Pub Date : 2000-12-01DOI: 10.1002/1521-3838(200012)19:5<475::AID-QSAR475>3.0.CO;2-3
Somsak Tonmunphean, V. Parasuk, S. Kokpol
The quantitative structure-activity relationships (QSAR) between antimalarial activities and artemisinin-heme binding properties were studied by means of docking calculations. Automated molecular dockings of 30 artemisinin derivatives to heme revealed a significant relationship between biological activity and binding energy (ra ˇ0:93) and less significantly with the O1-Fe distance (raˇ0:55). The QSAR models were constructed and the predicted biological activities were in good agreement with the corresponding experimental values. The docking also showed that artemisinin compounds approach heme by pointing O1 at the endoperoxide linkage toward the iron center, a mechanism controlled by the steric hindrance.
{"title":"QSAR study of antimalarial activities and artemisinin-heme binding properties obtained from docking calculations.","authors":"Somsak Tonmunphean, V. Parasuk, S. Kokpol","doi":"10.1002/1521-3838(200012)19:5<475::AID-QSAR475>3.0.CO;2-3","DOIUrl":"https://doi.org/10.1002/1521-3838(200012)19:5<475::AID-QSAR475>3.0.CO;2-3","url":null,"abstract":"The quantitative structure-activity relationships (QSAR) between antimalarial activities and artemisinin-heme binding properties were studied by means of docking calculations. Automated molecular dockings of 30 artemisinin derivatives to heme revealed a significant relationship between biological activity and binding energy (ra ˇ0:93) and less significantly with the O1-Fe distance (raˇ0:55). The QSAR models were constructed and the predicted biological activities were in good agreement with the corresponding experimental values. The docking also showed that artemisinin compounds approach heme by pointing O1 at the endoperoxide linkage toward the iron center, a mechanism controlled by the steric hindrance.","PeriodicalId":20818,"journal":{"name":"Quantitative Structure-activity Relationships","volume":"1 1","pages":"475-483"},"PeriodicalIF":0.0,"publicationDate":"2000-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86553134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2000-12-01DOI: 10.1002/1521-3838(200012)19:6<574::AID-QSAR574>3.0.CO;2-2
A. Davis, D. Salt, P. Webborn
{"title":"Induced Correlations in the Use of Unbound/Intrinsic Pharmacokinetic Parameters in Quantitative Structure-Pharmacokinetic Relationships with Lipophilicity","authors":"A. Davis, D. Salt, P. Webborn","doi":"10.1002/1521-3838(200012)19:6<574::AID-QSAR574>3.0.CO;2-2","DOIUrl":"https://doi.org/10.1002/1521-3838(200012)19:6<574::AID-QSAR574>3.0.CO;2-2","url":null,"abstract":"","PeriodicalId":20818,"journal":{"name":"Quantitative Structure-activity Relationships","volume":"11 1","pages":"574-580"},"PeriodicalIF":0.0,"publicationDate":"2000-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86887315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2000-12-01DOI: 10.1002/1521-3838(200012)19:6<547::AID-QSAR547>3.0.CO;2-2
A. Freidig, J. Hermens
Quantitative structure activity relationships (QSAR) that describe the acute fish toxicity have been published for many different groups of reactive organic chemicals. The structural similarity of chemicals within such groups, suggests that they share a common mode of action (MOA) which is based on their common chemical reactivity. Often, however, a descriptor for this reactivity alone can not explain the observed toxicity satisfactory but addition of a hydrophobicity parameter, like log KOW, is found to improve the relationship. In the present paper, an alternative strategy is proposed and tested with three different literature data sets. Instead of searching for better descriptors to establish a QSAR for the whole data set, the assumption that all compounds within the set act by the same MOA was critically reviewed. We tested the hypothesis that some of the compounds within the data sets acted by narcosis (general anesthesia), a second plausible mode of action in acute fish toxicity. Narcosis potency at observed lethal exposure levels was modeled with a baseline toxicity QSAR. The literature data sets were split in a narcosis and a reactive subset and for each of them a separate, one-parameter QSAR was established. For a set of OP-esters, nine out of 20 compounds were identified as possible narcotic compounds and their toxicity could be described with a narcosis QSAR. For the 11 compounds remaining in the reactive subset, a good correlation between acute toxicity and measured, in-vitro AChE inhibition rate was found (r2=0.68) which would have been overlooked if the whole data set was used. The use of two separate QSARs instead of one mixed QSAR was also tested for literature data sets of nitrobenzenes and α,β-unsaturated carboxylates. It was shown that for the description of toxicity data of all three groups of reactive compounds, a model which uses two separate modes of action was superior to a mixed model which uses a reactivity and a hydrophobicity parameter in a multiple linear regression.
