Pub Date : 2001-05-01DOI: 10.1002/1521-3838(200105)20:1<3::AID-QSAR3>3.0.CO;2-H
A. Bassoli, M. Drew, Channa K. Hattotuwagama, L. Merlini, G. Morini, Gareth R. H. Wilden
Isovanillyl derivatives constitute a large class of sweet compounds in which there is a high degree of structural similarity and a wide range of biological activity, the relative sweetness RS spanning from 50 to 10 000 times with respect to sucrose. This paper describes the results obtained by applying statistical models to develop QSARs for these derivatives. For a set of 14 compounds (set 1) appropriate physicochemical parameters for regression equations were selected using the genetic algorithm method. The best equation indicates a very close correlation (N=14, ND=5, r2=0.982, Rcv2=0.942, LOF=0.074, PRESS=0.271, SPRESS=0.184, SDEP=0.139). Good results have also been obtained by Molecular Field Analysis (MFA) applied to the same set of compounds (N=14, ND=4, r2=0.957, rcv2=0.925, LOF=0.044, PRESS=0.348, SPRESS=0.196, SDEP=0.158). QSARs have also been derived for a larger set of 41 compounds (set 2, including set 1, plus other 27 compounds) with a much larger variety of structural types. These compounds have been divided into a training set of 35 compounds and a test set of 6 compounds. The most significant QSAR obtained using physicochemical parameters (N=35, ND=6, r2=0.673, rcv2=0.522, LOF 0.337, PRESS=7.432, SPRESS=0.515, SDEP=0.461) proved less successful than one using MFA parameters (N=35, ND=6, r2=0.746, rcv2=0.607, LOF 0.261, PRESS=6.110, SPRESS=0.467, SDEP=0.418). PRESS values for the test set were 4.079 and 1.962 respectively showing that the MFA data had more predictive power. Equations with different numbers of descriptors were compared and it was concluded that the LOF which is dependent upon the number of parameters used as well as the sum of squares is a suitable measure of equation quality. These equations were also validated by scrambling the experimental data which gave significantly worse agreement than the real data except when an excessive number of descriptors was used.
{"title":"Quantitative Structure-Activity Relationships of Sweet Isovanillyl Derivatives","authors":"A. Bassoli, M. Drew, Channa K. Hattotuwagama, L. Merlini, G. Morini, Gareth R. H. Wilden","doi":"10.1002/1521-3838(200105)20:1<3::AID-QSAR3>3.0.CO;2-H","DOIUrl":"https://doi.org/10.1002/1521-3838(200105)20:1<3::AID-QSAR3>3.0.CO;2-H","url":null,"abstract":"Isovanillyl derivatives constitute a large class of sweet compounds in which there is a high degree of structural similarity and a wide range of biological activity, the relative sweetness RS spanning from 50 to 10 000 times with respect to sucrose. This paper describes the results obtained by applying statistical models to develop QSARs for these derivatives. For a set of 14 compounds (set 1) appropriate physicochemical parameters for regression equations were selected using the genetic algorithm method. The best equation indicates a very close correlation (N=14, ND=5, r2=0.982, Rcv2=0.942, LOF=0.074, PRESS=0.271, SPRESS=0.184, SDEP=0.139). Good results have also been obtained by Molecular Field Analysis (MFA) applied to the same set of compounds (N=14, ND=4, r2=0.957, rcv2=0.925, LOF=0.044, PRESS=0.348, SPRESS=0.196, SDEP=0.158). QSARs have also been derived for a larger set of 41 compounds (set 2, including set 1, plus other 27 compounds) with a much larger variety of structural types. These compounds have been divided into a training set of 35 compounds and a test set of 6 compounds. The most significant QSAR obtained using physicochemical parameters (N=35, ND=6, r2=0.673, rcv2=0.522, LOF 0.337, PRESS=7.432, SPRESS=0.515, SDEP=0.461) proved less successful than one using MFA parameters (N=35, ND=6, r2=0.746, rcv2=0.607, LOF 0.261, PRESS=6.110, SPRESS=0.467, SDEP=0.418). PRESS values for the test set were 4.079 and 1.962 respectively showing that the MFA data had more predictive power. Equations with different numbers of descriptors were compared and it was concluded that the LOF which is dependent upon the number of parameters used as well as the sum of squares is a suitable measure of equation quality. These equations were also validated by scrambling the experimental data which gave significantly worse agreement than the real data except when an excessive number of descriptors was used.","PeriodicalId":20818,"journal":{"name":"Quantitative Structure-activity Relationships","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2001-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87519831","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 : 2001-05-01DOI: 10.1002/1521-3838(200105)20:1<17::AID-QSAR17>3.0.CO;2-X
P. Fossa, G. Menozzi, L. Mosti
