J Chem Inf Model - Predicting myelosuppression of drugs from in silico models.

Tópicos

{ compound(1573) activ(1297) structur(1058) }
{ model(2656) set(1616) predict(1553) }
{ analysi(2126) use(1163) compon(1037) }
{ drug(1928) target(777) effect(648) }
{ sampl(1606) size(1419) use(1276) }
{ system(1976) rule(880) can(841) }
{ method(1969) cluster(1462) data(1082) }
{ import(1318) role(1303) understand(862) }
{ model(2341) predict(2261) use(1141) }
{ featur(1941) imag(1645) propos(1176) }
{ studi(2440) review(1878) systemat(933) }
{ error(1145) method(1030) estim(1020) }
{ studi(1119) effect(1106) posit(819) }
{ measur(2081) correl(1212) valu(896) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ process(1125) use(805) approach(778) }
{ featur(3375) classif(2383) classifi(1994) }
{ take(945) account(800) differ(722) }
{ model(2220) cell(1177) simul(1124) }
{ search(2224) databas(1162) retriev(909) }
{ case(1353) use(1143) diagnosi(1136) }
{ perform(999) metric(946) measur(919) }
{ first(2504) two(1366) second(1323) }
{ use(2086) technolog(871) perceiv(783) }
{ can(774) often(719) complex(702) }
{ data(1737) use(1416) pattern(1282) }
{ imag(1057) registr(996) error(939) }
{ assess(1506) score(1403) qualiti(1306) }
{ treatment(1704) effect(941) patient(846) }
{ problem(2511) optim(1539) algorithm(950) }
{ method(1557) propos(1049) approach(1037) }
{ general(901) number(790) one(736) }
{ data(3963) clinic(1234) research(1004) }
{ risk(3053) factor(974) diseas(938) }
{ research(1085) discuss(1038) issu(1018) }
{ system(1050) medic(1026) inform(1018) }
{ perform(1367) use(1326) method(1137) }
{ age(1611) year(1155) adult(843) }
{ group(2977) signific(1463) compar(1072) }
{ data(3008) multipl(1320) sourc(1022) }
{ can(981) present(881) function(850) }
{ structur(1116) can(940) graph(676) }
{ detect(2391) sensit(1101) algorithm(908) }
{ model(3404) distribut(989) bayesian(671) }
{ imag(1947) propos(1133) code(1026) }
{ inform(2794) health(2639) internet(1427) }
{ bind(1733) structur(1185) ligand(1036) }
{ sequenc(1873) structur(1644) protein(1328) }
{ method(1219) similar(1157) match(930) }
{ imag(2830) propos(1344) filter(1198) }
{ network(2748) neural(1063) input(814) }
{ imag(2675) segment(2577) method(1081) }
{ patient(2315) diseas(1263) diabet(1191) }
{ motion(1329) object(1292) video(1091) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ framework(1458) process(801) describ(734) }
{ 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) }
{ data(1714) softwar(1251) tool(1186) }
{ design(1359) user(1324) use(1319) }
{ control(1307) perform(991) simul(935) }
{ care(1570) inform(1187) nurs(1089) }
{ method(984) reconstruct(947) comput(926) }
{ howev(809) still(633) remain(590) }
{ studi(1410) differ(1259) use(1210) }
{ 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) }
{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ data(2317) use(1299) case(1017) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ gene(2352) biolog(1181) express(1162) }
{ 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) }
{ use(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
{ result(1111) use(1088) new(759) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ activ(1452) weight(1219) physic(1104) }
{ method(2212) result(1239) propos(1039) }

Resumo

Anticancer agents targeting proliferating cell populations in tumor as well as in normal tissues can lead to a number of side effects including hematotoxicity, a common dose-limiting toxicity associated with oncology drugs. Myelosuppression, regarded as unacceptable for other therapeutic indications, is considered a clinical risk also for new targeted anticancer drugs acting specifically on tumor cells. Thus, it becomes important not only to evaluate the potential toxicity of such new therapeutics to human hematopoietic tissue during preclinical development but also to anticipate this liability in early drug discovery. This could be achieved by using in silico models to guide the design of new lead compounds and the selection of analogs with reduced myelosuppressive potential. Hence, the purpose of this study was to develop computational models able to predict the potential myelotoxicity of drugs from their chemical structure. The data set analyzed included 38 drugs. The structural diversity and the