Pub Date : 2001-10-01DOI: 10.1002/1521-3838(200110)20:3<215::AID-QSAR215>3.0.CO;2-9
I. Muegge, B. Podlogar
A series of CoMFA models have been derived from docking-based and atom-based alignments. The statistics of these approaches has been compared to determine whether a docking approach can be employed as an automated alignment tool for the development of 3D-QSAR models. Using a well-characterized training set of 51 biphenyl carboxylic acid MMP-3 inhibitors, the docking-based alignment provided by a DOCK4/PMF-scoring protocol has yielded statistically significant, cross-validated CoMFA models comparable to those derived with a traditional atom-based alignment technique. Field fit minimization has been applied to refine the atom-based and docking-based alignments. The refinement appears to be beneficial for the docking-based approach. For the atom-based alignment, however, field-fit refinement has not resulted in improved CoMFA models. The statistically best CoMFA model has been created by the atom-based alignment that has been found, however, to be inconsistent with the stromelysin crystal structure. The docking alignment refined by field-fit alignment has resulted in a final alignment that is consistent with the crystal structure and only slightly statistically inferior to the atom-based aligned CoMFA model. The results show␣the ability of an automated docking/field-fit alignment technique to provide self-consistent CoMFA alignments.
{"title":"3D-Quantitative Structure Activity Relationships of Biphenyl Carboxylic Acid MMP-3 Inhibitors: Exploring Automated Docking as Alignment Method","authors":"I. Muegge, B. Podlogar","doi":"10.1002/1521-3838(200110)20:3<215::AID-QSAR215>3.0.CO;2-9","DOIUrl":"https://doi.org/10.1002/1521-3838(200110)20:3<215::AID-QSAR215>3.0.CO;2-9","url":null,"abstract":"A series of CoMFA models have been derived from docking-based and atom-based alignments. The statistics of these approaches has been compared to determine whether a docking approach can be employed as an automated alignment tool for the development of 3D-QSAR models. Using a well-characterized training set of 51 biphenyl carboxylic acid MMP-3 inhibitors, the docking-based alignment provided by a DOCK4/PMF-scoring protocol has yielded statistically significant, cross-validated CoMFA models comparable to those derived with a traditional atom-based alignment technique. Field fit minimization has been applied to refine the atom-based and docking-based alignments. The refinement appears to be beneficial for the docking-based approach. For the atom-based alignment, however, field-fit refinement has not resulted in improved CoMFA models. The statistically best CoMFA model has been created by the atom-based alignment that has been found, however, to be inconsistent with the stromelysin crystal structure. The docking alignment refined by field-fit alignment has resulted in a final alignment that is consistent with the crystal structure and only slightly statistically inferior to the atom-based aligned CoMFA model. The results show␣the ability of an automated docking/field-fit alignment technique to provide self-consistent CoMFA alignments.","PeriodicalId":20818,"journal":{"name":"Quantitative Structure-activity Relationships","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2001-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84748402","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 : 2001-10-01DOI: 10.1002/1521-3838(200110)20:3<223::AID-QSAR223>3.0.CO;2-D
Ş. Niculescu, K. Kaiser
{"title":"Modeling the Relative Binding Affinity of Steroids to the Progesterone Receptor with Probabilistic Neural Networks","authors":"Ş. Niculescu, K. Kaiser","doi":"10.1002/1521-3838(200110)20:3<223::AID-QSAR223>3.0.CO;2-D","DOIUrl":"https://doi.org/10.1002/1521-3838(200110)20:3<223::AID-QSAR223>3.0.CO;2-D","url":null,"abstract":"","PeriodicalId":20818,"journal":{"name":"Quantitative Structure-activity Relationships","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2001-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89189462","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 : 2001-10-01DOI: 10.1002/1521-3838(200110)20:3<227::AID-QSAR227>3.0.CO;2-Y
R. Czerminski, A. Yasri, D. Hartsough
The Support Vector Machine (SVM) approach for classification and regression problems was originally developed by Vapnik and co-workers [1]. For the last few years it has been gaining acceptance in the machine learning community [2]. The purpose of this paper is to evaluate SVM performance in the quantitative structure-activity relationship (QSAR) domain for classification applications and to compare the performance of one particular implementation of an SVM [3] to one particular implementation of an artificial neural network (ANN) [4]. For this purpose, we used artificial data simulating various response surfaces, and biological data derived from the literature covering various pharmacological domains. The results obtained on biological data are also compared to previous work using other modeling techniques. We also discuss the usage of SVM in building QSAR models for biological activity of drugs.