Preclinical and clinical studies on cyclic nucleotide phosphodiesterases 4 (PDE4) inhibitors showed that these agents might be employed in the treatment of allergic diseases, in particular asthma. Unfortunately, many of these compounds such as rolipram, which belongs to the so-called “first generation”, showed undesirable side effects such as nausea and emesis. Efforts to eliminate these adverse side effects prompted the synthesis of a “second generation” of PDE4 inhibitors, with improved selectivity towards the enzyme catalytic site. So as to refine the pharmacophoric models of the catalytic site previously described in literature and better define the structural requirements which are essential for potent and selective PDE4 inhibition, we undertook the present computational study. DISCO approach was applied to generate an optimal alignment for a set of structurally diverse selective inhibitors 1–18 chosen from literature. The resulting superimposition of common pharmacophoric elements was refined by evaluating molecular field properties. A rational pharmacophoric model of the enzyme active site was thus derived and tested for its ability in predicting the degree of potency for a novel ligand. The comparison of the pharmacophoric areas common to cAMP, the natural substrate of the enzyme, and the most selective inhibitors was performed so as to better understand the binding mode of PDE4 selective inhibitors in the catalytic site.
{"title":"An Updated Topographical Model for Phosphodiesterase 4 (PDE4) Catalytic Site","authors":"P. Fossa, G. Menozzi, L. Mosti","doi":"10.1002/1521-3838(200105)20:1<17::AID-QSAR17>3.0.CO;2-X","DOIUrl":"https://doi.org/10.1002/1521-3838(200105)20:1<17::AID-QSAR17>3.0.CO;2-X","url":null,"abstract":"Preclinical and clinical studies on cyclic nucleotide phosphodiesterases 4 (PDE4) inhibitors showed that these agents might be employed in the treatment of allergic diseases, in particular asthma. Unfortunately, many of these compounds such as rolipram, which belongs to the so-called “first generation”, showed undesirable side effects such as nausea and emesis. Efforts to eliminate these adverse side effects prompted the synthesis of a “second generation” of PDE4 inhibitors, with improved selectivity towards the enzyme catalytic site. So as to refine the pharmacophoric models of the catalytic site previously described in literature and better define the structural requirements which are essential for potent and selective PDE4 inhibition, we undertook the present computational study. DISCO approach was applied to generate an optimal alignment for a set of structurally diverse selective inhibitors 1–18 chosen from literature. The resulting superimposition of common pharmacophoric elements was refined by evaluating molecular field properties. A rational pharmacophoric model of the enzyme active site was thus derived and tested for its ability in predicting the degree of potency for a novel ligand. The comparison of the pharmacophoric areas common to cAMP, the natural substrate of the enzyme, and the most selective inhibitors was performed so as to better understand the binding mode of PDE4 selective inhibitors in the catalytic site.","PeriodicalId":20818,"journal":{"name":"Quantitative Structure-activity Relationships","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2001-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75104255","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 : 2001-05-01DOI: 10.1002/1521-3838(200105)20:1<23::AID-QSAR23>3.0.CO;2-I
Y. Song, I. M. Coupar, M. Iskander
{"title":"Structural Predictions of Adenosine 2B Antagonist Affinity Using Molecular Field Analysis","authors":"Y. Song, I. M. Coupar, M. Iskander","doi":"10.1002/1521-3838(200105)20:1<23::AID-QSAR23>3.0.CO;2-I","DOIUrl":"https://doi.org/10.1002/1521-3838(200105)20:1<23::AID-QSAR23>3.0.CO;2-I","url":null,"abstract":"","PeriodicalId":20818,"journal":{"name":"Quantitative Structure-activity Relationships","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2001-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72705146","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 : 2001-05-01DOI: 10.1002/1521-3838(200105)20:1<46::AID-QSAR46>3.0.CO;2-X
Klaus-Jürgen Schaper, Huabei Zhang, O. Raevsky
The partitioning of drugs in the phospholipid liposome/water system has often been used as a model for the investigation of drug/cell membrane interactions. Generally distribution/pH profiles for acidic and basic compounds observed in the phospholipid/water system cannot be predicted by profiles obtained in the octanol/water system. This is true especially for pH regions where acids and bases are ionized. Based on the well-known pH-partition theory our nonlinear regression analysis of published pH-dependent liposome-water distribution coefficients of 18 basic and acidic drugs resulted in equations with satisfactory predictive power. Surprisingly the separate equations describing the partitioning of neutral, anionic or cationic drugs into phosphatidylcholine liposomes