drug-like space covered by these molecules were investigated using the ChemGPS methodology. Two sets of potentially relevant descriptors for modeling myelotoxicity (i.e., 3D Volsurf+ and 2D structural and electrotopological E-states descriptors) were selected and a Principal Component Analysis was carried out on the entire set of data. The first two PCs were able to discriminate the highest from the least myelotoxic compounds with a total accuracy of 95%. Then, a quantitative PLS model was developed by correlating a selected subset of in vitro hematotoxicity data with Volsurf+ descriptors. After variable selection, the PLS analysis resulted in a one-latent-variable model with r(2) of 0.79 and q(2) of 0.72. The inclusion of 2D descriptors in the PLS analysis improved only slightly the robustness and quality of the model that predicted the pIC(50) values of 21 drugs not included in the model with a RMSEP of 0.67 and a squared correlation coefficient (r(0)(2)) of 0.70. Furthermore, in order to investigate whether the highly myelotoxic compounds are characterized by common structural features, which should be taken into consideration in the design of new candidate drugs, the entire data set was analyzed using GRIND toxicophore-based descriptors. One toxicophore emerged from the interpretation of the model. The toxicophore elements, at least determined by the molecules used in this study, are a pattern of H-bond acceptor groups, presence of a H-bond donor and H-bond acceptor regions at ~15 ? distance and a hydrophobic and H-bond acceptor interacting regions separated by a distance of ~12.4 ?. Moreover, the dimensions of the molecule play a role in its recognition as a myelotoxic compound.

Resumo Limpo

anticanc agent target prolifer cell popul tumor well normal tissu can lead number side effect includ hematotox common doselimit toxic associ oncolog drug myelosuppress regard unaccept therapeut indic consid clinic risk also new target anticanc drug act specif tumor cell thus becom import evalu potenti toxic new therapeut human hematopoiet tissu preclin develop also anticip liabil earli drug discoveri achiev use silico model guid design new lead compound select analog reduc myelosuppress potenti henc purpos studi develop comput model abl predict potenti myelotox drug chemic structur data set analyz includ drug structur divers druglik space cover molecul investig use chemgp methodolog two set potenti relev descriptor model myelotox ie d volsurf d structur electrotopolog estat descriptor select princip compon analysi carri entir set data first two pcs abl discrimin highest least myelotox compound total accuraci quantit pls model develop correl select subset vitro hematotox data volsurf descriptor variabl select pls analysi result onelatentvari model r q inclus d descriptor pls analysi improv slight robust qualiti model predict pic valu drug includ model rmsep squar correl coeffici r furthermor order investig whether high myelotox compound character common structur featur taken consider design new candid drug entir data set analyz use grind toxicophorebas descriptor one toxicophor emerg interpret model toxicophor element least determin molecul use studi pattern hbond acceptor group presenc hbond donor hbond acceptor region distanc hydrophob hbond acceptor interact region separ distanc moreov dimens molecul play role recognit myelotox compound

Resumos Similares

J Chem Inf Model - Profile-QSAR and Surrogate AutoShim protein-family modeling of proteases. ( 0,803455212620539 )
J Chem Inf Model - Predictive models for cytochrome p450 isozymes based on quantitative high throughput screening data. ( 0,801907199899553 )
J Chem Inf Model - A new protocol for predicting novel GSK-3? ATP competitive inhibitors. ( 0,773761284710089 )
J Chem Inf Model - Jointly handling potency and toxicity of antimicrobial peptidomimetics by simple rules from desirability theory and chemoinformatics. ( 0,764580718918278 )
J Chem Inf Model - Prediction of compound potency changes in matched molecular pairs using support vector regression. ( 0,73623283159699 )
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. ( 0,732912871404153 )
J Chem Inf Model - A critical assessment of combined ligand- and structure-based approaches to HERG channel blocker modeling. ( 0,727534755687149 )