{"title":"Narcosis and chemical reactivity QSARs for acute fish toxicity","authors":"A. Freidig, J. Hermens","doi":"10.1002/1521-3838(200012)19:6<547::AID-QSAR547>3.0.CO;2-2","DOIUrl":"https://doi.org/10.1002/1521-3838(200012)19:6<547::AID-QSAR547>3.0.CO;2-2","url":null,"abstract":"Quantitative structure activity relationships (QSAR) that describe the acute fish toxicity have been published for many different groups of reactive organic chemicals. The structural similarity of chemicals within such groups, suggests that they share a common mode of action (MOA) which is based on their common chemical reactivity. Often, however, a descriptor for this reactivity alone can not explain the observed toxicity satisfactory but addition of a hydrophobicity parameter, like log KOW, is found to improve the relationship. In the present paper, an alternative strategy is proposed and tested with three different literature data sets. Instead of searching for better descriptors to establish a QSAR for the whole data set, the assumption that all compounds within the set act by the same MOA was critically reviewed. We tested the hypothesis that some of the compounds within the data sets acted by narcosis (general anesthesia), a second plausible mode of action in acute fish toxicity. Narcosis potency at observed lethal exposure levels was modeled with a baseline toxicity QSAR. The literature data sets were split in a narcosis and a reactive subset and for each of them a separate, one-parameter QSAR was established. For a set of OP-esters, nine out of 20 compounds were identified as possible narcotic compounds and their toxicity could be described with a narcosis QSAR. For the 11 compounds remaining in the reactive subset, a good correlation between acute toxicity and measured, in-vitro AChE inhibition rate was found (r2=0.68) which would have been overlooked if the whole data set was used. The use of two separate QSARs instead of one mixed QSAR was also tested for literature data sets of nitrobenzenes and α,β-unsaturated carboxylates. It was shown that for the description of toxicity data of all three groups of reactive compounds, a model which uses two separate modes of action was superior to a mixed model which uses a reactivity and a hydrophobicity parameter in a multiple linear regression.","PeriodicalId":20818,"journal":{"name":"Quantitative Structure-activity Relationships","volume":"20 1","pages":"547-553"},"PeriodicalIF":0.0,"publicationDate":"2000-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79433050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2000-12-01DOI: 10.1002/1521-3838(200012)19:6<581::AID-QSAR581>3.0.CO;2-A
M. Nendza, Martin Müller
Environmental contaminants with common mode of toxic action (MOA) are generally expected to have similar structures and/or physico-chemical properties. Calculated descriptors of lipophilic, electronic and steric properties were used to cluster 115 test chemicals by MOA into nine different toxicant classes (non-polar non-specific toxicants, polar non-specific toxicants, uncouplers of oxidative phosphorylation, inhibitors of photosynthesis, inhibitors of acetylcholinesterase, inhibitors of respiration, thiol-alkylating agents, reactives (irritants), estrogenic compounds). Stepwise discriminant analysis of the test chemicals resulted in 89.6% correct classifications into the MOA classes. The final model uses 10 significant variables (log KOW, eHOMO, V+, QAV, HMAX+, MR, MW, DEFF, SASA, SAVOL). PLS discriminant analysis of the same data set resulted in a three-component model with r=0.89; the variables with the highest discriminatory power are log KOW, HMAX+, DEFF and QAV. Each MOA class reveals a characteristic profile in physico-chemical properties. Deviations relative to non-specific baseline toxicants are specific for each MOA class and reflect the structural dependences of the rate-limiting interactions that are causing the respective toxicities (functional similarity). By combining physiological and chemical knowledge about underlying processes, it is possible to indicate descriptor-based discrimination criteria by MOA as an essential prerequisite for rational selection and application of process-related QSARS for predictive purposes.
