Unified Markov thermodynamics based on stochastic forms to classify drugs considering molecular structure, partition system, and biological species:

González-Dı́az, Humberto; Agüero, Guillermin; Cabrera, Miguel A.; Molina, Reinaldo; Santana, Lourdes; Uriarte, Eugenio; Delogu, Giovanna; Castañedo, Nilo

doi:10.1016/j.bmcl.2004.11.059

Cited by 45 publications

(33 citation statements)

References 54 publications

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“…In all these studies, we used different indices such as entropies [17], spectral moments [18,19], free energies [20,21], and electrostatic potentials [22,23]. The common feature of all these QSAR studies is the application of a Markov model (MM) to derive the molecular descriptors that encode the macromolecular structure [24].…”

Section: Introductionmentioning

confidence: 99%

3D-QSAR study for DNA cleavage proteins with a potential anti-tumor ATCUN-like motif

González-Dı́az

González

González-Díaz³

2006

Journal of Inorganic Biochemistry

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Section: Introductionmentioning

confidence: 99%

3D-QSAR study for DNA cleavage proteins with a potential anti-tumor ATCUN-like motif

González-Dı́az

González

González-Díaz³

2006

Journal of Inorganic Biochemistry

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“…27 In the referenced work, w j is the atomic standard partition free energy ( 1 g ij ) with which the jth atom pass from A to B. This is not coherent because inverting the sign of 1 g ij physically means inverting the direction of partition but in mathematical terms it indicates a change on the system of reference.…”

Section: Checking Physical Coherence Of the Proposed Partition Probabmentioning

confidence: 99%

“…For the sake of simplicity, all the partition coefficients were written in such a way that partition phenomena are studied from a more organized system to a less organized one, that is, from water to air, or from tissue to plasma. 27 Names, BPS, data distribution for statistical analysis, experimental log p values in the specific BPS and SBP of PL/PUL appear in the supporting information material. SBP is determined here as SBP ¼ (log p value max þ log p value min )/2, where log p value-*This is a preliminary experimental version.…”

Section: Data Basementioning

confidence: 99%

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Computational chemistry development of a unified free energy Markov model for the distribution of 1300 chemicals to 38 different environmental or biological systems

et al. 2007

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Predicting tissue and environmental distribution of chemicals is of major importance for environmental and life sciences. Most of the molecular descriptors used in computational prediction of chemicals partition behavior consider molecular structure but ignore the nature of the partition system. Consequently, computational models derived up-to-date are restricted to the specific system under study. Here, a free energy-based descriptor (DeltaG(k)) is introduced, which circumvent this problem. Based on DeltaG(k), we developed for the first time a single linear classification model to predict the partition behavior of a broad number of structurally diverse drugs and other chemicals (1300) for 38 different partition systems of biological and environmental significance. The model presented training/predicting set accuracies of 91.79/88.92%. Parametrical assumptions were checked. Desirability analysis was used to explore the levels of the predictors that produce the most desirable partition properties. Finally, inversion of the partition direction for each one of the 38 partition systems evidences that our models correctly classified 89.08% of compounds with an uncertainty of only +/-0.17% independently of the direction of the partition process used to seek the model. Other 10 different classification models (linear, neural networks, and genetic algorithms) were also tested for the same purposes. None of these computational models favorably compare with respect to the linear model indicating that our approach capture the main aspects that govern chemicals partition in different systems.

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“…These molecular descriptors describe changes in electrostatic potential, [21][22][23] vibration frequency, 24 electron distribution, 25 or free energies. 26 In this sense, we used different mathematical formulations such as entropies, 27 absolute probabilities, 28 electrostatic potentials, 29 affinity constants, 30 and stochastic spectral moments. [31][32][33] However, a thorough inspection of this approach reveals that little attention is paid to other effects that are also of major importance for biological activity, such as van der Waals (vdw) interactions.…”

Section: Introductionmentioning

confidence: 99%

Computational chemistry approach to protein kinase recognition using 3D stochastic van der Waals spectral moments

et al. 2007

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Three-dimensional (3D) protein structures now frequently lack functional annotations because of the increase in the rate at which chemical structures are solved with respect to experimental knowledge of biological activity. As a result, predicting structure-function relationships for proteins is an active research field in computational chemistry and has implications in medicinal chemistry, biochemistry and proteomics. In previous studies stochastic spectral moments were used to predict protein stability or function (González-Díaz, H. et al. Bioorg Med Chem 2005, 13, 323; Biopolymers 2005, 77, 296). Nevertheless, these moments take into consideration only electrostatic interactions and ignore other important factors such as van der Waals interactions. The present study introduces a new class of 3D structure molecular descriptors for folded proteins named the stochastic van der Waals spectral moments ((o)beta(k)). Among many possible applications, recognition of kinases was selected due to the fact that previous computational chemistry studies in this area have not been reported, despite the widespread distribution of kinases. The best linear model found was Kact = -9.44 degrees beta(0)(c) +10.94 degrees beta(5)(c) -2.40 degrees beta(0)(i) + 2.45 degrees beta(5)(m) + 0.73, where core (c), inner (i) and middle (m) refer to specific spatial protein regions. The model with a high Matthew's regression coefficient (0.79) correctly classified 206 out of 230 proteins (89.6%) including both training and predicting series. An area under the ROC curve of 0.94 differentiates our model from a random classifier. A subsequent principal components analysis of 152 heterogeneous proteins demonstrated that beta(k) codifies information different to other descriptors used in protein computational chemistry studies. Finally, the model recognizes 110 out of 125 kinases (88.0%) in a virtual screening experiment and this can be considered as an additional validation study (these proteins were not used in training or predicting series).

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Unified Markov thermodynamics based on stochastic forms to classify drugs considering molecular structure, partition system, and biological species:

Cited by 45 publications

References 54 publications

3D-QSAR study for DNA cleavage proteins with a potential anti-tumor ATCUN-like motif

3D-QSAR study for DNA cleavage proteins with a potential anti-tumor ATCUN-like motif

Computational chemistry development of a unified free energy Markov model for the distribution of 1300 chemicals to 38 different environmental or biological systems

Computational chemistry approach to protein kinase recognition using 3D stochastic van der Waals spectral moments

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