Connecting chemistry and biology through molecular descriptors

The intrinsic (gas-phase) stacking energies of natural and artificial nucleobases were explored using density functional theory (DFT) and correlated ab initio methods. Ranking the stacking strength of natural nucleobase dimers revealed a preference in binding partner similar to that seen from experiments, namely G > C > A > T > U. Decomposition of these interaction energies using symmetry-adapted perturbation theory (SAPT) showed that these dispersion dominated interactions are modulated by electrostatics. Artificial nucleobases showed a similar stacking preference for natural nucleobases and were also modulated by electrostatic interactions. A robust predictive multivariate model was developed that quantitively predicts the maximum stacking interaction between natural and a wide range of artificial nucleobases using molecular descriptors based on computed electrostatic potentials (ESPs) and the number of heavy atoms. This model should find utility in designing artificial nucleobase analogs that exhibit stacking interactions comparable to those of natural nucleobases. Further analysis of the descriptors in this model unveil the origin of superior stacking abilities of certain nucleobases, including cytosine and guanine.

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Quantitative Structure-Property Relationship (QSPR) Study of the Hydrophobicity of Phenols and 2-(Aryloxy-a-acetyl)-phenoxathiin Derivatives

Revista de Chimie ◽

10.37358/rc.08.11.1994 ◽

2008 ◽

Vol 59 (11) ◽

Author(s):

Adrian Beteringhe ◽

Ana Cristina Radutiu ◽

Titus Constantinescu ◽

Luminita Patron ◽

Alexandru T. Balaban

Keyword(s):

Water Solubility ◽

Molecular Descriptors ◽

Log P ◽

Structure Property ◽

Substituted Phenols ◽

Reverse Phase ◽

Aromaticity Index ◽

Structure Property Relationship ◽

Layer Chromatography ◽

Energy Of Formation

In a preceding study, the molecular hydrophobicity (RM0) was determined experimentally from reverse-phase thin-layer chromatography data for several substituted phenols and 2-(aryloxy-a-acetyl)-phenoxathiin derivatives, obtained from the corresponding phenoxides and 2-(a-bromoacetyl)-phenoxathiin. QSPR correlations for RM0 were explored using four calculated molecular descriptors: the water solubility parameter (log Sw), log P, the Gibbs energy of formation (DGf), and the aromaticity index (HOMA). Triparametric correlations do not improve substantially the biparametric correlation of RM0 in terms of log Sw and HOMA.

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Generalized Molecular Descriptors Derived From Event-Based Discrete Derivative

Current Pharmaceutical Design ◽

10.2174/1381612822666160610114148 ◽

2016 ◽

Vol 22 (33) ◽

pp. 5095-5113 ◽

Cited By ~ 4

Author(s):

Oscar Martínez-Santiago ◽

Reisel Cabrera ◽

Yovani Marrero-Ponce ◽

Stephen Barigye ◽

Huong Le-Thi-Thu ◽

...

Keyword(s):

Molecular Descriptors ◽

Discrete Derivative ◽

Event Based

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Molecular Topology and Other Promiscuity Determinants as Predictors of Therapeutic Class - A Theoretical Framework to Guide Drug Repositioning?

Current Topics in Medicinal Chemistry ◽

10.2174/1568026618666180801091642 ◽

2018 ◽

Vol 18 (13) ◽

pp. 1110-1122 ◽

Cited By ~ 2

Author(s):

Juan F. Morales ◽

Lucas N. Alberca ◽

Sara Chuguransky ◽

Mauricio E. Di Ianni ◽

Alan Talevi ◽

...

