Quantitative Structure-activity Relationship Data Research Articles

Computational-based investigation of antioxidative potential polyphenolic compounds of Salvia officinalis L.: combined DFT and molecular docking approaches.

The antioxidant properties of the three polyphenolic compounds (carnosol, cirsiliol, and luteolin) of Salvia officinalis L. were investigated employing the density functional theory (DFT) calculations at the B3LYP of basis set at 6-311 + + G (d, p) in order to evaluate their antioxidant activity. The enthalpies of reactions associated with the SET-PT, SPLET, and HAT mechanisms were analyzed in gas and in different solvents using the CPCM (conductor-like polarizable continuum) model. For all possible hydrogen donor sites, the corresponding parameters (BDE, AIP, PDE, PA, ETE, HOMOs, and LUMOs) and reactivity indices (IPE, EA, Χ, η, S, and ω) were also evaluated. The calculated results showed that derivatives 12-OH, 11-OH, 4'-OH, and 3'-OH had the lowest antioxidant activity. The results showed as well that carnosol, cirsiliol, and luteolin have higher reactivity compared to ascorbic acid and could be considered better antioxidants. According to research, the catechol group is crucial in influencing the studied compounds antioxidant activity. The theoretically predicted order of antioxidant efficiencies in this work agrees well with the QSAR (quantitative structure-activity relationship) data. The findings show that in the vacuum as well as benzene media. HAT would be the most effective mechanism; in contrast, the thermodynamic equilibrium approach in polar media is the SPLET mechanism. Likewise, the outcomes of the docking modeling confirm that the selected molecules have high inhibitory activity to glutathione-S-transferases (GSTs) receptors. Moreover, they have very important pharmacokinetic, chemical, and biological profiles. Finally, all the results show that the three natural molecules have good pharmacokinetic profiles, particularly the bioavailability and permeability toward biological membranes. The software packages used in this investigation are Gaussian 16, Discovery studio Visualizer, and AutoDock vina. The three compounds (carnosol, cirsiliol, and luteolin) of Salvia officinalis L. were optimized with DFT/B3LYP of basis set at 6-311 + + G (d, p). The optimized structures were established via vibrational analysis (i.e., no imaginary frequencies in the frequency set). All enthalpies were zero-point (ZPE) corrected. Vibrational frequency calculations were performed at 298.15K and 1 atmosphere pressure to determine the thermodynamic characteristics of the investigated reactions. The descriptors were associated with the antioxidant mechanisms for investigated molecules in vacuum and in various solvents. The molecular docking was used by AutoDock vina to estimate and evaluate the title compounds compatibility as potential antioxidant drugs utilizing appropriate receptor proteins. The solvation effect in the medium of benzene (ɛ = 2.27) and water (ɛ = 78.39) was taken into account. Furthermore, a methanol solvent (ɛ = 32.61) was also taken into consideration to compare with the empirical data.

Machine learning and traditional QSAR modeling methods: a case study of known PXR activators

Pregnane X receptor (PXR), extensively expressed in human tissues related to digestion and metabolism, is responsible for recognizing and detoxifying diverse xenobiotics encountered by humans. To comprehend the promiscuous nature of PXR and its ability to bind a variety of ligands, computational approaches, viz., quantitative structure–activity relationship (QSAR) models, aid in the rapid dereplication of potential toxicological agents and mitigate the number of animals used to establish a meaningful regulatory decision. Recent advancements in machine learning techniques accommodating larger datasets are expected to aid in developing effective predictive models for complex mixtures (viz., dietary supplements) before undertaking in-depth experiments. Five hundred structurally diverse PXR ligands were used to develop traditional two-dimensional (2D) QSAR, machine-learning-based 2D-QSAR, field-based three-dimensional (3D) QSAR, and machine-learning-based 3D-QSAR models to establish the utility of predictive machine learning methods. Additionally, the applicability domain of the agonists was established to ensure the generation of robust QSAR models. A prediction set of dietary PXR agonists was used to externally-validate generated QSAR models. QSAR data analysis revealed that machine-learning 3D-QSAR techniques were more accurate in predicting the activity of external terpenes with an external validation squared correlation coefficient (R 2) of 0.70 versus an R 2 of 0.52 in machine-learning 2D-QSAR. Additionally, a visual summary of the binding pocket of PXR was assembled from the field 3D-QSAR models. By developing multiple QSAR models in this study, a robust groundwork for assessing PXR agonism from various chemical backbones has been established in anticipation of the identification of potential causative agents in complex mixtures. Communicated by Ramaswamy H. Sarma

