J Chem Inf Model - Build-up algorithm for atomic correspondence between chemical structures.

Tópicos

{ method(1219) similar(1157) match(930) }
{ compound(1573) activ(1297) structur(1058) }
{ algorithm(1844) comput(1787) effici(935) }
{ structur(1116) can(940) graph(676) }
{ take(945) account(800) differ(722) }
{ error(1145) method(1030) estim(1020) }
{ data(1714) softwar(1251) tool(1186) }
{ case(1353) use(1143) diagnosi(1136) }
{ perform(1367) use(1326) method(1137) }
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{ data(1737) use(1416) pattern(1282) }
{ measur(2081) correl(1212) valu(896) }
{ studi(2440) review(1878) systemat(933) }
{ assess(1506) score(1403) qualiti(1306) }
{ problem(2511) optim(1539) algorithm(950) }
{ howev(809) still(633) remain(590) }
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{ cost(1906) reduc(1198) effect(832) }
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{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
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{ system(1976) rule(880) can(841) }
{ imag(1057) registr(996) error(939) }
{ bind(1733) structur(1185) ligand(1036) }
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{ imag(2830) propos(1344) filter(1198) }
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{ imag(2675) segment(2577) method(1081) }
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{ studi(1410) differ(1259) use(1210) }
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{ visual(1396) interact(850) tool(830) }
{ 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) }
{ ehr(2073) health(1662) electron(1139) }
{ 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) }
{ signal(2180) analysi(812) frequenc(800) }
{ group(2977) signific(1463) compar(1072) }
{ gene(2352) biolog(1181) express(1162) }
{ 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) }
{ analysi(2126) use(1163) compon(1037) }
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{ drug(1928) target(777) effect(648) }
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{ 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(1969) cluster(1462) data(1082) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Determining a one-to-one atom correspondence between two chemical compounds is important to measure molecular similarities and to find compounds with similar biological activities. This calculation can be formalized as the maximum common substructure (MCS) problem, which is well-studied and has been shown to be NP-complete. Although many rigorous and heuristic algorithms have been developed, none of these algorithms is sufficiently fast and accurate. We developed a new program, called "kcombu" using a build-up algorithm, which is a type of the greedy heuristic algorithms. The program can search connected and disconnected MCSs as well as topologically constrained disconnected MCS (TD-MCS), which is introduced in this study. To evaluate the performance of our program, we prepared two correct standards: the exact correspondences generated by the maximum clique algorithms and the 3D correspondences obtained from superimposed 3D structure of the molecules in a complex 3D structure with the same protein. For the five sets of molecules taken from the protein structure database, the agreement value between the build-up and the exact correspondences for the connected MCS is sufficiently high, but the computation time of the build-up algorithm is much smaller than that of the exact algorithm. The comparison between the build-up and the 3D correspondences shows that the TD-MCS has the best agreement value among the other types of MCS. Additionally, we observed a strong correlation between the molecular similarity and the agreement with the correct and 3D correspondences; more similar molecule pairs are more correctly matched. Molecular pairs with more than 40% Tanimoto similarities can be correctly matched for more than half of the atoms with the 3D correspondences.

Resumo Limpo

determin onetoon atom correspond two chemic compound import measur molecular similar find compound similar biolog activ calcul can formal maximum common substructur mcs problem wellstudi shown npcomplet although mani rigor heurist algorithm develop none algorithm suffici fast accur develop new program call kcombu use buildup algorithm type greedi heurist algorithm program can search connect disconnect mcss well topolog constrain disconnect mcs tdmcs introduc studi evalu perform program prepar two correct standard exact correspond generat maximum cliqu algorithm d correspond obtain superimpos d structur molecul complex d structur protein five set molecul taken protein structur databas agreement valu buildup exact correspond connect mcs suffici high comput time buildup algorithm much smaller exact algorithm comparison buildup d correspond show tdmcs best agreement valu among type mcs addit observ strong correl molecular similar agreement correct d correspond similar molecul pair correct match molecular pair tanimoto similar can correct match half atom d correspond

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