{"title":"Discriminating Toxicant Classes by Mode of Action: 2. Physico‐Chemical Descriptors","authors":"M. Nendza, Martin Müller","doi":"10.1002/1521-3838(200012)19:6<581::AID-QSAR581>3.0.CO;2-A","DOIUrl":"https://doi.org/10.1002/1521-3838(200012)19:6<581::AID-QSAR581>3.0.CO;2-A","url":null,"abstract":"Environmental contaminants with common mode of toxic action (MOA) are generally expected to have similar structures and/or physico-chemical properties. Calculated descriptors of lipophilic, electronic and steric properties were used to cluster 115 test chemicals by MOA into nine different toxicant classes (non-polar non-specific toxicants, polar non-specific toxicants, uncouplers of oxidative phosphorylation, inhibitors of photosynthesis, inhibitors of acetylcholinesterase, inhibitors of respiration, thiol-alkylating agents, reactives (irritants), estrogenic compounds). Stepwise discriminant analysis of the test chemicals resulted in 89.6% correct classifications into the MOA classes. The final model uses 10 significant variables (log KOW, eHOMO, V+, QAV, HMAX+, MR, MW, DEFF, SASA, SAVOL). PLS discriminant analysis of the same data set resulted in a three-component model with r=0.89; the variables with the highest discriminatory power are log KOW, HMAX+, DEFF and QAV. Each MOA class reveals a characteristic profile in physico-chemical properties. Deviations relative to non-specific baseline toxicants are specific for each MOA class and reflect the structural dependences of the rate-limiting interactions that are causing the respective toxicities (functional similarity). By combining physiological and chemical knowledge about underlying processes, it is possible to indicate descriptor-based discrimination criteria by MOA as an essential prerequisite for rational selection and application of process-related QSARS for predictive purposes.","PeriodicalId":20818,"journal":{"name":"Quantitative Structure-activity Relationships","volume":"33 1","pages":"581-598"},"PeriodicalIF":0.0,"publicationDate":"2000-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90568061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2000-12-01DOI: 10.1002/1521-3838(200012)19:6<565::AID-QSAR565>3.0.CO;2-2
D. Streich, Margareta Neuburger‐Zehnder, A. Vedani
{"title":"Induced Fit—The Key for Understanding LSD Activity? A 4D‐QSAR Study on the 5‐HT2A Receptor System","authors":"D. Streich, Margareta Neuburger‐Zehnder, A. Vedani","doi":"10.1002/1521-3838(200012)19:6<565::AID-QSAR565>3.0.CO;2-2","DOIUrl":"https://doi.org/10.1002/1521-3838(200012)19:6<565::AID-QSAR565>3.0.CO;2-2","url":null,"abstract":"","PeriodicalId":20818,"journal":{"name":"Quantitative Structure-activity Relationships","volume":"158 1","pages":"565-573"},"PeriodicalIF":0.0,"publicationDate":"2000-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76887381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2000-12-01DOI: 10.1002/1521-3838(200012)19:5<443::AID-QSAR443>3.0.CO;2-N
Y. Chumakov, A. Terletskaya, A. Dimoglo, S. Andronati
{"title":"The Electron‐Conformational Approach to QSAR Study in Series of Benzodiazepine Derivatives","authors":"Y. Chumakov, A. Terletskaya, A. Dimoglo, S. Andronati","doi":"10.1002/1521-3838(200012)19:5<443::AID-QSAR443>3.0.CO;2-N","DOIUrl":"https://doi.org/10.1002/1521-3838(200012)19:5<443::AID-QSAR443>3.0.CO;2-N","url":null,"abstract":"","PeriodicalId":20818,"journal":{"name":"Quantitative Structure-activity Relationships","volume":"49 1","pages":"443-447"},"PeriodicalIF":0.0,"publicationDate":"2000-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84461359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2000-10-01DOI: 10.1002/1521-3838(200010)19:4<356::AID-QSAR356>3.0.CO;2-I