are rather similar with respect to significant physicochemical properties and their regression coefficients. Despite being differently charged the distribution of all three species into the phospholipid bilayer increases with size/bulk (polarizability α) and decreases with polarity characterized by the sum of substructural partial charges. Furthermore, for the neutral compounds and their cations an unfavorable effect of H-bond acceptor strength on distribution is found. Unexpectedly the H-bond donor effect of neutral or ionized species does not seem to influence their liposome partitioning behavior (as observed within each group of neutral, cationic or anionic drugs). Generally the distribution of ionizable molecules into phospholipid bilayers decreases with increasing degree of ionization. In this data set it was found for the first time that specific N-H-acidic drugs may show the opposite behavior. Obviously anions with a delocalized negative charge like anions of tautomeric pyrimidinones/pyrimidinols have the possibility to enter into some strong special interaction with phospholipid molecules.
{"title":"pH‐Dependent Partitioning of Acidic and Basic Drugs into Liposomes—A Quantitative Structure‐Activity Relationship Analysis","authors":"Klaus-Jürgen Schaper, Huabei Zhang, O. Raevsky","doi":"10.1002/1521-3838(200105)20:1<46::AID-QSAR46>3.0.CO;2-X","DOIUrl":"https://doi.org/10.1002/1521-3838(200105)20:1<46::AID-QSAR46>3.0.CO;2-X","url":null,"abstract":"The partitioning of drugs in the phospholipid liposome/water system has often been used as a model for the investigation of drug/cell membrane interactions. Generally distribution/pH profiles for acidic and basic compounds observed in the phospholipid/water system cannot be predicted by profiles obtained in the octanol/water system. This is true especially for pH regions where acids and bases are ionized. Based on the well-known pH-partition theory our nonlinear regression analysis of published pH-dependent liposome-water distribution coefficients of 18 basic and acidic drugs resulted in equations with satisfactory predictive power. Surprisingly the separate equations describing the partitioning of neutral, anionic or cationic drugs into phosphatidylcholine liposomes are rather similar with respect to significant physicochemical properties and their regression coefficients. Despite being differently charged the distribution of all three species into the phospholipid bilayer increases with size/bulk (polarizability α) and decreases with polarity characterized by the sum of substructural partial charges. Furthermore, for the neutral compounds and their cations an unfavorable effect of H-bond acceptor strength on distribution is found. Unexpectedly the H-bond donor effect of neutral or ionized species does not seem to influence their liposome partitioning behavior (as observed within each group of neutral, cationic or anionic drugs). Generally the distribution of ionizable molecules into phospholipid bilayers decreases with increasing degree of ionization. In this data set it was found for the first time that specific N-H-acidic drugs may show the opposite behavior. Obviously anions with a delocalized negative charge like anions of tautomeric pyrimidinones/pyrimidinols have the possibility to enter into some strong special interaction with phospholipid molecules.","PeriodicalId":20818,"journal":{"name":"Quantitative Structure-activity Relationships","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2001-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87951063","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 : 2001-05-01DOI: 10.1002/1521-3838(200105)20:1<31::AID-QSAR31>3.0.CO;2-S
A. Dimoglo, N. Shvets, I. Tetko, D. Livingstone
“Structure – acetylcholinesterase (AChE) inhibitor activity” relationship studies have been performed for three series of N-benzylpiperidine derivatives using the Electronic-Topological Method (ETM) which is a structural approach designed for the investigation of structure-property relationships. Biological activities of the compounds belonging to three different series have been measured on mouse, human and Torpedo californica AChE. Molecular fragments that are only specific for active compounds (“activity features”) were found for each of these series. In a similar way, “breaks of activity” (i.e. molecular fragments that are typical of inactive compounds and cannot be a part of an active compound) were calculated by applying the ETM. Requirements necessary for a compound to be active are formulated; they are the result of a detailed analysis of all compounds under study. The analysis shows that any violation of these requirements for a molecule decreases considerably or even provokes a complete loss of its activity. A comparative study of the activity features found relative to three different AChE has also been performed.
