QSAR Methods Research Articles

4D-fingerprints, universal QSAR and QSPR descriptors.

An elusive goal in the field of chemoinformatics and molecular modeling has been the generation of a set of descriptors that, once calculated for a molecule, may be used in a wide variety of applications. Since such universal descriptors are generated free from external constraints, they are inherently independent of the data set in which they are employed. The realization of a set of universal descriptors would significantly streamline such chemoinformatics tasks as virtual high-throughout screening (VHTS) and toxicity profiling. The current study reports the derivation and validation of a potential set of universal descriptors, referred to as the 4D-fingerprints. The 4D-fingerprints are derived from the 4D-molecular similarity analysis. To evaluate the applicability of the 4D-fingerprints as universal descriptors, they are used to generate descriptive QSAR models for 5 independent training sets. Each of the training sets has been analyzed previously by several varying QSAR methods, and the results of the models generated using the 4D-fingerprints are compared to the results of the previous QSAR analyses. It was found that the models generated using the 4D-fingerprints are comparable in quality, based on statistical measures of fit and test set prediction, to the previously reported models for the other QSAR methods. This finding is particularly significant considering the 4D-fingerprints are generated independent of external constraints such as alignment, while the QSAR methods used for comparison all require an alignment analysis.

Modeling of p38 mitogen-activated protein kinase inhibitors using the Catalyst™ HypoGen and k-nearest neighbor QSAR methods

We have employed in parallel the Catalyst HypoGen pharmacophore modeling approach and the variable selection k-nearest neighbor quantitative structure–activity relationship ( kNN QSAR) method to model a diverse data set of p38 mitogen-activated protein (MAP) kinase inhibitors. The HypoGen pharmacophore model, developed from a novel automated training set selection protocol, identified chemical functional features that were characteristic of the active compounds and differentiated the active from the inactive inhibitors. The kNN QSAR modeling employed topological descriptors and afforded predictive QSAR models with consistently high values of both leave-one-out cross-validated R 2 for the training set and predictive R 2 for the test set. The results of both modeling approaches were sensitive to the selection of the training and test sets used for model development and validation. The resulting Catalyst pharmacophore and kNN QSAR models can be used concurrently for rapid virtual screening of chemical databases to identify novel p38 MAP kinase inhibitors.

Molecular electronegativity-distance vector (MEDV-4): a two-dimensional QSAR method for the estimation and prediction of biological activities of estradiol derivatives

By using molecular electronegativity-distance vector (MEDV-4) approach, a two-dimensional quantitative structure–activity relationship (2D-QSAR) model has been developed to estimate and predict the binding activities of a series of 11β-, 16α-, 17α-substituted estradiols for estrogen receptor. Considering the contribution of each non-hydrogen atom, the MEDV-4 exhibits high relevance to the structural properties. The 10-parameter model yields a R 2=0.870 and a leave-one-out procedure cross-validation R CV 2=0.616. The parameters were screened by the stepwise multiple regression approach and the newly formed optimal models had R 2=0.852 and R CV 2=0.747 when the number of parameters are taken as m=6. The performance of the models is better than that reported in literature, and had high correlation with the typical parameters (hydrophobic parameter π 11, molecular refraction MR 11 and indicator variable I) used in literature. The MEDV-4 model can provide a simple and rapid way to investigate the binding activities of estradiols.

Comparative Study of 3D-QSAR Techniques on Angiotensin II Receptor (AT1) Antagonists

Three different 3D-QSAR methods namely CoMFA, CoMSIA and RSA were applied to a set of 38 angiotensin type 1 receptor (AT1) antagonists. The conformation and alignment of molecules used in each of the three QSAR methods were obtained by a novel method - Consensus Dynamics. To derive the best CoMFA model, various parameters such as partial charge formalism, grid spacing and the absolute orientation of the molecules in the grid were varied. The best CoMFA model had an r2 of 0.926 and a cross-validated correlation coefficient (q2) of 0.546, which improved with region focussing to 0.710. The best CoMSIA model for the same set had an r2 of 0.969 and a q2 of 0.524. Likewise, the best RSA model with r2 of 0.893 and q2 of 0.639 resulted from optimization of various parameters such as atomic partial charges, surface fit and the manner of representation of electrostatics on the receptor surface. The models were thoroughly validated through trials using scrambled activities and bootstrapping. The predictive power of these models was evaluated on a test set that had almost 40% representation outside the training set. The three techniques were gainfully used to identify the structural features important for biological activity in these compounds.

Discovery of a Novel Series of DHODH Inhibitors by a Docking Procedure and QSAR Refinement.