P. S. Magee
{"title":"Exploring the Potential for Allergic Contact Dermatitisvia Computed Heats of Reaction of Haptens with Protein End-groups Heats of Reaction of Haptens with Protein End-groups by Computation","authors":"P. S. Magee","doi":"10.1002/1521-3838(200010)19:4<356::AID-QSAR356>3.0.CO;2-I","DOIUrl":"https://doi.org/10.1002/1521-3838(200010)19:4<356::AID-QSAR356>3.0.CO;2-I","url":null,"abstract":"","PeriodicalId":20818,"journal":{"name":"Quantitative Structure-activity Relationships","volume":"32 1","pages":"356-365"},"PeriodicalIF":0.0,"publicationDate":"2000-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88172615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2000-10-01DOI: 10.1002/1521-3838(200010)19:4<345::AID-QSAR345>3.0.CO;2-Q
L. Eriksson, E. Johansson, F. Lindgren, S. Wold
This paper introduces to the QSAR community a novel method for modeling and understanding non-linear relationships between biological potency and chemical structure properties of molecules. The approach, GIFI-PLS, is based on ``binning'' of quantitative X-variables into categorical variables. Each categorical variable is then expanded into a set of linked 1/0 dummy variables, which enable modeling of non-linearity. By way of four QSAR data sets, it is demonstrated that GIFI-PLS is useful for modeling of non-linearity and discontinuity in QSAR, and that the predictive power of a QSAR model may improve.
{"title":"GIFI‐PLS: Modeling of Non‐Linearities and Discontinuities in QSAR","authors":"L. Eriksson, E. Johansson, F. Lindgren, S. Wold","doi":"10.1002/1521-3838(200010)19:4<345::AID-QSAR345>3.0.CO;2-Q","DOIUrl":"https://doi.org/10.1002/1521-3838(200010)19:4<345::AID-QSAR345>3.0.CO;2-Q","url":null,"abstract":"This paper introduces to the QSAR community a novel method for modeling and understanding non-linear relationships between biological potency and chemical structure properties of molecules. The approach, GIFI-PLS, is based on ``binning'' of quantitative X-variables into categorical variables. Each categorical variable is then expanded into a set of linked 1/0 dummy variables, which enable modeling of non-linearity. By way of four QSAR data sets, it is demonstrated that GIFI-PLS is useful for modeling of non-linearity and discontinuity in QSAR, and that the predictive power of a QSAR model may improve.","PeriodicalId":20818,"journal":{"name":"Quantitative Structure-activity Relationships","volume":"23 1","pages":"345-355"},"PeriodicalIF":0.0,"publicationDate":"2000-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88968644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2000-10-01DOI: 10.1002/1521-3838(200010)19:4<366::AID-QSAR366>3.0.CO;2-E
O. Raevsky, V. Fetisov, E. P. Trepalina, J. McFarland, K. Schaper
{"title":"Quantitative Estimation of Drug Absorption in Humans for Passively Transported Compounds on the Basis of Their Physico‐chemical Parameters","authors":"O. Raevsky, V. Fetisov, E. P. Trepalina, J. McFarland, K. Schaper","doi":"10.1002/1521-3838(200010)19:4<366::AID-QSAR366>3.0.CO;2-E","DOIUrl":"https://doi.org/10.1002/1521-3838(200010)19:4<366::AID-QSAR366>3.0.CO;2-E","url":null,"abstract":"","PeriodicalId":20818,"journal":{"name":"Quantitative Structure-activity Relationships","volume":"26-27 1","pages":"366-374"},"PeriodicalIF":0.0,"publicationDate":"2000-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78307124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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