{"title":"Electronic‐Topological Investigation of theStructure – Acetylcholinesterase Inhibitor Activity Relationship in the Series of N‐Benzylpiperidine Derivatives","authors":"A. Dimoglo, N. Shvets, I. Tetko, D. Livingstone","doi":"10.1002/1521-3838(200105)20:1<31::AID-QSAR31>3.0.CO;2-S","DOIUrl":"https://doi.org/10.1002/1521-3838(200105)20:1<31::AID-QSAR31>3.0.CO;2-S","url":null,"abstract":"“Structure – acetylcholinesterase (AChE) inhibitor activity” relationship studies have been performed for three series of N-benzylpiperidine derivatives using the Electronic-Topological Method (ETM) which is a structural approach designed for the investigation of structure-property relationships. Biological activities of the compounds belonging to three different series have been measured on mouse, human and Torpedo californica AChE. Molecular fragments that are only specific for active compounds (“activity features”) were found for each of these series. In a similar way, “breaks of activity” (i.e. molecular fragments that are typical of inactive compounds and cannot be a part of an active compound) were calculated by applying the ETM. Requirements necessary for a compound to be active are formulated; they are the result of a detailed analysis of all compounds under study. The analysis shows that any violation of these requirements for a molecule decreases considerably or even provokes a complete loss of its activity. A comparative study of the activity features found relative to three different AChE has also been performed.","PeriodicalId":20818,"journal":{"name":"Quantitative Structure-activity Relationships","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2001-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86081987","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<599::AID-QSAR599>3.0.CO;2-B
Daniel Oliveira, A. C. Gaudio
A new computer program called BuildQSAR has been designed to help the QSAR practitioner on the task of building and analyzing quantitative models through regression analysis. The main part of the program is a spreadsheet, in which the user can enter with the data set composed by the structure definition of the compounds, one or more types of biological activity values and many physicochemical properties. The program has an external data bank, which includes the values of many known substituent parameters. The common tasks that can be performed by the program are: (a) linear, quadratic and bilinear models; (b) graphics analysis; (c) correlation analysis and; (d) hypothesis testing. BuildQSAR can also: (e) build subsets of the data set that include groups of compounds chosen by the user; (f) create new variables, derived from the existing ones, and create many types of indicator variables; (g) build data sets directly from the data bank by selecting the substituents and the variables therein; (h) perform variable selection by systematic search and genetic algorithm; (i) perform cross-validation of specific models; (j) eliminate outliers and analyze the resulting model; and others. The program was coded in Delphi 5 and runs in Windows 95/98/00/NT environments.