{"title":"Use of Support Vector Machine in Pattern Classification: Application to QSAR Studies","authors":"R. Czerminski, A. Yasri, D. Hartsough","doi":"10.1002/1521-3838(200110)20:3<227::AID-QSAR227>3.0.CO;2-Y","DOIUrl":"https://doi.org/10.1002/1521-3838(200110)20:3<227::AID-QSAR227>3.0.CO;2-Y","url":null,"abstract":"The Support Vector Machine (SVM) approach for classification and regression problems was originally developed by Vapnik and co-workers [1]. For the last few years it has been gaining acceptance in the machine learning community [2]. The purpose of this paper is to evaluate SVM performance in the quantitative structure-activity relationship (QSAR) domain for classification applications and to compare the performance of one particular implementation of an SVM [3] to one particular implementation of an artificial neural network (ANN) [4]. For this purpose, we used artificial data simulating various response surfaces, and biological data derived from the literature covering various pharmacological domains. The results obtained on biological data are also compared to previous work using other modeling techniques. We also discuss the usage of SVM in building QSAR models for biological activity of drugs.","PeriodicalId":20818,"journal":{"name":"Quantitative Structure-activity Relationships","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2001-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77904232","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 : 2001-10-01DOI: 10.1002/1521-3838(200110)20:3<241::AID-QSAR241>3.0.CO;2-D
H. Morais, C. Ramos, E. Forgács, A. Jakab, T. Cserháti, J. Oliviera, Tibor Illés, Z. Illés
{"title":"Three dimensional principal component analysis used for the study of enzyme kinetics. An empirical approximation for the determination of the dimensions of component matrices","authors":"H. Morais, C. Ramos, E. Forgács, A. Jakab, T. Cserháti, J. Oliviera, Tibor Illés, Z. Illés","doi":"10.1002/1521-3838(200110)20:3<241::AID-QSAR241>3.0.CO;2-D","DOIUrl":"https://doi.org/10.1002/1521-3838(200110)20:3<241::AID-QSAR241>3.0.CO;2-D","url":null,"abstract":"","PeriodicalId":20818,"journal":{"name":"Quantitative Structure-activity Relationships","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2001-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73653422","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 : 2001-07-01DOI: 10.1002/1521-3838(200107)20:2<130::AID-QSAR130>3.0.CO;2-6
I. Pajeva, M. Wiese
{"title":"Human P‐Glycoprotein Pseudoreceptor Modeling: 3D‐QSAR Study on Thioxanthene Type Multidrug Resistance Modulators","authors":"I. Pajeva, M. Wiese","doi":"10.1002/1521-3838(200107)20:2<130::AID-QSAR130>3.0.CO;2-6","DOIUrl":"https://doi.org/10.1002/1521-3838(200107)20:2<130::AID-QSAR130>3.0.CO;2-6","url":null,"abstract":"","PeriodicalId":20818,"journal":{"name":"Quantitative Structure-activity Relationships","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2001-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89248952","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 : 2001-07-01DOI: 10.1002/1521-3838(200107)20:2<124::AID-QSAR124>3.0.CO;2-V
I. Doytchinova, I. Valkova, R. Natcheva
{"title":"CoMFA Study on Adenosine A2A Receptor Agonists","authors":"I. Doytchinova, I. Valkova, R. Natcheva","doi":"10.1002/1521-3838(200107)20:2<124::AID-QSAR124>3.0.CO;2-V","DOIUrl":"https://doi.org/10.1002/1521-3838(200107)20:2<124::AID-QSAR124>3.0.CO;2-V","url":null,"abstract":"","PeriodicalId":20818,"journal":{"name":"Quantitative Structure-activity Relationships","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2001-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91539619","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 : 2001-07-01DOI: 10.1002/1521-3838(200107)20:2<115::AID-QSAR115>3.0.CO;2-V
E. Jacoby
In the frame of the discussion of monoamine-related GPCRs, a novel knowledge-based ligand design strategy is presented. The strategy is founded on the integration of both, the deconvolution of known ligands into their component fragments and the structural bioinformatics comparison of the binding sites for the individual ligand fragments. Positioning analyses of monoamine-related GPCRs in 1) the sequence space of the seven transmembrane domains of the receptors, and 2) in the sequence spaces of the previously identified three distinct ligand fragment binding regions of the monoamine GPCRs, are carried out in the perspective to characterize orphan receptors and monoamine receptors for which no specific ligands are yet known. Compared to the commonly accepted strategy to analyze the overall sequence identity of the seven transmembrane domains in order to find starting points for lead finding and ligand design programs, the strategy to localize the sequence homology to the different ligand fragment binding sites clearly enhances the identification of putative similarities for the orphan receptors. Correspondingly, in the ligand space, by the analysis of both, the ligand architectures and the structures of the component “one-site filling” fragments of known GPCR ligands, it is then possible, by referring to the locally most directly related and characterized receptors, to identify those component ligand fragments which based on the binding site similarities are potentially best suited for the design of ligands tailored to the new target receptor. Predictions are made for several orphan GPCRs, including GPR7, GPR8, GPR14, GPR24, GPR57, GPR58 and AF021818, as well as for the 5HT1E and 5HT5 serotonin receptors for which no specific agonists and antagonists are yet known. Although the method is herein discussed with a focus on GPCRs, it is expected that such chemogenomics knowledge-based strategies – bridging the chem- and bioinformatics worlds – should open novel perspectives in drug discovery for orphan targets revealed by the human genome project belonging to other therapeutic target families.