Predicting rejection of emerging contaminants through RO membrane filtration based on ANN-QSAR modeling approach: trends in molecular descriptors and structures towards rejections.

In this work, a quantitative structure-activity relationship (QSAR) study was performed on a set of emerging contaminants (ECs) to predict their rejections by reverse osmosis membrane (RO). A wide range of molecular descriptors was calculated by Dragon software for 72 ECs. The QSAR data was analyzed by an artificial neural network method (ANN), in which four out of 3000 theoretical molecular descriptors were chosen and their significance was computed based on the Garson method. The significance trends of descriptors were as follows in descending order: ESpm14u > R2e > SIC1 > EEig03d. The selected descriptors were ranked based on their importance and then an explorative study was conducted on the QSAR data to show the trends in molecular descriptors and structures toward the rejections values of ECs. The MLR algorithm was used to make a linear model and the results were compared with those of the nonlinear ANN algorithm. The comparison results revealed it is necessary to apply the ANN model to this data with non-linear properties. For the whole dataset, the correlation coefficient (R2) and residual mean squared error (RMSE) of the ANN and MLR methods were 0.9528, 6.4224; and 0.8753, 11.3400, respectively. The comparison results showed the superiority of ANN modeling in the analysis of ECs' QSAR data.

Predicting the aggregation number of cationic surfactants based on ANN-QSAR modeling approaches: understanding the impact of molecular descriptors on aggregation numbers.

In this work, a quantitative structure-activity relationship (QSAR) study is performed on some cationic surfactants to evaluate the relationship between the molecular structures of the compounds with their aggregation numbers (AGGNs) in aqueous solution at 25 °C. An artificial neural network (ANN) model is combined with the QSAR study to predict the aggregation number of the surfactants. In the ANN analysis, four out of more than 3000 molecular descriptors were used as input variables, and the complete set of 41 cationic surfactants was randomly divided into a training set of 29, a test set of 6, and a validation set of 6 molecules. After that, a multiple linear regression (MLR) analysis was utilized to build a linear model using the same descriptors and the results were compared statistically with those of the ANN analysis. The square of the correlation coefficient (R 2) and root mean square error (RMSE) of the ANN and MLR models (for the whole data set) were 0.9392, 7.84, and 0.5010, 22.52, respectively. The results of the comparison revealed the efficiency of ANN in detecting a correlation between the molecular structure of surfactants and their AGGN values with a high predictive power due to the non-linearity in the studied data. Based on the ANN algorithm, the relative importance of the selected descriptors was computed and arranged in the following descending order: H-047 > ESpm12x > JGI6> Mor20p. Then, the QSAR data was interpreted and the impact of each descriptor on the AGGNs of the molecules were thoroughly discussed. The results showed there is a correlation between each selected descriptor and the AGGN values of the surfactants.

Reliable Model Selection without Reference Values by Utilizing Model Diversity with Prediction Similarity.