{"title":"BuildQSAR: A New Computer Program for QSAR Analysis","authors":"Daniel Oliveira, A. C. Gaudio","doi":"10.1002/1521-3838(200012)19:6<599::AID-QSAR599>3.0.CO;2-B","DOIUrl":"https://doi.org/10.1002/1521-3838(200012)19:6<599::AID-QSAR599>3.0.CO;2-B","url":null,"abstract":"A new computer program called BuildQSAR has been designed to help the QSAR practitioner on the task of building and analyzing quantitative models through regression analysis. The main part of the program is a spreadsheet, in which the user can enter with the data set composed by the structure definition of the compounds, one or more types of biological activity values and many physicochemical properties. The program has an external data bank, which includes the values of many known substituent parameters. The common tasks that can be performed by the program are: (a) linear, quadratic and bilinear models; (b) graphics analysis; (c) correlation analysis and; (d) hypothesis testing. BuildQSAR can also: (e) build subsets of the data set that include groups of compounds chosen by the user; (f) create new variables, derived from the existing ones, and create many types of indicator variables; (g) build data sets directly from the data bank by selecting the substituents and the variables therein; (h) perform variable selection by systematic search and genetic algorithm; (i) perform cross-validation of specific models; (j) eliminate outliers and analyze the resulting model; and others. The program was coded in Delphi 5 and runs in Windows 95/98/00/NT environments.","PeriodicalId":20818,"journal":{"name":"Quantitative Structure-activity Relationships","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2000-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82200155","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<554::AID-QSAR554>3.0.CO;2-A
I. B. Golovanov, I. G. Tsygankova
{"title":"Estimation of Physicochemical Properties from the Structure‐Property Relationship: A New Approach","authors":"I. B. Golovanov, I. G. Tsygankova","doi":"10.1002/1521-3838(200012)19:6<554::AID-QSAR554>3.0.CO;2-A","DOIUrl":"https://doi.org/10.1002/1521-3838(200012)19:6<554::AID-QSAR554>3.0.CO;2-A","url":null,"abstract":"","PeriodicalId":20818,"journal":{"name":"Quantitative Structure-activity Relationships","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2000-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91266195","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<467::AID-QSAR467>3.0.CO;2-#
Jeffrey S. Smith, O. Macina, N. Sussman, M. Karol, H. Maibach
SAR model development should be a continuous process involving formulation then experimental testing of the model, incorporation of test results into the database, then refinement of the model using the expanded database. The larger database affords greater confidence in its ability to predict the biological response. This iterative procedure was employed with a recently developed structure-activity relationship (SAR) model of human skin irritation. Based on a “leave-one-out” cross validation, the mean sensitivity of the initial model was 0.89, the mean specificity was 0.74. A clinical validation study was conducted to assess the ability of the model to predict human skin irritation by esters commonly used as fragrance ingredients. Esters that were found to cause irritation in rabbits, and that were within the predictive space of the SAR model, were selected for human testing using the patch test procedure. Of the 34 rabbit irritants selected, 16 were predicted by the model to be positive and 18 were predicted to be negative. Patch testing yielded two positive esters, allyl heptanoate and allyl cyclohexanepropionate. These test results were incorporated into the database to refine the SAR model. Best subsets regression and linear discriminant analysis were used to generate 10 submodels consisting of 10 irritants and 50 non-irritants randomly selected from the new database. Physicochemical parameters associated with irritant esters, when compared with non-irritant esters, differed somewhat from those identified in the original model. Irritant esters had lower solubility parameter and water solubility, higher Hansen dispersion and Hansen hydrogen bonding, and lower sum of partial positive charges, when compared with non-irritant esters. The sensitivity of the new model is 0.69 and specificity is 0.67. The results of this study indicate that SAR models based on limited data may not accurately predict the activity of unknown chemicals even though the computationally-derived sensitivity and specificity of the models are high. This finding emphasizes the need for experimental validation of models and their refinement as new data become available.
