CoMFA as a tool for active site modelling

Folkers, Gerd; Merz, Alfred; Rognan, Didier

doi:10.1007/978-94-011-1472-1_36

Cited by 16 publications

(9 citation statements)

References 14 publications

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“…For each combination of fields, the number of components to be used in a final CoMFA model was identified by leave-one-out cross-validation of models with up to five components . Since in several cases the model with the highest value of q 2 had a relatively large number of components, the associated risk of overfitting was avoided by taking, for the final model, the number corresponding to the first maximum of a plot of q 2 against the number of components. Final non-cross-validated models were only actually calculated for two-field combinations chosen on the basis of the q 2 results.…”

Section: Resultsmentioning

confidence: 99%

Conformationally Constrained Butyrophenones with Mixed Dopaminergic (D₂) and Serotoninergic (5-HT_2A, 5-HT_2C) Affinities: Synthesis, Pharmacology, 3D-QSAR, and Molecular Modeling of (Aminoalkyl)benzo- and -thienocycloalkanones as Putative Atypical Antipsychotics

Raviña

Negreira

et al. 1999

J. Med. Chem.

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A series of novel conformationally restricted butyrophenones (2-(aminoethyl)- and 3-(aminomethyl)thieno- or benzocycloalkanones bearing (6-fluorobenzisoxazolyl)piperidine, (p-fluorobenzoyl)piperidine, (o-methoxyphenyl)piperazine, or linear butyrophenone fragments) were prepared and evaluated as atypical antipsychotic agents by in vitro assays of affinity for dopamine receptors (D(1), D(2)) and serotonin receptors (5-HT(2A), 5-HT(2C)) and by in vivo assays of antipsychotic potential and the risk of inducing extrapyramidal side effects. Potency and selectivity depended mainly on the amine fragment connected to the cycloalkanone structure. As a group, compounds with a benzisoxazolyl fragment had the highest 5-HT(2A) activities, followed by the benzoylpiperidine derivatives; in general, alpha-substituted cycloalkanone derivatives were more active than the corresponding beta-substituted congeners. CoMFA (comparative molecular field analysis) and docking studies showed electrostatic, steric, and lipophilic determinants of 5-HT(2A) and D(2) affinities and 5-HT(2A)/D(2) selectivity. The in vitro and in vivo pharmacological profiles of N-[(4-oxo-4H-5, 6-dihydrocyclopenta[b]thiophene-5-yl)ethyl]-4-(6-fluorobenzisox azol-3 -yl)piperidine (23b, QF 0510B), N-[(4-oxo-4,5,6, 7-tetrahydrobenzo[b]thiophene-5-yl)ethyl]-4-(6-fluorobenzisoxazol- 3-y l)piperidine (24b, QF 0610B), and N-[(7-oxo-4,5,6, 7-tetrahydrobenzo[b]thiophene-6-yl)ethyl]-4-(6-fluorobenzisoxazol- 3-y l)piperidine (29b, QF 0902B) suggest that they may be effective antipsychotic drugs with low propensity to induce extrapyramidal side effects.

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Section: Resultsmentioning

confidence: 99%

Conformationally Constrained Butyrophenones with Mixed Dopaminergic (D₂) and Serotoninergic (5-HT_2A, 5-HT_2C) Affinities: Synthesis, Pharmacology, 3D-QSAR, and Molecular Modeling of (Aminoalkyl)benzo- and -thienocycloalkanones as Putative Atypical Antipsychotics

Raviña

Negreira

et al. 1999

J. Med. Chem.

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“…Equal weights for CoMFA or CoMSIA fields were assigned using the CoMFA_STD block scaling option . Cross-validated analyses were run using the leave-one-out method in SAMPLS and cross-validation groups with random members selection, averaged over 100 runs.…”

Section: Methodsmentioning

confidence: 99%

Affinity and Selectivity of Matrix Metalloproteinase Inhibitors: A Chemometrical Study from the Perspective of Ligands and Proteins

Matter¹,

Schwab²

1999

J. Med. Chem.

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A novel strategy to understand affinity and selectivity for enzyme inhibitors using information from ligands and target protein 3D structures is described. It was applied to 2-arylsulfonyl-1,2,3, 4-tetrahydro-isoquinoline-3-carboxylates and -hydroxamates as inhibitors of the matrix metalloproteinases MMP-3 (stromelysin-1) and MMP-8 (human neutrophil collagenase). As the first step, consistent and predictive 3D-QSAR models were derived using CoMFA, CoMSIA, and GRID/Golpe approaches, leading to the identification of binding regions where steric, electronic, or hydrophobic effects are important for affinity. These models were validated using multiple analyses using two or five randomly chosen cross-validation groups and randomizations of biological activities. Second, 3D-QSAR models were derived based on the affinity ratio IC(50)(MMP-8)/IC(50)(MMP-3), allowing the identification of key ligand determinants for selectivity toward one of both enzymes. In addition to this ligands' view, the third step encompasses a chemometrical approach based on principal component analysis (PCA) of multivariate GRID descriptors to uncover the major differences between both protein binding sites with respect to their GRID probe interaction pattern. The resulting information, based on the accurate knowledge of the target protein 3D structures, led to a consistent picture in good agreement with experimentally observed differences in selectivity toward MMP-8 or MMP-3. The interpretation of all three classes of statistical models leads to detailed SAR information for MMP inhibitors, which is in agreement with available data for binding site topologies, ligand affinities, and selectivities. Thus the combined chemical analyses provide guidelines and accurate activity predictions for designing novel, selective MMP inhibitors.