Predictive modeling (calibration or training) with various data formats, such as near-infrared (NIR) spectra and quantitative structure-activity relationship (QSAR) data, provides essential information if a proper model is selected. Similarly, with a general model selection approach, spectral model maintenance (updating) from original modeling conditions to new conditions can be performed for dynamic modeling. Fundamental modeling (partial least-squares (PLS) and others) and maintenance processes (domain adaptation or transfer learning and others) require selection of tuning parameter(s) values to isolate models that can accurately predict new samples or molecules, e.g., number of PLS latent variables to predict analyte concentration. Regardless of the modeling task, model selection is complex and without a reliable protocol. Tuning parameter selection typically depends on only one model quality measure assessing model bias using prediction accuracy. Developed in this paper is a generic model selection process using concepts from consensus modeling and QSAR activity landscapes. It is a consensus filtering approach that prioritizes model diversity (MD) while conserving prediction similarity (PS) fused with a common bias-variance trade-off measure. A significant feature of MDPS is that a cross-validation scheme is not needed because models are selected relative to predicting new samples or molecules, i.e., model selection uses unlabeled samples (without reference values) for active predictions. The versatility and reliability of MDPS model selection is shown using four NIR data sets and a QSAR data set. The study also substantiates the Rashomon effect where there is not one best model tuning parameter value that provides accurate predictions.

Auto-KPCA: A Two-Step Hybrid Feature Extraction Technique for Quantitative Structure–Activity Relationship Modeling

Quantitative structure-activity relationship (QSAR) modeling is an established approach for drug discovery, but many QSAR datasets suffer from the curse of dimensionality, a challenge that is usually addressed by using dimensionality reduction techniques such as principal component analysis (PCA). However, although linear feature extraction techniques have low computational cost and can handle linear relationships between descriptors, they cannot handle the complex structures found in QSAR data. Hybridization of feature extraction techniques is an effective approach to address the challenges of high-dimensional datasets, and combining the benefits of at least two dimensionality reduction techniques has been successful in many fields. This paper proposes Auto-KPCA, a two-step hybrid feature extraction technique that leverages (i) the fast computational capability of kernel PCA (KPCA) and (ii) the performance of a deep generalized autoencoder in handling complex data structures. Based on classification accuracy, the proposed approach is compared to other feature extraction techniques on the same benchmark dataset. The capability of Auto-KPCA is then investigated further by testing four deep-learning classification models, namely a convolutional neural network, a recurrent neural network, a feedforward deep neural network, and long short-term memory. To the best of the authors' knowledge, this study is the first to investigate hybridization of KPCA and a deep generalized autoencoder in the context of QSAR. The reported results (i) provide invaluable insights regarding the behavior of different techniques in predicting class labels and (ii) demonstrate increased classification accuracy and noticeably decreased mean square error when compared with KPCA and autoencoders.

Do we need different machine learning algorithms for QSAR modeling? A comprehensive assessment of 16 machine learning algorithms on 14 QSAR data sets.

Although a wide variety of machine learning (ML) algorithms have been utilized to learn quantitative structure-activity relationships (QSARs), there is no agreed single best algorithm for QSAR learning. Therefore, a comprehensive understanding of the performance characteristics of popular ML algorithms used in QSAR learning is highly desirable. In this study, five linear algorithms [linear function Gaussian process regression (linear-GPR), linear function support vector machine (linear-SVM), partial least squares regression (PLSR), multiple linear regression (MLR) and principal component regression (PCR)], three analogizers [radial basis function support vector machine (rbf-SVM), K-nearest neighbor (KNN) and radial basis function Gaussian process regression (rbf-GPR)], six symbolists [extreme gradient boosting (XGBoost), Cubist, random forest (RF), multiple adaptive regression splines (MARS), gradient boosting machine (GBM), and classification and regression tree (CART)] and two connectionists [principal component analysis artificial neural network (pca-ANN) and deep neural network (DNN)] were employed to learn the regression-based QSAR models for 14 public data sets comprising nine physicochemical properties and five toxicity endpoints. The results show that rbf-SVM, rbf-GPR, XGBoost and DNN generally illustrate better performances than the other algorithms. The overall performances of different algorithms can be ranked from the best to the worst as follows: rbf-SVM > XGBoost > rbf-GPR > Cubist > GBM > DNN > RF > pca-ANN > MARS > linear-GPR ≈ KNN > linear-SVM ≈ PLSR > CART ≈ PCR ≈ MLR. In terms of prediction accuracy and computational efficiency, SVM and XGBoost are recommended to the regression learning for small data sets, and XGBoost is an excellent choice for large data sets. We then investigated the performances of the ensemble models by integrating the predictions of multiple ML algorithms. The results illustrate that the ensembles of two or three algorithms in different categories can indeed improve the predictions of the best individual ML algorithms.