Keyword(s):

Molecular Descriptors ◽

Drug Repositioning ◽

Drug Repurposing ◽

Topological Descriptors ◽

Log P ◽

Acidity Constant ◽

Molecular Topology ◽

Clustering Methods ◽

Mean Values ◽

Qsar Models

Much interest has been paid in the last decade on molecular predictors of promiscuity, including molecular weight, log P, molecular complexity, acidity constant and molecular topology, with correlations between promiscuity and those descriptors seemingly being context-dependent. It has been observed that certain therapeutic categories (e.g. mood disorders therapies) display a tendency to include multi-target agents (i.e. selective non-selectivity). Numerous QSAR models based on topological descriptors suggest that the topology of a given drug could be used to infer its therapeutic applications. Here, we have used descriptive statistics to explore the distribution of molecular topology descriptors and other promiscuity predictors across different therapeutic categories. Working with the publicly available ChEMBL database and 14 molecular descriptors, both hierarchical and non-hierchical clustering methods were applied to the descriptors mean values of the therapeutic categories after the refinement of the database (770 drugs grouped into 34 therapeutic categories). On the other hand, another publicly available database (repoDB) was used to retrieve cases of clinically-approved drug repositioning examples that could be classified into the therapeutic categories considered by the aforementioned clusters (111 cases), and the correspondence between the two studies was evaluated. Interestingly, a 3- cluster hierarchical clustering scheme based on only 14 molecular descriptors linked to promiscuity seem to explain up to 82.9% of approved cases of drug repurposing retrieved of repoDB. Therapeutic categories seem to display distinctive molecular patterns, which could be used as a basis for drug screening and drug design campaigns, and to unveil drug repurposing opportunities between particular therapeutic categories.

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Prediction and Dissection of Protein-RNA Interactions by Molecular Descriptors

Current Topics in Medicinal Chemistry ◽

10.2174/1568026615666150819110703 ◽

2015 ◽

Vol 16 (6) ◽

pp. 604-615 ◽

Cited By ~ 4

Author(s):

Zhi-Ping Liu ◽

Luonan Chen

Keyword(s):

Molecular Descriptors

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Higher-Order and Mixed Discrete Derivatives such as a Novel Graph- Theoretical Invariant for Generating New Molecular Descriptors

Current Topics in Medicinal Chemistry ◽

10.2174/1568026619666190510093651 ◽

2019 ◽

Vol 19 (11) ◽

pp. 944-956 ◽

Cited By ~ 2

Author(s):

Oscar Martínez-Santiago ◽

Yovani Marrero-Ponce ◽

Ricardo Vivas-Reyes ◽

Mauricio E.O. Ugarriza ◽

Elízabeth Hurtado-Rodríguez ◽

...

Keyword(s):

Molecular Descriptors ◽

3D Qsar ◽

Higher Order ◽

Molecular Structures ◽

Qsar Modeling ◽

Statistical Parameters ◽

Molecular Graphs ◽

New Family ◽

Mixed Derivatives ◽

Discrete Derivative

Background: Recently, some authors have defined new molecular descriptors (MDs) based on the use of the Graph Discrete Derivative, known as Graph Derivative Indices (GDI). This new approach about discrete derivatives over various elements from a graph takes as outset the formation of subgraphs. Previously, these definitions were extended into the chemical context (N-tuples) and interpreted in structural/physicalchemical terms as well as applied into the description of several endpoints, with good results. Objective: A generalization of GDIs using the definitions of Higher Order and Mixed Derivative for molecular graphs is proposed as a generalization of the previous works, allowing the generation of a new family of MDs. Methods: An extension of the previously defined GDIs is presented, and for this purpose, the concept of Higher Order Derivatives and Mixed Derivatives is introduced. These novel approaches to obtaining MDs based on the concepts of discrete derivatives (finite difference) of the molecular graphs use the elements of the hypermatrices conceived from 12 different ways (12 events) of fragmenting the molecular structures. The result of applying the higher order and mixed GDIs over any molecular structure allows finding Local Vertex Invariants (LOVIs) for atom-pairs, for atoms-pairs-pairs and so on. All new families of GDIs are implemented in a computational software denominated DIVATI (acronym for Discrete DeriVAtive Type Indices), a module of KeysFinder Framework in TOMOCOMD-CARDD system. Results: QSAR modeling of the biological activity (Log 1/K) of 31 steroids reveals that the GDIs obtained using the higher order and mixed GDIs approaches yield slightly higher performance compared to previously reported approaches based on the duplex, triplex and quadruplex matrix. In fact, the statistical parameters for models obtained with the higher-order and mixed GDI method are superior to those reported in the literature by using other 0-3D QSAR methods. Conclusion: It can be suggested that the higher-order and mixed GDIs, appear as a promissory tool in QSAR/QSPRs, similarity/dissimilarity analysis and virtual screening studies.

