predict(1553) }
{ sampl(1606) size(1419) use(1276) }
{ featur(3375) classif(2383) classifi(1994) }
{ treatment(1704) effect(941) patient(846) }
{ data(1714) softwar(1251) tool(1186) }
{ gene(2352) biolog(1181) express(1162) }
{ health(1844) social(1437) communiti(874) }
{ drug(1928) target(777) effect(648) }
{ system(1976) rule(880) can(841) }
{ bind(1733) structur(1185) ligand(1036) }
{ method(1219) similar(1157) match(930) }
{ studi(2440) review(1878) systemat(933) }
{ problem(2511) optim(1539) algorithm(950) }
{ chang(1828) time(1643) increas(1301) }
{ data(3963) clinic(1234) research(1004) }
{ studi(1410) differ(1259) use(1210) }
{ health(3367) inform(1360) care(1135) }
{ ehr(2073) health(1662) electron(1139) }
{ state(1844) use(1261) util(961) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
{ data(3008) multipl(1320) sourc(1022) }
{ activ(1138) subject(705) human(624) }
{ can(981) present(881) function(850) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ implement(1333) system(1263) develop(1122) }
{ model(3404) distribut(989) bayesian(671) }
{ imag(1947) propos(1133) code(1026) }
{ data(1737) use(1416) pattern(1282) }
{ inform(2794) health(2639) internet(1427) }
{ imag(1057) registr(996) error(939) }
{ sequenc(1873) structur(1644) protein(1328) }
{ 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) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ framework(1458) process(801) describ(734) }
{ error(1145) method(1030) estim(1020) }
{ clinic(1479) use(1117) guidelin(835) }
{ algorithm(1844) comput(1787) effici(935) }
{ extract(1171) text(1153) clinic(932) }
{ method(1557) propos(1049) approach(1037) }
{ design(1359) user(1324) use(1319) }
{ control(1307) perform(991) simul(935) }
{ model(2220) cell(1177) simul(1124) }
{ care(1570) inform(1187) nurs(1089) }
{ method(984) reconstruct(947) comput(926) }
{ search(2224) databas(1162) retriev(909) }
{ featur(1941) imag(1645) propos(1176) }
{ perform(999) metric(946) measur(919) }
{ research(1085) discuss(1038) issu(1018) }
{ model(2341) predict(2261) use(1141) }
{ visual(1396) interact(850) tool(830) }
{ studi(1119) effect(1106) posit(819) }
{ blood(1257) pressur(1144) flow(957) }
{ spatial(1525) area(1432) region(1030) }
{ record(1888) medic(1808) patient(1693) }
{ model(3480) simul(1196) paramet(876) }
{ monitor(1329) mobil(1314) devic(1160) }
{ 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) }
{ signal(2180) analysi(812) frequenc(800) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ analysi(2126) use(1163) compon(1037) }
{ use(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
{ result(1111) use(1088) new(759) }
{ survey(1388) particip(1329) question(1065) }
{ decis(3086) make(1611) patient(1517) }
{ process(1125) use(805) approach(778) }
{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }
{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Millions of compounds are available as potential drug candidates. High throughput screening (HTS) is widely used in drug discovery to assay compounds for a particular biological activity. A common approach is to build a classification model using a smaller sample of assay data to predict the activity of unscreened compounds and hence select further compounds for assay. This improves the efficiency of the search by increasing the proportion of hits found among the assayed compounds. In many assays, the biological activity is dichotomized into a binary indicator variable; the explanatory variables are chemical descriptors capturing compound structure. A tree model is interpretable, which is key, since it is of interest to identify diverse chemical classes among the active compounds to serve as leads for drug optimization. Interpretability of a tree is often reduced, however, by the sheer size of the tree model and the number of variables and rules of the terminal nodes. We develop a "tree harvesting" algorithm to filter out redundant "junk" rules from the tree while retaining its predictive accuracy. This simplification can facilitate the process of uncovering key relations between molecular structure and activity and may clarify rules defining multiple activity mechanisms. Using data from the National Cancer Institute, we illustrate that many of the rules used to build a classification tree may be redundant. Unlike tree pruning, tree harvesting allows variables with junk rules to be removed near the top of the tree. The reduction in complexity of the terminal nodes improves the interpretability of the model. The algorithm also aims to reorganize the tree nodes associated with the interesting "active" class into larger, more coherent groups, thus facilitating identification of the mechanisms for activity.