Feature Extraction Methods in Quantitative Structure–Activity Relationship Modeling: A Comparative Study

Computational approaches for synthesizing new chemical compounds have resulted in a major explosion of chemical data in the field of drug discovery. The quantitative structure–activity relationship (QSAR) is a widely used classification and regression method used to represent the relationship between a chemical structure and its activities. This research focuses on the effect of dimensionality-reduction techniques on a high-dimensional QSAR dataset. Because of the multi-dimensional nature of QSAR, dimensionality-reduction techniques have become an integral part of its modeling process. Principal component analysis (PCA) is a feature extraction technique with several applications in exploratory data analysis, visualization and dimensionality reduction. However, linear PCA is inadequate to handle the complex structure of QSAR data. In light of the wide array of current feature-extraction techniques, we perform a comparative empirical study to investigate five feature-extraction techniques: PCA, kernel PCA, deep generalized autoencoder (dGAE), Gaussian random projection (GRP), and sparse random projection (SRP). The experiments are performed on a high-dimensional QSAR dataset, which comprises 6394 features. The transformed low-dimensional dataset is inputted into a deep learning classification model to predict a QSAR biological activity. Three approaches are adopted to validate and measure the proposed techniques: (i) comparing the performance of the classification models, (ii) visualizing the relationship (correlation) between features in the low-dimension Euclidean space, and (iii) validating the proposed techniques using an external dataset. To the best of our knowledge, this study is the first to investigate and compare the aforementioned feature-extraction techniques in QSAR modeling context. The results obtained provide invaluable insights regarding the behavior of different techniques with both negative and positive classes. With linear PCA as a baseline, we prove that the investigated techniques substantially outperform the baseline in multiple accuracy measures and demonstrate useful ways of extracting significant features.

Analysis and Comparison of Vector Space and Metric Space Representations in QSAR Modeling.

The performance of quantitative structure–activity relationship (QSAR) models largely depends on the relevance of the selected molecular representation used as input data matrices. This work presents a thorough comparative analysis of two main categories of molecular representations (vector space and metric space) for fitting robust machine learning models in QSAR problems. For the assessment of these methods, seven different molecular representations that included RDKit descriptors, five different fingerprints types (MACCS, PubChem, FP2-based, Atom Pair, and ECFP4), and a graph matching approach (non-contiguous atom matching structure similarity; NAMS) in both vector space and metric space, were subjected to state-of-art machine learning methods that included different dimensionality reduction methods (feature selection and linear dimensionality reduction). Five distinct QSAR data sets were used for direct assessment and analysis. Results show that, in general, metric-space and vector-space representations are able to produce equivalent models, but there are significant differences between individual approaches. The NAMS-based similarity approach consistently outperformed most fingerprint representations in model quality, closely followed by Atom Pair fingerprints. To further verify these findings, the metric space-based models were fitted to the same data sets with the closest neighbors removed. These latter results further strengthened the above conclusions. The metric space graph-based approach appeared significantly superior to the other representations, albeit at a significant computational cost.

Sparse Generative Topographic Mapping for Both Data Visualization and Clustering.

To achieve simultaneous data visualization and clustering, the method of sparse generative topographic mapping (SGTM) is developed by modifying the conventional GTM algorithm. While the weight of each grid point is constant in the original GTM, it becomes a variable in the proposed SGTM, enabling data points to be clustered on two-dimensional maps. The appropriate number of clusters is determined by optimization based on the Bayesian information criterion. Analysis of numerical simulation data sets along with quantitative structure-property relationship and quantitative structure-activity relationship data sets confirmed that the proposed SGTM provides the same degree of visualization performance as the original GTM and clusters data points appropriately. Python and MATLAB codes for the proposed algorithm are available at https://github.com/hkaneko1985/gtm-generativetopographicmapping .

Optimal Piecewise Linear Regression Algorithm for QSAR Modelling.