A novel series of DHODH inhibitors was developed based on a lead which was obtained by a docking procedure and a medicinal chemistry exploration. The activity of the initial lead was improved by a QSAR method to yield low nanomolar inhibitors.

Combinatorial QSAR of ambergris fragrance compounds.

A combinatorial quantitative structure-activity relationships (Combi-QSAR) approach has been developed and applied to a data set of 98 ambergris fragrance compounds with complex stereochemistry. The Combi-QSAR approach explores all possible combinations of different independent descriptor collections and various individual correlation methods to obtain statistically significant models with high internal (for the training set) and external (for the test set) accuracy. Seven different descriptor collections were generated with commercially available MOE, CoMFA, CoMMA, Dragon, VolSurf, and MolconnZ programs; we also included chirality topological descriptors recently developed in our laboratory (Golbraikh, A.; Bonchev, D.; Tropsha, A. J. Chem. Inf. Comput. Sci. 2001, 41, 147-158). CoMMA descriptors were used in combination with MOE descriptors. MolconnZ descriptors were used in combination with chirality descriptors. Each descriptor collection was combined individually with four correlation methods, including k-nearest neighbors (kNN) classification, Support Vector Machines (SVM), decision trees, and binary QSAR, giving rise to 28 different types of QSAR models. Multiple diverse and representative training and test sets were generated by the divisions of the original data set in two. Each model with high values of leave-one-out cross-validated correct classification rate for the training set was subjected to extensive internal and external validation to avoid overfitting and achieve reliable predictive power. Two validation techniques were employed, i.e., the randomization of the target property (in this case, odor intensity) also known as the Y-randomization test and the assessment of external prediction accuracy using test sets. We demonstrate that not every combination of the data modeling technique and the descriptor collection yields a validated and predictive QSAR model. kNN classification in combination with CoMFA descriptors was found to be the best QSAR approach overall since predictive models with correct classification rates for both training and test sets of 0.7 and higher were obtained for all divisions of the ambergris data set into the training and test sets. Many predictive QSAR models were also found using a combination of kNN classification method with other collections of descriptors. The combinatorial QSAR affords automation, computational efficiency, and higher probability of identifying significant QSAR models for experimental data sets than the traditional approaches that rely on a single QSAR method.

3D-quantitative structure-activity relationship study of organophosphate compounds

The biological effects of most organophosphate compounds (OP) are arising by inhibition of the enzyme acetylcholinesterase (AChE). The 3D-quantitative structure-activity relationship (3D-QSAR) on the acute toxicity to housefly (Musca nobulo L.) of 35 dialkyl phenyl phosphate compounds are studied by using comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) methods, and the reaction mechanism between the OP and the AChE are discussed. In contrast to classical QSAR methods, CoMFA and CoMSIA, especially the combination of both approaches, can give more comprehensive and accurate perspectives on the mechanism of the reaction between OP and AChE. The results show that the length of alkyl, and the electronegative of substituent on phenyl of OP have significant effects on the AChE activity, whereas, the hydrophobicity of OP has little influence. The steric and electronic properties of OP have a dominant influence on the reaction between OP and AChE.

Potentiometric detection for high-performance liquid chromatography is a reality: Which classes of organic substances are the targets?

Abstract Potentiometric detection in high-performance liquid chromatography (HPLC) is shown to be an underexploited technique. The technique can be of great use to less classical potentiometry targets, such as bioorganics, of low as well as of high molecular weight. The understanding of non-faradaic potentiometry is, however, still problematic. Predicting the selectivity and sensitivity of a potentiometric electrode for organic ionizable substances can be done to a certain extent using QSAR methods. Although many new polymer materials and synthetic receptor molecules for organic ionics are being synthesized, few of them are applied in potentiometric membrane coatings. Hydrophilic organics form an interesting target group for these new materials.

Quantum Theoretic QSAR of Benzene Derivatives: Some Enzyme Inhibitors

Our previously developed approach to the development of QSAR equations for benzene derivatives, originally for phenylalkylamine hallucinogens, has been applied to four new systems: sulfonamide inhibitors of the enzymes carbonic anhydrase, thrombin, trypsin, and Clostridium histolyticum collagenase. The novel features involve the energies and nodal orientations of π-like orbitals, and an allowance for the symmetry of the benzene nucleus. The resulting equations give better fits, better predictivity and are more easily interpretable than those resulting from traditional QSAR methods.

Discovery of a novel series of DHODH inhibitors by a docking procedure and QSAR refinement

A novel series of DHODH inhibitors was developed based on a lead which was obtained by a docking procedure and a medicinal chemistry exploration. The activity of the initial lead was improved by a QSAR method to yield low nanomolar inhibitors.