Resumo Limpo

million compound avail potenti drug candid high throughput screen hts wide use drug discoveri assay compound particular biolog activ common approach build classif model use smaller sampl assay data predict activ unscreen compound henc select compound assay improv effici search increas proport hit found among assay compound mani assay biolog activ dichotom binari indic variabl explanatori variabl chemic descriptor captur compound structur tree model interpret key sinc interest identifi divers chemic class among activ compound serv lead drug optim interpret tree often reduc howev sheer size tree model number variabl rule termin node develop tree harvest algorithm filter redund junk rule tree retain predict accuraci simplif can facilit process uncov key relat molecular structur activ may clarifi rule defin multipl activ mechan use data nation cancer institut illustr mani rule use build classif tree may redund unlik tree prune tree harvest allow variabl junk rule remov near top tree reduct complex termin node improv interpret model algorithm also aim reorgan tree node associ interest activ class larger coher group thus facilit identif mechan activ

Resumos Similares

J Chem Inf Model - Navigating high-dimensional activity landscapes: design and application of the ligand-target differentiation map. ( 0,877253596042077 )
J Chem Inf Model - Discovery of novel histamine H4 and serotonin transporter ligands using the topological feature tree descriptor. ( 0,869985069378518 )
J Chem Inf Model - BioSM: metabolomics tool for identifying endogenous mammalian biochemical structures in chemical structure space. ( 0,868689067240526 )
J Chem Inf Model - A multivariate chemical similarity approach to search for drugs of potential environmental concern. ( 0,867478821768725 )
J Chem Inf Model - Polypharmacology directed compound data mining: identification of promiscuous chemotypes with different activity profiles and comparison to approved drugs. ( 0,860332029605749 )
J Chem Inf Model - Combining horizontal and vertical substructure relationships in scaffold hierarchies for activity prediction. ( 0,858454161498073 )
J Chem Inf Model - Mining for bioactive scaffolds with scaffold networks: improved compound set enrichment from primary screening data. ( 0,85660660986034 )
J Chem Inf Model - Natural product-like virtual libraries: recursive atom-based enumeration. ( 0,855916348113537 )
J Chem Inf Model - Atom pair 2D-fingerprints perceive 3D-molecular shape and pharmacophores for very fast virtual screening of ZINC and GDB-17. ( 0,85358907840881 )
J Chem Inf Model - Prediction of individual compounds forming activity cliffs using emerging chemical patterns. ( 0,847386744958058 )
J Chem Inf Model - Freely available conformer generation methods: how good are they? ( 0,840768731398574 )
J Chem Inf Model - Characterizing the diversity and biological relevance of the MLPCN assay manifold and screening set. ( 0,839282850144591 )
J Chem Inf Model - In silico enzymatic synthesis of a 400,000 compound biochemical database for nontargeted metabolomics. ( 0,834891749915373 )
J Chem Inf Model - Enumeration of 166 billion organic small molecules in the chemical universe database GDB-17. ( 0,831947413311875 )
J Chem Inf Model - How diverse are diversity assessment methods? A comparative analysis and benchmarking of molecular descriptor space. ( 0,830216156060361 )
J Chem Inf Model - Target-independent prediction of drug synergies using only drug lipophilicity. ( 0,82997326068326 )
J Chem Inf Model - Scaffold diversity of exemplified medicinal chemistry space. ( 0,828640846705107 )
J Chem Inf Model - Similarity boosted quantitative structure-activity relationship--a systematic study of enhancing structural descriptors by molecular similarity. ( 0,828450504421038 )
J Chem Inf Model - Knowledge-based libraries for predicting the geometric preferences of druglike molecules. ( 0,827481887903309 )
J Chem Inf Model - Design of multitarget activity landscapes that capture hierarchical activity cliff distributions. ( 0,827436811150065 )
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. ( 0,824708102119068 )
J Chem Inf Model - Identification of multitarget activity ridges in high-dimensional bioactivity spaces. ( 0,822965512372932 )
J Chem Inf Model - TIN-a combinatorial compound collection of synthetically feasible multicomponent synthesis products. ( 0,822086609468177 )
J Chem Inf Model - Mining the ChEMBL database: an efficient chemoinformatics workflow for assembling an ion channel-focused screening library. ( 0,820461957815801 )
J Chem Inf Model - G-protein coupled receptors virtual screening using genetic algorithm focused chemical space. ( 0,81924882013228 )
J Chem Inf Model - Fighting high molecular weight in bioactive molecules with sub-pharmacophore-based virtual screening. ( 0,815293666057629 )
J Chem Inf Model - Introduction of target cliffs as a concept to identify and describe complex molecular selectivity patterns. ( 0,813398729012028 )
