Development of CoMFA models of affinity and selectivity to angiotensin II type-1 and type-2 receptors

Sköld, Christian; Karlén, Anders

doi:10.1016/j.jmgm.2006.10.004

Cited by 2 publications

(3 citation statements)

References 38 publications

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“…The ratio of compounds for the training and test set was 3:1. Data sets were divided into test set molecules and training set molecules on the assumption that the molecules in the test set are structurally similar to the compounds of the training set, which concluded the study with high representative and predictive ability of the training set toward the test set of compounds …”

Section: D-qsar Methodology Employed For Steroidal 5α-reductase Inhib...mentioning

confidence: 99%

Steroidal 5α-Reductase Inhibitors: A Comparative 3D-QSAR Study Review

Thareja

2015

Chem. Rev.

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Section: D-qsar Methodology Employed For Steroidal 5α-reductase Inhib...mentioning

confidence: 99%

Steroidal 5α-Reductase Inhibitors: A Comparative 3D-QSAR Study Review

Thareja

2015

Chem. Rev.

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“…Experimental Data. Table 2 shows the considered data sets, with their IDs in the present work, the references to the publications 13,21,22,23,24 reporting the previous QSAR studies, the numbers of populated triplets for each 2D-FPT version, and the set sizes. Please refer to the original publications and the Supporting Information for compound set sizes and training/validation set definitions.…”

Section: A(m)mentioning

confidence: 99%

“…Success of 2D-FPT descriptors in QSAR would be good news, because they are computationally cheap (no 3D structure generation and alignment required). This benchmarking study revisits 13 ligand/inhibitor sets concerning various receptors, from various literature sources ,− where various QSARs, including high-end, 3D overlay-dependent CoMFA models, were proposed. Linear models were generated for all these sets, using the following:…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Tricentric Pharmacophore Fingerprints. 2. Application of Topological Fuzzy Pharmacophore Triplets in Quantitative Structure−Activity Relationships

Bonachéra

Horvath

2008

J. Chem. Inf. Model.

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Topological fuzzy pharmacophore triplets (2D-FPT), using the number of interposed bonds to measure separation between the atoms representing pharmacophore types, were employed to establish and validate quantitative structure-activity relationships (QSAR). Thirteen data sets for which state-of-the-art QSAR models were reported in literature were revisited in order to benchmark 2D-FPT biological activity-explaining propensities. Linear and nonlinear QSAR models were constructed for each compound series (following the original author's splitting into training/validation subsets) with three different 2D-FPT versions, using the genetic algorithm-driven Stochastic QSAR sampler (SQS) to pick relevant triplets and fit their coefficients. 2D-FPT QSARs are computationally cheap, interpretable, and perform well in benchmarking. In a majority of cases (10/13), default 2D-FPT models validated better than or as well as the best among those reported, including 3D overlay-dependent approaches. Most of the analogues series, either unaffected by protonation equilibria or unambiguously adopting expected protonation states, were equally well described by rule-or pK a -based pharmacophore flagging. Thermolysin inhibitors represent a notable exception: pK a -based flagging boosts model quality, althoughssurprisinglysnot due to proteolytic equilibrium effects. The optimal degree of 2D-FPT fuzziness is compound set dependent. This work further confirmed the higher robustness of nonlinear over linear SQS models. In spite of the wealth of studied sets, benchmarking is nevertheless flawed by low intraset diversity: a whole series of thereby caused artifacts were evidenced, implicitly raising questions about the way QSAR studies are conducted nowadays. An in-depth investigation of thrombin inhibition models revealed that some of the selected triplets make sense (one of these stands for a topological pharmacophore covering the P 1 and P 2 binding pockets). Nevertheless, equations were either unable to predict the activity of the structurally different ligands or tended to indiscriminately predict any compound outside the training family to be active. 2D-FPT QSARs do however not depend on any common scaffold required for molecule superimposition and may in principle be trained on hand of diverse sets, which is a must in order to obtain widely applicable models. Adding (assumed) inactives of various families for training enabled discovery of models that specifically recognize the structurally different actives.

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Development of CoMFA models of affinity and selectivity to angiotensin II type-1 and type-2 receptors

Cited by 2 publications

References 38 publications

Steroidal 5α-Reductase Inhibitors: A Comparative 3D-QSAR Study Review

Steroidal 5α-Reductase Inhibitors: A Comparative 3D-QSAR Study Review

Fuzzy Tricentric Pharmacophore Fingerprints. 2. Application of Topological Fuzzy Pharmacophore Triplets in Quantitative Structure−Activity Relationships

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