A structure-activity relationship study of catechol-O-methyltransferase inhibitors combining molecular docking and 3D QSAR methods.

A panel of 92 catechol-O-methyltransferase (COMT) inhibitors was used to examine the molecular interactions affecting their biological activity. COMT inhibitors are used as therapeutic agents in the treatment of Parkinson's disease, but there are limitations in the currently marketed compounds due to adverse side effects. This study combined molecular docking methods with three-dimensional structure-activity relationships (3D QSAR) to analyse possible interactions between COMT and its inhibitors, and to incite the design of new inhibitors. Comparative molecular field analysis (CoMFA) and GRID/GOLPE models were made by using bioactive conformations from docking experiments, which yielded q2 values of 0.594 and 0.636, respectively. The docking results, the COMT X-ray structure, and the 3D QSAR models are in agreement with each other. The models suggest that an interaction between the inhibitor's catechol oxygens and the Mg2+ ion in the COMT active site is important. Both hydrogen bonding with Lys144, Asn170 and Glu199, and hydrophobic contacts with Trp38, Pro174 and Leu198 influence inhibitor binding. Docking suggests that a large R1 substituent of the catechol ring can form hydrophobic contacts with side chains of Val173, Leu198, Met201 and Val203 on the COMT surface. Our models propose that increasing steric volume of e.g. the diethylamine tail of entacapone is favourable for COMT inhibitory activity.

Synthesis and anti-microbial activities of choline-like quaternary ammonium chlorides

New choline-like quaternary ammonium chlorides were obtained. The work-up procedure of synthesis was quick and efficient. The obtained chlorides showed anti-microbial activities. Quaternary ammonium chlorides derivatives of deanol esters exhibited strong activity and wide anti-bacterial spectra, similar to the activity of benzalkonium chloride. The relationship between chemical structure and anti-microbial activity was analyzed by the QSAR method.

Understanding Stereoinduction in Catalysis via Computer: New Tools for Asymmetric Synthesis

AbstractFor Abstract see ChemInform Abstract in Full Text.

Hologram Quantitative Structure-Activity Relationships Investigations of Non-nucleoside Reverse Transcriptase Inhibitors

Non-nucleoside reverse transcriptase inhibitors (NNRTIs) such as TIBO, HEPT and dipyridodiazepinone are effective against HIV-1 RT. These NNRTIs are chemically and structurally diverse, but they all bind to a common allosteric site of HIV-1 RT. These inhibitors exhibit high potency, low cytotoxicity and produce few side effects. However, the emergency of drug-resistance viral strain has limited the therapeutic efficiency of the NNRTIs. Several different QSAR studies were reported to identify important structural features responsible for the inhibitory activity of these NNRTIs. In this study, hologram quantitative structure-activity relationships (HQSAR) was applied to three different data sets, 70 TIBO, 101 HEPT and 125 dipyridodiazepinone derivatives. Starting geometries of compounds were taken from available X-ray crystallographic data. Modification and full geometry optimization of all derivatives were performed, based on quantum chemical calculations at the HF/3-21G level of theory. All derived HQSAR models produce satisfying predictive ability and yield r(2)(cv) values ranging from 0.62-0.84. Moreover, it was also found that the quality of models enhances as the size of fragments increases. The obtained HQSAR results indicate the similarity of the interactions of these three different NNRTIs with the inhibition pocket of the enzyme. Comparisons of different QSAR methods on these NNRTIs data sets were also considered and it could be shown that HQSAR results yield superior predictive models than other 2D-QSAR approaches. In particular, the predictive ability of the models derived from dipyridodiazepinone analogues was significantly improved and apparently revealed differentiating structural requirements between WT and Y181C HIV RT inhibition. Additionally, the quality of QSAR models constructed by CoMFA and HQSAR methods are comparable and the interpretations of the models reinforce each other. It suggests an advantage of HQSAR as a useful tool in designing new potent inhibitors with enhanced HIV-1 RT inhibition activity, especially against mutant enzyme.

Holographic QSAR of estradiol derivatives

Holographic QSAR model is constructed to predict relative binding affinities of estradiol derivatives with lamb uterine estrogen receptor. The method does not require the generation of three-dimensional structure of the compounds. The factors that influence the quality of QSAR model are discussed. The result indicates that the model has good predictability and yields a high q 2LOO value of 0.897. Compared to other QSAR methods, the HQSAR technique is suitable for use in screening large database of chemicals.