Quantitative Structure-Activity Relationship (QSAR) models have been successfully applied to lead optimisation, virtual screening and other areas of drug discovery over the years. Recent studies, however, have focused on the development of models that are predictive but often not interpretable. In this article, we propose the application of a piecewise linear regression algorithm, OPLRAreg, to develop both predictive and interpretable QSAR models. The algorithm determines a feature to best separate the data into regions and identifies linear equations to predict the outcome variable in each region. A regularisation term is introduced to prevent overfitting problems and implicitly selects the most informative features. As OPLRAreg is based on mathematical programming, a flexible and transparent representation for optimisation problems, the algorithm also permits customised constraints to be easily added to the model. The proposed algorithm is presented as a more interpretable alternative to other commonly used machine learning algorithms and has shown comparable predictive accuracy to Random Forest, Support Vector Machine and Random Generalised Linear Model on tests with five QSAR data sets compiled from the ChEMBL database.

Discussion on Regression Methods Based on Ensemble Learning and Applicability Domains of Linear Submodels.

To develop a new ensemble learning method and construct highly predictive regression models in chemoinformatics and chemometrics, applicability domains (ADs) are introduced into the ensemble learning process of prediction. When estimating values of an objective variable using subregression models, only the submodels with ADs that cover a query sample, i.e., the sample is inside the model's AD, are used. By constructing submodels and changing a list of selected explanatory variables, the union of the submodels' ADs, which defines the overall AD, becomes large, and the prediction performance is enhanced for diverse compounds. By analyzing a quantitative structure-activity relationship data set and a quantitative structure-property relationship data set, it is confirmed that the ADs can be enlarged and the estimation performance of regression models is improved compared with traditional methods.

A combined Fisher and Laplacian score for feature selection in QSAR based drug design using compounds with known and unknown activities.

Quantitative structure-activity relationship (QSAR) is an effective computational technique for drug design that relates the chemical structures of compounds to their biological activities. Feature selection is an important step in QSAR based drug design to select the most relevant descriptors. One of the most popular feature selection methods for classification problems is Fisher score which aim is to minimize the within-class distance and maximize the between-class distance. In this study, the properties of Fisher criterion were extended for QSAR models to define the new distance metrics based on the continuous activity values of compounds with known activities. Then, a semi-supervised feature selection method was proposed based on the combination of Fisher and Laplacian criteria which exploits both compounds with known and unknown activities to select the relevant descriptors. To demonstrate the efficiency of the proposed semi-supervised feature selection method in selecting the relevant descriptors, we applied the method and other feature selection methods on three QSAR data sets such as serine/threonine-protein kinase PLK3 inhibitors, ROCK inhibitors and phenol compounds. The results demonstrated that the QSAR models built on the selected descriptors by the proposed semi-supervised method have better performance than other models. This indicates the efficiency of the proposed method in selecting the relevant descriptors using the compounds with known and unknown activities. The results of this study showed that the compounds with known and unknown activities can be helpful to improve the performance of the combined Fisher and Laplacian based feature selection methods.

Demystifying Multitask Deep Neural Networks for Quantitative Structure-Activity Relationships.

Deep neural networks (DNNs) are complex computational models that have found great success in many artificial intelligence applications, such as computer vision1,2 and natural language processing.3,4 In the past four years, DNNs have also generated promising results for quantitative structure-activity relationship (QSAR) tasks.5,6 Previous work showed that DNNs can routinely make better predictions than traditional methods, such as random forests, on a diverse collection of QSAR data sets. It was also found that multitask DNN models-those trained on and predicting multiple QSAR properties simultaneously-outperform DNNs trained separately on the individual data sets in many, but not all, tasks. To date there has been no satisfactory explanation of why the QSAR of one task embedded in a multitask DNN can borrow information from other unrelated QSAR tasks. Thus, using multitask DNNs in a way that consistently provides a predictive advantage becomes a challenge. In this work, we explored why multitask DNNs make a difference in predictive performance. Our results show that during prediction a multitask DNN does borrow "signal" from molecules with similar structures in the training sets of the other tasks. However, whether this borrowing leads to better or worse predictive performance depends on whether the activities are correlated. On the basis of this, we have developed a strategy to use multitask DNNs that incorporate prior domain knowledge to select training sets with correlated activities, and we demonstrate its effectiveness on several examples.