J Chem Inf Model - Predictions of BuChE inhibitors using support vector machine and naive Bayesian classification techniques in drug discovery. ( 0,722177853124417 )
J Chem Inf Model - Statistical analysis and compound selection of combinatorial libraries for soluble epoxide hydrolase. ( 0,721576466524661 )
J Am Med Inform Assoc - Drug repurposing: mining protozoan proteomes for targets of known bioactive compounds. ( 0,720855450139036 )
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. ( 0,719304944373845 )
J Chem Inf Model - Design and synthesis of new antioxidants predicted by the model developed on a set of pulvinic acid derivatives. ( 0,718261205952721 )
J Chem Inf Model - Hsp90 inhibitors, part 1: definition of 3-D QSAutogrid/R models as a tool for virtual screening. ( 0,716711339326589 )
J Chem Inf Model - Hsp90 inhibitors, part 2: combining ligand-based and structure-based approaches for virtual screening application. ( 0,702883464078136 )
J Integr Bioinform - Database supported candidate search for metabolite identification. ( 0,698590962744156 )
J Chem Inf Model - Construction and use of fragment-augmented molecular Hasse diagrams. ( 0,698461422705161 )
J Chem Inf Model - Coping with unbalanced class data sets in oral absorption models. ( 0,698440756636287 )
J Chem Inf Model - Modeling drug-induced anorexia by molecular topology. ( 0,698058386784295 )
J Chem Inf Model - Discovery of novel antimalarial compounds enabled by QSAR-based virtual screening. ( 0,695840932911256 )
J Chem Inf Model - In silico prediction of aqueous solubility using simple QSPR models: the importance of phenol and phenol-like moieties. ( 0,692978859268546 )
J Chem Inf Model - Development of a minimal kinase ensemble receptor (MKER) for surrogate AutoShim. ( 0,687271259978543 )
J Chem Inf Model - How accurately can we predict the melting points of drug-like compounds? ( 0,68571343192722 )
J Chem Inf Model - Predicting pK(a) values of substituted phenols from atomic charges: comparison of different quantum mechanical methods and charge distribution schemes. ( 0,684844689408884 )
J Chem Inf Model - Discovering new agents active against methicillin-resistant Staphylococcus aureus with ligand-based approaches. ( 0,683766566794741 )
J Chem Inf Model - Revisiting the general solubility equation: in silico prediction of aqueous solubility incorporating the effect of topographical polar surface area. ( 0,683207925749972 )
J Chem Inf Model - Structure based model for the prediction of phospholipidosis induction potential of small molecules. ( 0,682157337763148 )
J Chem Inf Model - Classification of compounds with distinct or overlapping multi-target activities and diverse molecular mechanisms using emerging chemical patterns. ( 0,680893720796127 )
J Chem Inf Model - QSAR classification model for antibacterial compounds and its use in virtual screening. ( 0,679471138607664 )
J Chem Inf Model - Applicability Domain ANalysis (ADAN): a robust method for assessing the reliability of drug property predictions. ( 0,674832888138527 )
J Chem Inf Model - Quantitative structure-activity relationship models for ready biodegradability of chemicals. ( 0,674663425291508 )
J Chem Inf Model - QSAR modeling of imbalanced high-throughput screening data in PubChem. ( 0,674563313397229 )
J Chem Inf Model - Probing the bioactivity-relevant chemical space of robust reactions and common molecular building blocks. ( 0,671798202325945 )
J Chem Inf Model - Fighting high molecular weight in bioactive molecules with sub-pharmacophore-based virtual screening. ( 0,671348207038839 )
J Chem Inf Model - Analysis and study of molecule data sets using snowflake diagrams of weighted maximum common subgraph trees. ( 0,670673659819208 )
J Chem Inf Model - Algorithm for reaction classification. ( 0,669113733143946 )
J Chem Inf Model - Drug side-effect prediction based on the integration of chemical and biological spaces. ( 0,667618119512801 )
BMC Med Inform Decis Mak - Regression tree construction by bootstrap: model search for DRG-systems applied to Austrian health-data. ( 0,659840081951449 )
J Chem Inf Model - Improving the use of ranking in virtual screening against HIV-1 integrase with triangular numbers and including ligand profiling with antitargets. ( 0,657975005283642 )