{"title":"A Novel Chemogenomics Knowledge-Based Ligand Design Strategy—Application to G Protein-Coupled Receptors","authors":"E. Jacoby","doi":"10.1002/1521-3838(200107)20:2<115::AID-QSAR115>3.0.CO;2-V","DOIUrl":"https://doi.org/10.1002/1521-3838(200107)20:2<115::AID-QSAR115>3.0.CO;2-V","url":null,"abstract":"In the frame of the discussion of monoamine-related GPCRs, a novel knowledge-based ligand design strategy is presented. The strategy is founded on the integration of both, the deconvolution of known ligands into their component fragments and the structural bioinformatics comparison of the binding sites for the individual ligand fragments. Positioning analyses of monoamine-related GPCRs in 1) the sequence space of the seven transmembrane domains of the receptors, and 2) in the sequence spaces of the previously identified three distinct ligand fragment binding regions of the monoamine GPCRs, are carried out in the perspective to characterize orphan receptors and monoamine receptors for which no specific ligands are yet known. Compared to the commonly accepted strategy to analyze the overall sequence identity of the seven transmembrane domains in order to find starting points for lead finding and ligand design programs, the strategy to localize the sequence homology to the different ligand fragment binding sites clearly enhances the identification of putative similarities for the orphan receptors. Correspondingly, in the ligand space, by the analysis of both, the ligand architectures and the structures of the component “one-site filling” fragments of known GPCR ligands, it is then possible, by referring to the locally most directly related and characterized receptors, to identify those component ligand fragments which based on the binding site similarities are potentially best suited for the design of ligands tailored to the new target receptor. Predictions are made for several orphan GPCRs, including GPR7, GPR8, GPR14, GPR24, GPR57, GPR58 and AF021818, as well as for the 5HT1E and 5HT5 serotonin receptors for which no specific agonists and antagonists are yet known. Although the method is herein discussed with a focus on GPCRs, it is expected that such chemogenomics knowledge-based strategies – bridging the chem- and bioinformatics worlds – should open novel perspectives in drug discovery for orphan targets revealed by the human genome project belonging to other therapeutic target families.","PeriodicalId":20818,"journal":{"name":"Quantitative Structure-activity Relationships","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2001-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78719473","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 : 2001-07-01DOI: 10.1002/1521-3838(200107)20:2<143::AID-QSAR143>3.0.CO;2-R
K. Gohda, D. Ohta, A. Kozaki, K. Fujimori, I. Mori, T. Kikuchi
We identified new potent inhibitors for ATP-phosphoribosyl transferase, which is the first enzyme in histidine biosynthesis pathway, using three-dimensional database search (3D-search) technique. The 3D-search was based on the structure of product molecule, N-1-(5′-phosphoribosyl)-ATP, as a template to find molecules targeting to the binding sites of two substrates (ATP and 5′-phosphoribosyl-1-pyrophosphate), i.e., bi-substrate mimicking. Four commercially-available compounds with three different chemical classes were examined out of 36 low-molecular weight compounds selected from the hits of the searches. Amino-(chlorophenyl)-triazolopyrimidine compounds, which are the simplest and smallest ones, showed potent activity (e.g., 92% inhibition at 100 μM). The structural comparison with the product molecule suggests that the simultaneous occupation of two substrate-binding sites likely enhances the enzyme inhibition. The most potent compound examined in this study was a disulfide-bond containing molecule (IC50=50 nM), whose mode of action seems to be different from the others. Further studies using its derivatives were carried out for clarification.