J Chem Inf Model - Compound optimization through data set-dependent chemical transformations. ( 0,812550025611084 )
J Chem Inf Model - Identification of 1,2,5-oxadiazoles as a new class of SENP2 inhibitors using structure based virtual screening. ( 0,810261505399418 )
J Chem Inf Model - Automated design of realistic organometallic molecules from fragments. ( 0,81009143724124 )
J Chem Inf Model - Conditional probabilistic analysis for prediction of the activity landscape and relative compound activities. ( 0,809111919280141 )
J Chem Inf Model - Scaffold-focused virtual screening: prospective application to the discovery of TTK inhibitors. ( 0,806576075367246 )
Curr Comput Aided Drug Des - Development of Chemical Compound Libraries for In Silico Drug Screening. ( 0,805753718334269 )
J Chem Inf Model - From activity cliffs to activity ridges: informative data structures for SAR analysis. ( 0,805437527479046 )
J Chem Inf Model - Stereo signature molecular descriptor. ( 0,805281659236013 )
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. ( 0,805267589348329 )
J Chem Inf Model - Design of a three-dimensional multitarget activity landscape. ( 0,804787697793183 )
J Chem Inf Model - Efficient enumeration of stereoisomers of outerplanar chemical graphs using dynamic programming. ( 0,804716209012614 )
J Chem Inf Model - A searchable map of PubChem. ( 0,804464746145247 )
J Chem Inf Model - Identifying compound-target associations by combining bioactivity profile similarity search and public databases mining. ( 0,803554552466764 )
J Chem Inf Model - Shaping a screening file for maximal lead discovery efficiency and effectiveness: elimination of molecular redundancy. ( 0,803317967591969 )
J Chem Inf Model - Visual characterization and diversity quantification of chemical libraries: 1. creation of delimited reference chemical subspaces. ( 0,803162313706013 )
J Chem Inf Model - MQN-mapplet: visualization of chemical space with interactive maps of DrugBank, ChEMBL, PubChem, GDB-11, and GDB-13. ( 0,802629922734596 )
J Chem Inf Model - Identification of novel liver X receptor activators by structure-based modeling. ( 0,801612396924612 )
J Chem Inf Model - Identification of novel malarial cysteine protease inhibitors using structure-based virtual screening of a focused cysteine protease inhibitor library. ( 0,798947120506847 )
J Chem Inf Model - Identification of a novel inhibitor of dengue virus protease through use of a virtual screening drug discovery Web portal. ( 0,798838801227133 )
J Chem Inf Model - Using novel descriptor accounting for ligand-receptor interactions to define and visually explore biologically relevant chemical space. ( 0,798653203173159 )
J Chem Inf Model - Identification of novel serotonin transporter compounds by virtual screening. ( 0,797655485480612 )
J Chem Inf Model - Automated recycling of chemistry for virtual screening and library design. ( 0,797296619445767 )
J Chem Inf Model - SMIfp (SMILES fingerprint) chemical space for virtual screening and visualization of large databases of organic molecules. ( 0,797131233294433 )
J Chem Inf Model - Searching for recursively defined generic chemical patterns in nonenumerated fragment spaces. ( 0,795542068421664 )
J Chem Inf Model - Increasing the coverage of medicinal chemistry-relevant space in commercial fragments screening. ( 0,795221419361024 )
J Chem Inf Model - Construction and use of fragment-augmented molecular Hasse diagrams. ( 0,795135935233121 )
J Chem Inf Model - QSAR classification model for antibacterial compounds and its use in virtual screening. ( 0,79474939924458 )
J Chem Inf Model - Ligand- and structure-based virtual screening for clathrodin-derived human voltage-gated sodium channel modulators. ( 0,794338351804224 )
J Chem Inf Model - ColBioS-FlavRC: a collection of bioselective flavonoids and related compounds filtered from high-throughput screening outcomes. ( 0,793614984343716 )
J Chem Inf Model - Molecular topology analysis of the differences between drugs, clinical candidate compounds, and bioactive molecules. ( 0,793612943363662 )
J Chem Inf Model - Rationalizing the role of SAR tolerance for ligand-based virtual screening. ( 0,793351241020181 )
J Chem Inf Model - Rapid scanning structure-activity relationships in combinatorial data sets: identification of activity switches. ( 0,793343874319807 )
J Chem Inf Model - Identification of a new class of FtsZ inhibitors by structure-based design and in vitro screening. ( 0,792571125467371 )
J Chem Inf Model - Compound set enrichment: a novel approach to analysis of primary HTS data. ( 0,792042508918032 )
J Chem Inf Model - Application of computer-aided drug repurposing in the search of new cruzipain inhibitors: discovery of amiodarone and bromocriptine inhibitory effects. ( 0,791301203603572 )
J Chem Inf Model - PyDPI: freely available python package for chemoinformatics, bioinformatics, and chemogenomics studies. ( 0,789976749962486 )
J Chem Inf Model - Neighborhood-based prediction of novel active compounds from SAR matrices. ( 0,788935171594831 )