Understanding Stereoinductionin Catalysis via Computer: New Tools for Asymmetric Synthesis

An overview of new concepts, new algorithms, and new computational protocols directed at synthetic chemists interested in generating chiral catalysts is presented in this report. A new mapping tool called stereocartography is described, which allows one to locate the most stereoinducing region around a proposed catalyst. Extant QSAR methods including Comparative Molecular Field Analysis (CoMFA) and a new technique called QM-QSAR are tools for modeling stereoinduction and for making predictions about whether a proposed chiral catalyst will be effective or not. Database mining with the intention of locating unbiased, unique ligand motifs for use in catalysis is described. Functionality mapping of transition states is introduced with the idea of locating where around that transition structure, functional groups assist or interfere with a given reaction coordinate. Functionality mapping in combination with database mining is shown to be an effective way to generate new catalysts for asymmetric induction. Beyond these new tools, more advanced treatments of chirality are investigated - the relationship between the chirality content of a catalyst and its ability to induce asymmetry is discussed along with the concept of distorting a ligand to make it more or less chiral.

Binding affinity prediction of novel estrogen receptor ligands using receptor-based 3-D QSAR methods

We have recently reported the development of a 3-D QSAR model for estrogen receptor ligands showing a significant correlation between calculated molecular interaction fields and experimentally measured binding affinity. The ligand alignment obtained from docking simulations was taken as basis for a comparative field analysis applying the GRID/GOLPE program. Using the interaction field derived with a water probe and applying the smart region definition (SRD) variable selection procedure, a significant and robust model was obtained ( q 2 LOO=0.921, SDEP=0.345). To further analyze the robustness and the predictivity of the established model several recently developed estrogen receptor ligands were selected as external test set. An excellent agreement between predicted and experimental binding data was obtained indicated by an external SDEP of 0.531. Two other traditionally used prediction techniques were applied in order to check the performance of the receptor-based 3-D QSAR procedure. The interaction energies calculated on the basis of receptor–ligand complexes were correlated with experimentally observed affinities. Also ligand-based 3-D QSAR models were generated using program FlexS. The interaction energy-based model, as well as the ligand-based 3-D QSAR models yielded models with lower predictivity. The comparison with the interaction energy-based model and with the ligand-based 3-D QSAR models, respectively, indicates that the combination of receptor-based and 3-D QSAR methods is able to improve the quality of prediction.

Three-dimensional molecular descriptors and a novel QSAR method

A novel set of molecular descriptors suitable for use in quantitative structure–activity relationships and related methods is described. These descriptors are a smooth and interpretable representation of atomic physicochemical property values and intramolecular atom pair distances. Distance atomic physicochemical parameter energy relationships (DAPPER), a novel structure–activity relationship (QSAR) method using these descriptors, is validated on standard datasets.

CoMFA, CoMSIA and GRID/GOLPE studies on calcium entry blocking 1,4-dihydropyridines

Three different 3D QSAR methods have been applied for a common pharmacophore model of 45 calcium antagonistically active 1,4-dihydropyridines (DHP) in order to find best correlation of interaction fields and biological activity. Analysis for the entire data set yielded r2/q values in a range starting from 0.821/0.620 (GRID/GOLPE) over 0.872/0.600 (CoMFA) to 0.908/0.744 (CoMSIA). The robustness of these models was tested not only via leave-one-out but also by leave-9-out crossvalidations. Furthermore, models were constructed using a subset of 37 DHPs (training set) allowing the prediction of activity for the residual 8 DHPs (test set). The training set yielded r2/q values starting from 0.826/0.672 (GRID/GOLPE) over 0.872/0.540 (CoMFA) to 0.899/0.662 (CoMSIA). For the test set r values from 0.677 (GRID/GOLPE) over 0.639 (CoMFA) to 0.470 (CoMSIA) were calculated. Besides the statistics, each 3D QSAR model yields further information by analysis of the generated contour maps. Consideration of the CoMFA and CoMSIA fields indicates unfavourable steric interactions for bulky moieties in 4′-position. On the other hand, sterical demanding 2′- and 3′-substituents are favourable and the biological activity of DHPs is further increased if these moieties produce a negative electrostatic potential. In contrast, high π-electron density on top of and parallel to the 4-phenyl ring beside the 2′-position is associated with decreasing activity. This could point to repulsive electronic interactions with binding site residues or to the potential of electron-deficient 4-aryl moieties to behave as electron acceptors in a charge transfer (CT) mechanism.

ChemInform Abstract: The Comparative Molecular Surface Analysis (COMSA) — A Nongrid 3D QSAR Method by a Coupled Neural Network and PLS System: Predicting pKa Values of Benzoic and Alkanoic Acids.

AbstractChemInform is a weekly Abstracting Service, delivering concise information at a glance that was extracted from about 100 leading journals. To access a ChemInform Abstract of an article which was published elsewhere, please select a “Full Text” option. The original article is trackable via the “References” option.