{"title":"Experimental Validation of a Structure–Activity Relationship Model of Skin Irritation by Esters","authors":"Jeffrey S. Smith, O. Macina, N. Sussman, M. Karol, H. Maibach","doi":"10.1002/1521-3838(200012)19:5<467::AID-QSAR467>3.0.CO;2-#","DOIUrl":"https://doi.org/10.1002/1521-3838(200012)19:5<467::AID-QSAR467>3.0.CO;2-#","url":null,"abstract":"SAR model development should be a continuous process involving formulation then experimental testing of the model, incorporation of test results into the database, then refinement of the model using the expanded database. The larger database affords greater confidence in its ability to predict the biological response. This iterative procedure was employed with a recently developed structure-activity relationship (SAR) model of human skin irritation. Based on a “leave-one-out” cross validation, the mean sensitivity of the initial model was 0.89, the mean specificity was 0.74. A clinical validation study was conducted to assess the ability of the model to predict human skin irritation by esters commonly used as fragrance ingredients. Esters that were found to cause irritation in rabbits, and that were within the predictive space of the SAR model, were selected for human testing using the patch test procedure. Of the 34 rabbit irritants selected, 16 were predicted by the model to be positive and 18 were predicted to be negative. Patch testing yielded two positive esters, allyl heptanoate and allyl cyclohexanepropionate. These test results were incorporated into the database to refine the SAR model. Best subsets regression and linear discriminant analysis were used to generate 10 submodels consisting of 10 irritants and 50 non-irritants randomly selected from the new database. Physicochemical parameters associated with irritant esters, when compared with non-irritant esters, differed somewhat from those identified in the original model. Irritant esters had lower solubility parameter and water solubility, higher Hansen dispersion and Hansen hydrogen bonding, and lower sum of partial positive charges, when compared with non-irritant esters. The sensitivity of the new model is 0.69 and specificity is 0.67. The results of this study indicate that SAR models based on limited data may not accurately predict the activity of unknown chemicals even though the computationally-derived sensitivity and specificity of the models are high. This finding emphasizes the need for experimental validation of models and their refinement as new data become available.","PeriodicalId":20818,"journal":{"name":"Quantitative Structure-activity Relationships","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2000-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84335166","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<455::AID-QSAR455>3.0.CO;2-B
H. Chuman, S. Goto, M. Karasawa, M. Sasaki, U. Nagashima, K. Nishimura, T. Fujita
{"title":"Three-dimensional structure-activity relationships of synthetic pyrethroids: 2. Three-dimensional and classical QSAR studies.","authors":"H. Chuman, S. Goto, M. Karasawa, M. Sasaki, U. Nagashima, K. Nishimura, T. Fujita","doi":"10.1002/1521-3838(200012)19:5<455::AID-QSAR455>3.0.CO;2-B","DOIUrl":"https://doi.org/10.1002/1521-3838(200012)19:5<455::AID-QSAR455>3.0.CO;2-B","url":null,"abstract":"","PeriodicalId":20818,"journal":{"name":"Quantitative Structure-activity Relationships","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2000-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81219271","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<448::AID-QSAR448>3.0.CO;2-3
C. Quiñones, Joaquín Caceres, M. Stud, Ana Martínez
The CODES/neural network model has been successfully applied to the prediction of pharmacokinetic properties of therapeutical compounds. The output of CODES, a graphical module based on the Gestalt isomorphism, is proved to be a valuable tool in the design of a neural network model able to predict the half-life values of antihistamines. Additionally, the generated models are able to classify these drugs in their corresponding therapeutic category (H1 or H2 receptor antagonists).
{"title":"Prediction of Drug Half-life Values of Antihistamines Based on the CODES/Neural Network Model","authors":"C. Quiñones, Joaquín Caceres, M. Stud, Ana Martínez","doi":"10.1002/1521-3838(200012)19:5<448::AID-QSAR448>3.0.CO;2-3","DOIUrl":"https://doi.org/10.1002/1521-3838(200012)19:5<448::AID-QSAR448>3.0.CO;2-3","url":null,"abstract":"The CODES/neural network model has been successfully applied to the prediction of pharmacokinetic properties of therapeutical compounds. The output of CODES, a graphical module based on the Gestalt isomorphism, is proved to be a valuable tool in the design of a neural network model able to predict the half-life values of antihistamines. Additionally, the generated models are able to classify these drugs in their corresponding therapeutic category (H1 or H2 receptor antagonists).","PeriodicalId":20818,"journal":{"name":"Quantitative Structure-activity Relationships","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2000-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75620982","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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