J Chem Inf Model - Large-scale assessment of activity landscape feature probabilities of bioactive compounds.

Tópicos

{ compound(1573) activ(1297) structur(1058) }
{ featur(1941) imag(1645) propos(1176) }
{ model(3404) distribut(989) bayesian(671) }
{ general(901) number(790) one(736) }
{ ehr(2073) health(1662) electron(1139) }
{ method(1219) similar(1157) match(930) }
{ data(3008) multipl(1320) sourc(1022) }
{ sequenc(1873) structur(1644) protein(1328) }
{ framework(1458) process(801) describ(734) }
{ error(1145) method(1030) estim(1020) }
{ analysi(2126) use(1163) compon(1037) }
{ high(1669) rate(1365) level(1280) }
{ imag(1947) propos(1133) code(1026) }
{ assess(1506) score(1403) qualiti(1306) }
{ search(2224) databas(1162) retriev(909) }
{ data(3963) clinic(1234) research(1004) }
{ system(1050) medic(1026) inform(1018) }
{ visual(1396) interact(850) tool(830) }
{ perform(1367) use(1326) method(1137) }
{ state(1844) use(1261) util(961) }
{ intervent(3218) particip(2042) group(1664) }
{ structur(1116) can(940) graph(676) }
{ decis(3086) make(1611) patient(1517) }
{ method(2212) result(1239) propos(1039) }
{ 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) }
{ imag(1057) registr(996) error(939) }
{ bind(1733) structur(1185) ligand(1036) }
{ featur(3375) classif(2383) classifi(1994) }
{ 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) }
{ treatment(1704) effect(941) patient(846) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ 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) }
{ extract(1171) text(1153) clinic(932) }
{ method(1557) propos(1049) approach(1037) }
{ 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) }
{ method(984) reconstruct(947) comput(926) }
{ case(1353) use(1143) diagnosi(1136) }
{ howev(809) still(633) remain(590) }
{ studi(1410) differ(1259) use(1210) }
{ risk(3053) factor(974) diseas(938) }
{ perform(999) metric(946) measur(919) }
{ research(1085) discuss(1038) issu(1018) }
{ import(1318) role(1303) understand(862) }
{ model(2341) predict(2261) use(1141) }
{ studi(1119) effect(1106) posit(819) }
{ 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) }
{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ model(2656) set(1616) predict(1553) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ first(2504) two(1366) second(1323) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
{ health(1844) social(1437) communiti(874) }
{ cancer(2502) breast(956) screen(824) }
{ use(976) code(926) identifi(902) }
{ 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) }
{ process(1125) use(805) approach(778) }
{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Activity landscape representations integrate pairwise compound similarity and potency relationships and provide direct access to characteristic structure-activity relationship features in compound data sets. Because pairwise compound comparisons provide the foundation of activity landscape design, the assessment of specific landscape features such as activity cliffs has generally been confined to the level of compound pairs. A conditional probability-based approach has been applied herein to assign most probable activity landscape features to individual compounds. For example, for a given data set compound, it was determined if it would preferentially engage in the formation of activity cliffs or other landscape features. In a large-scale effort, we have determined conditional activity landscape feature probabilities for more than 160,000 compounds with well-defined activity annotations contained in 427 different target-based data sets. These landscape feature probabilities provide a detailed view of how different activity landscape features are distributed over currently available bioactive compounds.

Resumo Limpo

activ landscap represent integr pairwis compound similar potenc relationship provid direct access characterist structureact relationship featur compound data set pairwis compound comparison provid foundat activ landscap design assess specif landscap featur activ cliff general confin level compound pair condit probabilitybas approach appli herein assign probabl activ landscap featur individu compound exampl given data set compound determin preferenti engag format activ cliff landscap featur largescal effort determin condit activ landscap featur probabl compound welldefin activ annot contain differ targetbas data set landscap featur probabl provid detail view differ activ landscap featur distribut current avail bioactiv compound

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