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“…Finally the effectiveness of maximum dissimilarity methods as experimental design technique to generate different sets of diverse compounds was investigated. Those compounds were subsequently analyzed using CoMFA as a 3D-QSAR technique. − The purpose of this study is to illustrate a strategy based on our previous results for the development of a predictive 3D-QSAR model , using CoMFA. The entire strategy is again based on the assumption that smaller sets of representative compounds, if properly chosen, represent all other compounds in a structurally homogeneous classa similar assumption to that previously used to design compound subsets for biological screening.…”

Section: Resultsmentioning

confidence: 99%

“…To speed up the analyses and reduce the amount of noise, a column filter was used to exclude the columns with a variance smaller than 2.0 ( minimum σ). Equal weights were assigned using the CoMFA scaling option . For cross-validation, the leave-one-out method was utilized, as implemented in the program SAMPLS within SYBYL.…”

Section: Methodsmentioning

confidence: 99%

Random or Rational Design? Evaluation of Diverse Compound Subsets from Chemical Structure Databases

1998

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The performance of rational design to maximize the structural diversity of databases for lead finding and lead refinement was investigated. Rational methods such as maximum dissimilarity methods or hierarchical cluster analysis for designing compound subsets were compared to a random approach to study their efficiency for an enhancement of the diversity of three different databases. All investigations were done based on 2D fingerprints as a validated molecular descriptor. To compare the performance of the rational selection methods to a random approach, we additionally used probability calculations. When using maximum dissimilarity-based selections, a single compound can be a member of different neighborhoods as defined by the similarity threshold value, while in hierarchical clustering each compound is assigned to only a single cluster. Therefore the relationship between the similarity threshold of the maximum diversity selection method and a 2D similarity search threshold was studied. In contrast to hierarchical clustering analysis, maximum dissimilarity selections allow to use a similarity threshold for adding a new compound to an already selected compound list. Reasonable values for this similarity threshold are presented here. More diverse subsets were designed using maximum dissimilarity selections, which cover more biological classes than using random selections. An optimally diverse subset without redundant structures containing only 38% of one original dataset was generated, where no structure is more similar than 0.85 to its nearest neighbor, but all biological classes were represented. When it is acceptable to cover only 90% of all biological targets, 3.5-3.7 times more compounds need to be selected using a random approach than in a rational design approach. Such coverage rate shows the highest efficiency of design techniques compared to a random approach. In those subsets no compound is closer than 0.70 to its nearest neighbor. Furthermore a comparative molecular field analysis (CoMFA) is used to evaluate designed and randomly chosen subsets for a database consisting of inhibitors of the angiotensin-converting enzyme. It was shown that designed subsets using maximum dissimilarity methods lead to more stable quantitative structure-activity relationship (QSAR) models with higher predictive power compared to randomly chosen compounds. This predictive power is especially high when there is no compound in the test dataset with a similarity coefficient less than 0.7 to its nearest neighbor in the training set.

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CoMFA as a tool for active site modelling

Cited by 16 publications

References 14 publications

Conformationally Constrained Butyrophenones with Mixed Dopaminergic (D₂) and Serotoninergic (5-HT_2A, 5-HT_2C) Affinities: Synthesis, Pharmacology, 3D-QSAR, and Molecular Modeling of (Aminoalkyl)benzo- and -thienocycloalkanones as Putative Atypical Antipsychotics

Conformationally Constrained Butyrophenones with Mixed Dopaminergic (D₂) and Serotoninergic (5-HT_2A, 5-HT_2C) Affinities: Synthesis, Pharmacology, 3D-QSAR, and Molecular Modeling of (Aminoalkyl)benzo- and -thienocycloalkanones as Putative Atypical Antipsychotics

Affinity and Selectivity of Matrix Metalloproteinase Inhibitors: A Chemometrical Study from the Perspective of Ligands and Proteins

Random or Rational Design? Evaluation of Diverse Compound Subsets from Chemical Structure Databases

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CoMFA as a tool for active site modelling

Cited by 16 publications

References 14 publications

Conformationally Constrained Butyrophenones with Mixed Dopaminergic (D2) and Serotoninergic (5-HT2A, 5-HT2C) Affinities: Synthesis, Pharmacology, 3D-QSAR, and Molecular Modeling of (Aminoalkyl)benzo- and -thienocycloalkanones as Putative Atypical Antipsychotics

Conformationally Constrained Butyrophenones with Mixed Dopaminergic (D2) and Serotoninergic (5-HT2A, 5-HT2C) Affinities: Synthesis, Pharmacology, 3D-QSAR, and Molecular Modeling of (Aminoalkyl)benzo- and -thienocycloalkanones as Putative Atypical Antipsychotics

Affinity and Selectivity of Matrix Metalloproteinase Inhibitors: A Chemometrical Study from the Perspective of Ligands and Proteins

Random or Rational Design? Evaluation of Diverse Compound Subsets from Chemical Structure Databases

Contact Info

Product

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Conformationally Constrained Butyrophenones with Mixed Dopaminergic (D₂) and Serotoninergic (5-HT_2A, 5-HT_2C) Affinities: Synthesis, Pharmacology, 3D-QSAR, and Molecular Modeling of (Aminoalkyl)benzo- and -thienocycloalkanones as Putative Atypical Antipsychotics

Conformationally Constrained Butyrophenones with Mixed Dopaminergic (D₂) and Serotoninergic (5-HT_2A, 5-HT_2C) Affinities: Synthesis, Pharmacology, 3D-QSAR, and Molecular Modeling of (Aminoalkyl)benzo- and -thienocycloalkanones as Putative Atypical Antipsychotics