{"title":"Identification of Novel Potent Inhibitors for ATP‐Phosphoribosyl Transferase Using Three‐Dimensional Structural Database Search Technique","authors":"K. Gohda, D. Ohta, A. Kozaki, K. Fujimori, I. Mori, T. Kikuchi","doi":"10.1002/1521-3838(200107)20:2<143::AID-QSAR143>3.0.CO;2-R","DOIUrl":"https://doi.org/10.1002/1521-3838(200107)20:2<143::AID-QSAR143>3.0.CO;2-R","url":null,"abstract":"We identified new potent inhibitors for ATP-phosphoribosyl transferase, which is the first enzyme in histidine biosynthesis pathway, using three-dimensional database search (3D-search) technique. The 3D-search was based on the structure of product molecule, N-1-(5′-phosphoribosyl)-ATP, as a template to find molecules targeting to the binding sites of two substrates (ATP and 5′-phosphoribosyl-1-pyrophosphate), i.e., bi-substrate mimicking. Four commercially-available compounds with three different chemical classes were examined out of 36 low-molecular weight compounds selected from the hits of the searches. Amino-(chlorophenyl)-triazolopyrimidine compounds, which are the simplest and smallest ones, showed potent activity (e.g., 92% inhibition at 100 μM). The structural comparison with the product molecule suggests that the simultaneous occupation of two substrate-binding sites likely enhances the enzyme inhibition. The most potent compound examined in this study was a disulfide-bond containing molecule (IC50=50 nM), whose mode of action seems to be different from the others. Further studies using its derivatives were carried out for clarification.","PeriodicalId":20818,"journal":{"name":"Quantitative Structure-activity Relationships","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2001-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82713694","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 : 2001-07-01DOI: 10.1002/1521-3838(200107)20:2<139::AID-QSAR139>3.0.CO;2-7
You-min Sun, X. Wang, Hong-yu Zhang, Dezhan Chen
Thiazolidinone Derivatives (TD) are a novel class of calcium ion (Ca2+) antagonists possessing both Ca2+ overload inhibition and antioxidant activity. The free radical scavenging activity of TD play a key role in its cardioprotective processes. To elucidate the structure-antioxidant activity relationships (SAAR) for TD, a series of phenolic analogues of TD were constructed by adding various substituents to phenol step by step. And the theoretical parameter characterizing the free radical scavenging activity, O–H bond dissociation energy (BDE), was calculated for these phenols by quantum chemical method AM1/B3LYP/6-31G**. Thus, the contribution of each substituent to the O–H BDE was obtained. As a result, not only the SAAR for TD was explained, but also the understanding on TD's antioxidative mechanism was deepened.
{"title":"Theoretical Elucidation of Structure-Antioxidant Activity Relationships for Thiazolidinone Derivatives","authors":"You-min Sun, X. Wang, Hong-yu Zhang, Dezhan Chen","doi":"10.1002/1521-3838(200107)20:2<139::AID-QSAR139>3.0.CO;2-7","DOIUrl":"https://doi.org/10.1002/1521-3838(200107)20:2<139::AID-QSAR139>3.0.CO;2-7","url":null,"abstract":"Thiazolidinone Derivatives (TD) are a novel class of calcium ion (Ca2+) antagonists possessing both Ca2+ overload inhibition and antioxidant activity. The free radical scavenging activity of TD play a key role in its cardioprotective processes. To elucidate the structure-antioxidant activity relationships (SAAR) for TD, a series of phenolic analogues of TD were constructed by adding various substituents to phenol step by step. And the theoretical parameter characterizing the free radical scavenging activity, O–H bond dissociation energy (BDE), was calculated for these phenols by quantum chemical method AM1/B3LYP/6-31G**. Thus, the contribution of each substituent to the O–H BDE was obtained. As a result, not only the SAAR for TD was explained, but also the understanding on TD's antioxidative mechanism was deepened.","PeriodicalId":20818,"journal":{"name":"Quantitative Structure-activity Relationships","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2001-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78326085","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 : 2001-07-01DOI: 10.1002/1521-3838(200107)20:2<148::AID-QSAR148>3.0.CO;2-7
Hong-yu Zhang, You-min Sun, Dezhan Chen
{"title":"O–H Bond Dissociation Energies of Phenolic Compounds are Determined by Field/Inductive Effect or Resonance Effect?A DFT Study and Its Implication","authors":"Hong-yu Zhang, You-min Sun, Dezhan Chen","doi":"10.1002/1521-3838(200107)20:2<148::AID-QSAR148>3.0.CO;2-7","DOIUrl":"https://doi.org/10.1002/1521-3838(200107)20:2<148::AID-QSAR148>3.0.CO;2-7","url":null,"abstract":"","PeriodicalId":20818,"journal":{"name":"Quantitative Structure-activity Relationships","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2001-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78225762","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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