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Record W2074344354 · doi:10.1021/jp040196f

Pharmacophore Fragment-Based Prediction and Gas-Phase ab Initio Optimization of Carvedilol Conformations

2004· article· en· W2074344354 on OpenAlexaff
David R. P. Almeida, Donna M. Gasparro, Ferenc Fülöp, Imre G. Csizmadia

Bibliographic record

VenueThe Journal of Physical Chemistry A · 2004
Typearticle
Languageen
FieldComputer Science
TopicComputational Drug Discovery Methods
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsConformational isomerismPharmacophoreChemistryComputational chemistryAb initioCarvedilolProtonationMolecular switchStereochemistryMoleculeIon

Abstract

fetched live from OpenAlex

This current communication gives the results of a novel computational molecular method of selecting, from a vast number of possible conformations, the dominant low-energy states of a large molecule by dividing it into separately analyzable structure−activity fragments. Carvedilol is a cardiovascular drug of proven efficacy with multiple molecular targets: it acts as a nonselective β-adrenoceptor (β 1 and β 2 ) and selective α 1 -adrenoceptor antagonist, an antioxidant able to reduce reactive oxygen species (ROS)-mediated oxidative stress, a beneficial modulator of cardiac electrophysiological properties (K + and Ca 2+ ion channels), a multifaceted cardioprotector, and novel antifibrillar agent able to inhibit amyloid-beta (Aβ) fibril formation. Given carvedilol's varied pharmacodynamic profiles, and the fact that a thorough analysis of its potential energy hypersurface (PEHS) has not yet been performed, an original molecular fragmentation method was developed to reveal carvedilol's low-energy states, to divulge their relevance to its biological activity. Multidimensional conformational analysis (MDCA) leads to a total of 177 147 (3 11 ) conformational possibilities, whereas fragmentation studies predict 240 gas-phase conformations. Structural predictions were tested on protonated R -carvedilol with gas-phase molecular orbital (MO) computations of PEHS minima at the restricted Hartree−Fock (RHF) (RHF/3-21G) level of theory, using the Gaussian 98 software program. Computation of the 240 predicted (input) carvedilol conformations revealed 121 converged (i.e., fully optimized) structures, of which nine possessed a conformer relative energy of <4 kcal/mol. Seven of these nine conformers possess a unique “tetra-centric” (four-centered) spiro-type structure that is composed of two rings (six- and eight-membered) enclosed by two O···H−N hydrogen bonds (H-bonds) that are connected via the protonated N atom in the side chain of carvedilol; this conformation is largely determined by the carbazole-containing pharmacophore (Fragment A) of carvedilol. In regard to the utility of the rational molecular fragmentation method used to predict and optimize the carvedilol structures, it is determined that 8 of the 11 torsional angles were accurately predicted (72.7%), according to torsional angle conformation distribution. The strength of this fragmentation method relies on full MDCA optimization of the individual fragments, which are then used to predict the carvedilol conformations. As such, the predicted inputs possess an inherent degree of energy minimization and, thus, are able to provide a better hypothesis of relevant sections of the carvedilol surface versus a random sampling of the PEHS. The elucidation of carvedilol's conformational identity greatly aids the full molecular understanding of carvedilol's adrenoceptor binding structure and carvedilol's involvement, at the molecular level, in ameliorating pathological states such as oxidative stress and Alzheimer's disease.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.622
Threshold uncertainty score0.248

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.302
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2004
Admission routes1
Has abstractyes

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