J Chem Inf Model - Synthesis, bioassay, and molecular field topology analysis of diverse vasodilatory heterocycles. ( 0,787087648723983 )
J Chem Inf Model - Discovery of new selective human aldose reductase inhibitors through virtual screening multiple binding pocket conformations. ( 0,784414212776999 )
J Chem Inf Model - Selection of in silico drug screening results for G-protein-coupled receptors by using universal active probes. ( 0,78266812171961 )
J Chem Inf Model - Identification of novel potential antibiotics against Staphylococcus using structure-based drug screening targeting dihydrofolate reductase. ( 0,781670552081082 )
J Chem Inf Model - Revisiting the general solubility equation: in silico prediction of aqueous solubility incorporating the effect of topographical polar surface area. ( 0,78144477902752 )
J Chem Inf Model - SABRE: ligand/structure-based virtual screening approach using consensus molecular-shape pattern recognition. ( 0,777186841977705 )
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. ( 0,776434142288597 )
J Chem Inf Model - Structural similarity based kriging for quantitative structure activity and property relationship modeling. ( 0,775718088389168 )
J Chem Inf Model - De novo design of drug-like molecules by a fragment-based molecular evolutionary approach. ( 0,775082893621283 )
J Chem Inf Model - Structure based model for the prediction of phospholipidosis induction potential of small molecules. ( 0,77493184782299 )
J Chem Inf Model - Fragment-based lead discovery and design. ( 0,774580087504908 )
J Chem Inf Model - Novel method for pharmacophore analysis by examining the joint pharmacophore space. ( 0,774231036478683 )
J Chem Inf Model - Discovery of a7-nicotinic receptor ligands by virtual screening of the chemical universe database GDB-13. ( 0,773834380155793 )
J Chem Inf Model - Capturing structure-activity relationships from chemogenomic spaces. ( 0,773676759115214 )
J Chem Inf Model - Multitarget structure-activity relationships characterized by activity-difference maps and consensus similarity measure. ( 0,772079080919773 )
J Chem Inf Model - Novel mycosin protease MycP1 inhibitors identified by virtual screening and 4D fingerprints. ( 0,771018090535459 )
J Chem Inf Model - Plane of best fit: a novel method to characterize the three-dimensionality of molecules. ( 0,770250721280701 )
J Chem Inf Model - Enrichment of chemical libraries docked to protein conformational ensembles and application to aldehyde dehydrogenase 2. ( 0,769818086338005 )
J Chem Inf Model - Mining chemical reactions using neighborhood behavior and condensed graphs of reactions approaches. ( 0,768889373919238 )
J Chem Inf Model - Identification of sumoylation activating enzyme 1 inhibitors by structure-based virtual screening. ( 0,768463135150253 )
J Chem Inf Model - Scanning structure-activity relationships with structure-activity similarity and related maps: from consensus activity cliffs to selectivity switches. ( 0,766874190797585 )
J Chem Inf Model - Visualization and virtual screening of the chemical universe database GDB-17. ( 0,765363231230921 )
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. ( 0,764766612979267 )
J Chem Inf Model - A new protocol for predicting novel GSK-3? ATP competitive inhibitors. ( 0,764717185858771 )
J Chem Inf Model - Virtual fragment screening: discovery of histamine H3 receptor ligands using ligand-based and protein-based molecular fingerprints. ( 0,76450368257561 )
J Chem Inf Model - Bioturbo similarity searching: combining chemical and biological similarity to discover structurally diverse bioactive molecules. ( 0,764280390975713 )
J Chem Inf Model - Identification of compounds with potential antibacterial activity against Mycobacterium through structure-based drug screening. ( 0,762686446185345 )
J Chem Inf Model - Probing the bioactivity-relevant chemical space of robust reactions and common molecular building blocks. ( 0,761952087170401 )
J Chem Inf Model - Chemical data visualization and analysis with incremental generative topographic mapping: big data challenge. ( 0,759757778552001 )
J Chem Inf Model - Hit expansion approaches using multiple similarity methods and virtualized query structures. ( 0,759190837986765 )
J Chem Inf Model - How do 2D fingerprints detect structurally diverse active compounds? Revealing compound subset-specific fingerprint features through systematic selection. ( 0,758975281278622 )
J Chem Inf Model - Prediction of activity cliffs using support vector machines. ( 0,757862401114121 )
AMIA Annu Symp Proc - Graph-based methods for discovery browsing with semantic predications. ( 0,756528335034318 )
J Chem Inf Model - Prediction of synthetic accessibility based on commercially available compound databases. ( 0,756101030549276 )
J Chem Inf Model - Noncontiguous atom matching structural similarity function. ( 0,755269901694051 )
J Chem Inf Model - Feasibility of using molecular docking-based virtual screening for searching dual target kinase inhibitors. ( 0,754227211340891 )