J Chem Inf Model - Assessing relative bioactivity of chemical substances using quantitative molecular network topology analysis.

Tópicos

{ perform(999) metric(946) measur(919) }
{ compound(1573) activ(1297) structur(1058) }
{ structur(1116) can(940) graph(676) }
{ can(981) present(881) function(850) }
{ network(2748) neural(1063) input(814) }
{ search(2224) databas(1162) retriev(909) }
{ risk(3053) factor(974) diseas(938) }
{ gene(2352) biolog(1181) express(1162) }
{ drug(1928) target(777) effect(648) }
{ implement(1333) system(1263) develop(1122) }
{ model(3404) distribut(989) bayesian(671) }
{ bind(1733) structur(1185) ligand(1036) }
{ system(1050) medic(1026) inform(1018) }
{ perform(1367) use(1326) method(1137) }
{ model(3480) simul(1196) paramet(876) }
{ can(774) often(719) complex(702) }
{ inform(2794) health(2639) internet(1427) }
{ sequenc(1873) structur(1644) protein(1328) }
{ method(1219) similar(1157) match(930) }
{ studi(2440) review(1878) systemat(933) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ framework(1458) process(801) describ(734) }
{ concept(1167) ontolog(924) domain(897) }
{ case(1353) use(1143) diagnosi(1136) }
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{ health(3367) inform(1360) care(1135) }
{ model(2656) set(1616) predict(1553) }
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{ featur(3375) classif(2383) classifi(1994) }
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{ imag(2675) segment(2577) method(1081) }
{ patient(2315) diseas(1263) diabet(1191) }
{ take(945) account(800) differ(722) }
{ motion(1329) object(1292) video(1091) }
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{ error(1145) method(1030) estim(1020) }
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{ learn(2355) train(1041) set(1003) }
{ clinic(1479) use(1117) guidelin(835) }
{ algorithm(1844) comput(1787) effici(935) }
{ extract(1171) text(1153) clinic(932) }
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{ 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) }
{ general(901) number(790) one(736) }
{ method(984) reconstruct(947) comput(926) }
{ featur(1941) imag(1645) propos(1176) }
{ data(3963) clinic(1234) research(1004) }
{ studi(1410) differ(1259) use(1210) }
{ import(1318) role(1303) understand(862) }
{ model(2341) predict(2261) use(1141) }
{ visual(1396) interact(850) tool(830) }
{ spatial(1525) area(1432) region(1030) }
{ record(1888) medic(1808) patient(1693) }
{ 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) }
{ signal(2180) analysi(812) frequenc(800) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ data(3008) multipl(1320) sourc(1022) }
{ 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) }
{ health(1844) social(1437) communiti(874) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ use(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ process(1125) use(805) approach(778) }
{ activ(1452) weight(1219) physic(1104) }
{ method(2212) result(1239) propos(1039) }

Resumo

Structurally different chemical substances may cause similar systemic effects in mammalian cells. It is therefore necessary to go beyond structural comparisons to quantify similarity in terms of their bioactivities. In this work, we introduce a generic methodology to achieve this on the basis of Network Biology principles and using publicly available molecular network topology information. An implementation of this method, denoted QuantMap, is outlined and applied to antidiabetic drugs, NSAIDs, 17?-estradiol, and 12 substances known to disrupt estrogenic pathways. The similarity of any pair of compounds is derived from topological comparison of intracellular protein networks, directly and indirectly associated with the respective query chemicals, via a straightforward pairwise comparison of ranked proteins. Although output derived from straightforward chemical/structural similarity analysis provided some guidance on bioactivity, QuantMap produced substance interrelationships that align well with reports on their respective perturbation properties. We believe that QuantMap has potential to provide substantial assistance to drug repositioning, pharmacology evaluation, and toxicology risk assessment.

Resumo Limpo

structur differ chemic substanc may caus similar system effect mammalian cell therefor necessari go beyond structur comparison quantifi similar term bioactiv work introduc generic methodolog achiev basi network biolog principl use public avail molecular network topolog inform implement method denot quantmap outlin appli antidiabet drug nsaid estradiol substanc known disrupt estrogen pathway similar pair compound deriv topolog comparison intracellular protein network direct indirect associ respect queri chemic via straightforward pairwis comparison rank protein although output deriv straightforward chemicalstructur similar analysi provid guidanc bioactiv quantmap produc substanc interrelationship align well report respect perturb properti believ quantmap potenti provid substanti assist drug reposit pharmacolog evalu toxicolog risk assess

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