Feature selection based on graph Laplacian by using compounds with known and unknown activities

A semisupervised feature selection method based on graph Laplacian (S2FSGL) was proposed for quantitative structure‐activity relationship (QSAR) models, which uses an ℓ2,1‐norm and compounds with both known and unknown activities. In the proposed S2FSGL method, 2 graphs Gunsup and Gsup are constructed. It uses the label information of compounds with known activities and the local structure of compounds with known and unknown activities to select the most important descriptors. The weight matrix of graph Gunsup models the local structure of the compounds with known and unknown activities. The S2FSGL method uses the ℓ2,1‐norm to consider the correlation between different descriptors when conducting descriptor selection. The performance of the proposed S2FSGL coupled with a kernel smoother model was evaluated using 2 QSAR data sets and compared with the performance of other feature selection methods. For the evaluation of the performance of QSAR models and selected descriptors, several different training and test sets were produced for each data set. The comparison between the statistical parameters of QSAR models built based on the semisupervised feature selection method and those obtained by other feature selection methods revealed the superiority of the proposed S2FSGL in selecting the most relevant descriptors. The results showed that the use of compounds with unknown activities beside compounds with known activities can be helpful in selecting the relevant descriptors of QSAR models.

Combining QSAR Modeling and Text-Mining Techniques to Link Chemical Structures and Carcinogenic Modes of Action.

There is an increasing need for new reliable non-animal based methods to predict and test toxicity of chemicals. Quantitative structure-activity relationship (QSAR), a computer-based method linking chemical structures with biological activities, is used in predictive toxicology. In this study, we tested the approach to combine QSAR data with literature profiles of carcinogenic modes of action automatically generated by a text-mining tool. The aim was to generate data patterns to identify associations between chemical structures and biological mechanisms related to carcinogenesis. Using these two methods, individually and combined, we evaluated 96 rat carcinogens of the hematopoietic system, liver, lung, and skin. We found that skin and lung rat carcinogens were mainly mutagenic, while the group of carcinogens affecting the hematopoietic system and the liver also included a large proportion of non-mutagens. The automatic literature analysis showed that mutagenicity was a frequently reported endpoint in the literature of these carcinogens, however, less common endpoints such as immunosuppression and hormonal receptor-mediated effects were also found in connection with some of the carcinogens, results of potential importance for certain target organs. The combined approach, using QSAR and text-mining techniques, could be useful for identifying more detailed information on biological mechanisms and the relation with chemical structures. The method can be particularly useful in increasing the understanding of structure and activity relationships for non-mutagens.

In silico modeling of β-carbonic anhydrase inhibitors from the fungus Malassezia globosa as antidandruff agents

A quantitative structure–activity relationship (QSAR) study of sulfonamide inhibitors targeting the β-carbonic anhydrase (CA, EC 4.2.1.1) from the fungus Malassezia globosa is reported. A large set of PRECLAV descriptors has been used to obtain four parametric models. This study presents QSAR data on a pool of 28 compounds. The quality of prediction is high enough (SE = 0.3446, r2 = 0.8687, F = 39.6921, Q = 0.7446). A heuristic algorithm selected the best multiple linear regression (MLR) equation which showed the correlation between the observed values and the calculated values of activity. The proposed prediction set included new, not yet synthesized, 23 molecules having various structures. Many compounds in the prediction set seem to possess higher computed activity compared to the presently available M. globosa β-CA inhibitors.

Deep neural nets as a method for quantitative structure-activity relationships.