J Chem Inf Model - In silico prediction of total human plasma clearance. ( 0,657423032582644 )
J Chem Inf Model - Automated building of organometallic complexes from 3D fragments. ( 0,656481165773786 )
J Chem Inf Model - Mining chemical reactions using neighborhood behavior and condensed graphs of reactions approaches. ( 0,655990769866056 )
J Chem Inf Model - ColBioS-FlavRC: a collection of bioselective flavonoids and related compounds filtered from high-throughput screening outcomes. ( 0,655157201551024 )
J Chem Inf Model - Application of quantitative structure-activity relationship models of 5-HT1A receptor binding to virtual screening identifies novel and potent 5-HT1A ligands. ( 0,654947862233446 )
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. ( 0,654116531320762 )
J Chem Inf Model - Discovery of a novel selective kappa-opioid receptor agonist using crystal structure-based virtual screening. ( 0,654045783780812 )
J Chem Inf Model - Classification of cytochrome P450 inhibitors and noninhibitors using combined classifiers. ( 0,651278634689574 )
J Chem Inf Model - Identification of a novel inhibitor of dengue virus protease through use of a virtual screening drug discovery Web portal. ( 0,650965277147961 )
J Chem Inf Model - Accurate atom-mapping computation for biochemical reactions. ( 0,650712365432244 )
J Chem Inf Model - Oversampling to overcome overfitting: exploring the relationship between data set composition, molecular descriptors, and predictive modeling methods. ( 0,649506052782713 )
J Chem Inf Model - Combined receptor and ligand-based approach to the universal pharmacophore model development for studies of drug blockade to the hERG1 pore domain. ( 0,648929062458951 )
J Chem Inf Model - Visualization and virtual screening of the chemical universe database GDB-17. ( 0,647630512523189 )
J Chem Inf Model - Experimental and computational prediction of glass transition temperature of drugs. ( 0,643061159456623 )
J Chem Inf Model - Development of a rule-based method for the assessment of protein druggability. ( 0,642251515548699 )
J Chem Inf Model - Similarity searching for potent compounds using feature selection. ( 0,640386970329188 )
J Chem Inf Model - Pharmacophore assessment through 3-D QSAR: evaluation of the predictive ability on new derivatives by the application on a series of antitubercular agents. ( 0,640088235489228 )
J Chem Inf Model - Pharmacophore modeling, virtual screening, and in vitro testing reveal haloperidol, eprazinone, and fenbutrazate as neurokinin receptors ligands. ( 0,639799615617898 )
J Chem Inf Model - Quantitative structure-activity relationship models of chemical transformations from matched pairs analyses. ( 0,63904504487567 )
J Chem Inf Model - Exploring polypharmacology using a ROCS-based target fishing approach. ( 0,638952641197025 )
J Chem Inf Model - Rationalizing the role of SAR tolerance for ligand-based virtual screening. ( 0,637731994768273 )
J Chem Inf Model - Structure based design, synthesis, pharmacophore modeling, virtual screening, and molecular docking studies for identification of novel cyclophilin D inhibitors. ( 0,635416630376609 )
J Chem Inf Model - Application of the 4D fingerprint method with a robust scoring function for scaffold-hopping and drug repurposing strategies. ( 0,63519831383139 )
J Chem Inf Model - A multivariate chemical similarity approach to search for drugs of potential environmental concern. ( 0,633535884900015 )
J Chem Inf Model - BioSM: metabolomics tool for identifying endogenous mammalian biochemical structures in chemical structure space. ( 0,632164075156395 )
J Chem Inf Model - Exploring the biologically relevant chemical space for drug discovery. ( 0,631757068621014 )
J Chem Inf Model - Discovery of inhibitors of Schistosoma mansoni HDAC8 by combining homology modeling, virtual screening, and in vitro validation. ( 0,631469327632753 )
J Chem Inf Model - Computational repositioning and experimental validation of approved drugs for HIF-prolyl hydroxylase inhibition. ( 0,631139560354807 )
J Chem Inf Model - FAst MEtabolizer (FAME): A rapid and accurate predictor of sites of metabolism in multiple species by endogenous enzymes. ( 0,630615466546659 )
J Chem Inf Model - ZINClick: a database of 16 million novel, patentable, and readily synthesizable 1,4-disubstituted triazoles. ( 0,630511305815919 )