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QSAR Analysis of Selected Antimicrobial Structures Belonging to Nitro-derivatives of Heterocyclic Compounds

Letters in Drug Design & Discovery ◽

10.2174/1570180815666181004112947 ◽

2020 ◽

Vol 17 (2) ◽

pp. 214-225 ◽

Cited By ~ 3

Author(s):

Piotr Kawczak ◽

Leszek Bober ◽

Tomasz Bączek

Keyword(s):

Antimicrobial Activity ◽

Heterocyclic Compounds ◽

Molecular Descriptors ◽

Microbiological Activity ◽

Activity Data ◽

Qsar Analysis ◽

Qsar Models ◽

Nitro Derivatives ◽

Semi Empirical ◽

Derivatives Of

Background: Nitro-derivatives of heterocyclic compounds were used as active agents against pathogenic microorganisms. A set of 4- and 5-nitroimidazole derivatives exhibiting antimicrobial activity was analyzed with the use of Quantitative Structure-Activity Relationships (QSAR) method. The study included compounds used both in documented treatment and those described as experimental. Objective: The purpose of this study was to demonstrate the common and differentiating characteristics of the above-mentioned chemical compounds alike physicochemically as well as pharmacologically based on the quantum chemical calculations and microbiological activity data. Methods: During the study PCA and MLR analysis were performed, as the types of proposed chemometric approach. The semi-empirical and ab initio level of in silico molecular modeling was performed for calculations of molecular descriptors. Results: QSAR models were proposed based on chosen descriptors. The relationship between the nitro-derivatives structure and microbiological activity data was able to class and describe the antimicrobial activity with the use of statistically significant molecular descriptors. Conclusion: The applied chemometric approaches revealed the influential features of the tested structures responsible for the antimicrobial activity of studied nitro-derivatives.

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Application of Ionic Liquids for the Determination of Lipophilicity Parameters Using TLC Method, and QSRR Analysis for the Antipsychotic Drugs

Medicinal Chemistry ◽

10.2174/1573406415666190723162959 ◽

2020 ◽

Vol 16 (7) ◽

pp. 848-859

Author(s):

Dominik Mieszkowski ◽

Marcin Koba ◽

Michał P. Marszałł

Keyword(s):

Ionic Liquids ◽

Antipsychotic Drugs ◽

Molecular Descriptors ◽

Harmful Effect ◽

Correlation Coefficients ◽

Reversed Phase ◽

Assessment Process ◽

Phase System ◽

Phase Liquid ◽

Lipophilicity Parameters

Background: Reversed-phase liquid chromatography may cause difficulties, especially in the case of basic drugs due to the strong silanophilic interactions in the partition mechanism. Recently, imidazolium-based ionic liquids additives appeared interesting and a convenient solution for suppressing the harmful effect of free residuals of silanol groups, allowing remodeling of the stationary/mobile-phase system, and thus improving the lipophilicity assessment process. Objective: The aim of the study was to evaluate the retention behavior of basic antipsychotics using various RP-LC systems, and compare them with data obtained from the modified ionic-liquids RP-TLC systems, and perform the QSRR analysis. Methods: Retention and lipophilicity parameters of diverse antipsychotics have been examined in various RP-LC systems. Lipophilicity indices were compared with miscellaneous computed logP values. Furthermore, a large number of molecular descriptors have been computed and compared using various medicinal chemistry software, in order to contribute to the analysis of QSRR. Results: Designated correlation coefficients showed that lipophilicity parameters from TLC systems without [EMIM][BF4] additive correlates very poor with the calculated logPs indices, whereas the indices from the traditional HPLC and TLC systems (with [EMIM][BF4]) were clearly better. Furthermore, QSRR analysis performed for these experimentally obtained lipophilicity parameters showed significant relationships between the retention constants (RO>M, logkw) and the in silico calculated physicochemical molecular descriptors. Conclusion: ILs additive may be a significant factor affecting the lipophilicity of basic compounds, thus their use may be favorable in lipophilicity assessment studies. QSRR models with ILs showed that they may be useful in searching/or predicting HPLC/TLC retention parameters for the new/other antipsychotic drugs.

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