J Chem Inf Model - Identification of sumoylation activating enzyme 1 inhibitors by structure-based virtual screening. ( 0,629930233397142 )
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. ( 0,629742037503459 )
J Chem Inf Model - Kinome-wide activity modeling from diverse public high-quality data sets. ( 0,62969574258659 )
J Chem Inf Model - G-protein coupled receptors virtual screening using genetic algorithm focused chemical space. ( 0,627886284404867 )
J Chem Inf Model - Feasibility of using molecular docking-based virtual screening for searching dual target kinase inhibitors. ( 0,62736646895274 )
J Chem Inf Model - Quantitative structure-activity relationship models of clinical pharmacokinetics: clearance and volume of distribution. ( 0,625497626640616 )
J Chem Inf Model - Identification of novel malarial cysteine protease inhibitors using structure-based virtual screening of a focused cysteine protease inhibitor library. ( 0,625221370989216 )
J Chem Inf Model - TIN-a combinatorial compound collection of synthetically feasible multicomponent synthesis products. ( 0,624789604648809 )
J Chem Inf Model - A comparison of different QSAR approaches to modeling CYP450 1A2 inhibition. ( 0,62427808164313 )
J Chem Inf Model - Increasing the coverage of medicinal chemistry-relevant space in commercial fragments screening. ( 0,62378950237047 )
J Chem Inf Model - Identification of compounds with potential antibacterial activity against Mycobacterium through structure-based drug screening. ( 0,623337475878128 )
J Chem Inf Model - Fragment-based lead discovery and design. ( 0,623133154689053 )
J Chem Inf Model - A searchable map of PubChem. ( 0,623101338343277 )
J Chem Inf Model - Compound optimization through data set-dependent chemical transformations. ( 0,622752213902054 )
J Chem Inf Model - Synthesis, bioassay, and molecular field topology analysis of diverse vasodilatory heterocycles. ( 0,620346859466536 )
J Chem Inf Model - Combinatorial ? computational ? cheminformatics (C3) approach to characterization of congeneric libraries of organic pollutants. ( 0,620017142485966 )
J Chem Inf Model - Identification of multitarget activity ridges in high-dimensional bioactivity spaces. ( 0,618687182753276 )
J Chem Inf Model - Prediction of individual compounds forming activity cliffs using emerging chemical patterns. ( 0,618679706465289 )
J Chem Inf Model - AlzPlatform: an Alzheimer's disease domain-specific chemogenomics knowledgebase for polypharmacology and target identification research. ( 0,618401799242596 )
J Chem Inf Model - Capturing structure-activity relationships from chemogenomic spaces. ( 0,618214193567935 )
J Chem Inf Model - Compound set enrichment: a novel approach to analysis of primary HTS data. ( 0,617645888192222 )
J Chem Inf Model - Freely available conformer generation methods: how good are they? ( 0,616566837254878 )
J Chem Inf Model - Structural similarity based kriging for quantitative structure activity and property relationship modeling. ( 0,616500141185961 )
J Chem Inf Model - Automated recycling of chemistry for virtual screening and library design. ( 0,614591710841656 )
J Chem Inf Model - Searching for recursively defined generic chemical patterns in nonenumerated fragment spaces. ( 0,614096654269005 )
J Chem Inf Model - Polypharmacology directed compound data mining: identification of promiscuous chemotypes with different activity profiles and comparison to approved drugs. ( 0,613744576299615 )
J Chem Inf Model - Visual characterization and diversity quantification of chemical libraries: 1. creation of delimited reference chemical subspaces. ( 0,613312609795624 )
J Chem Inf Model - Discovery of a7-nicotinic receptor ligands by virtual screening of the chemical universe database GDB-13. ( 0,612009719488146 )
J Chem Inf Model - HELM: a hierarchical notation language for complex biomolecule structure representation. ( 0,61096590877728 )
J Chem Inf Model - Beyond the scope of Free-Wilson analysis: building interpretable QSAR models with machine learning algorithms. ( 0,610964308830266 )
J Chem Inf Model - Development of novel 3D-QSAR combination approach for screening and optimizing B-Raf inhibitors in silico. ( 0,610669012254714 )
J Chem Inf Model - Maximum-score diversity selection for early drug discovery. ( 0,610440184110273 )