Neural networks were widely used for quantitative structure-activity relationships (QSAR) in the 1990s. Because of various practical issues (e.g., slow on large problems, difficult to train, prone to overfitting, etc.), they were superseded by more robust methods like support vector machine (SVM) and random forest (RF), which arose in the early 2000s. The last 10 years has witnessed a revival of neural networks in the machine learning community thanks to new methods for preventing overfitting, more efficient training algorithms, and advancements in computer hardware. In particular, deep neural nets (DNNs), i.e. neural nets with more than one hidden layer, have found great successes in many applications, such as computer vision and natural language processing. Here we show that DNNs can routinely make better prospective predictions than RF on a set of large diverse QSAR data sets that are taken from Merck's drug discovery effort. The number of adjustable parameters needed for DNNs is fairly large, but our results show that it is not necessary to optimize them for individual data sets, and a single set of recommended parameters can achieve better performance than RF for most of the data sets we studied. The usefulness of the parameters is demonstrated on additional data sets not used in the calibration. Although training DNNs is still computationally intensive, using graphical processing units (GPUs) can make this issue manageable.

Pharmacophore modeling of nilotinib as an inhibitor of ATP-binding cassette drug transporters and BCR-ABL kinase using a three-dimensional quantitative structure-activity relationship approach.

Nilotinib (Tasigna) is a tyrosine kinase inhibitor approved by the FDA to treat chronic phase chronic myeloid leukemia patients. It is also a transport substrate of the ATP-binding cassette (ABC) drug efflux transporters ABCB1 (P-glycoprotein, P-gp) and ABCG2 (BCRP), which may have an effect on the pharmacokinetics and toxicity of this drug. The goal of this study was to identify pharmacophoric features of nilotinib in order to potentially develop specific inhibitors of BCR-ABL kinase with minimal interactions with ABC drug transporters. Three-dimensional pharmacophore modeling and quantitative structure–activity relationship (QSAR) studies were carried out on a series of nilotinib analogues to identify chemical features that contribute to inhibitory activity of nilotinib against BCR-ABL kinase activity, P-gp, and ABCG2. Twenty-five derivatives of nilotinib were synthesized and were then tested to measure their activity to inhibit BCR-ABL kinase and to inhibit the function of ABC drug transporters. A set of in vitro experiments including kinase activity and cell-based transport assays and photolabeling of P-gp and ABCG2 with a transport substrate, [125I]-iodoarylazido-prazosin (IAAP), were carried out in isolated membranes to evaluate the potency of the derivatives to inhibit the function of ABC drug transporters and BCR-ABL kinase. Sixteen, fourteen, and ten compounds were selected as QSAR data sets, respectively, to generate PHASE v3.1 pharmacophore models for BCR-ABL kinase, ABCG2, and P-gp inhibitors. The IC50 values of these derivatives against P-gp, ABCG2, or BCR-ABL kinase were used to generate pharmacophore features required for optimal interactions with these targets. A seven-point pharmacophore (AADDRRR) for BCR-ABL kinase inhibitory activity, a six-point pharmacophore (ADHRRR) for ABCG2 inhibitory activity, and a seven-point pharmacophore (AADDRRR) for P-gp inhibitory activity were generated. The derived models clearly demonstrate high predictive power for test sets of BCR-ABL, ABCG2, and P-gp inhibitors. In aggregate, these results should aid in the development of specific inhibitors of BCR-ABL kinase that exhibit no or minimal interaction with ABC drug transporters.

QSAR DataBank - an approach for the digital organization and archiving of QSAR model information.

BackgroundResearch efforts in the field of descriptive and predictive Quantitative Structure-Activity Relationships or Quantitative Structure–Property Relationships produce around one thousand scientific publications annually. All the materials and results are mainly communicated using printed media. The printed media in its present form have obvious limitations when they come to effectively representing mathematical models, including complex and non-linear, and large bodies of associated numerical chemical data. It is not supportive of secondary information extraction or reuse efforts while in silico studies poses additional requirements for accessibility, transparency and reproducibility of the research. This gap can and should be bridged by introducing domain-specific digital data exchange standards and tools. The current publication presents a formal specification of the quantitative structure-activity relationship data organization and archival format called the QSAR DataBank (QsarDB for shorter, or QDB for shortest).ResultsThe article describes QsarDB data schema, which formalizes QSAR concepts (objects and relationships between them) and QsarDB data format, which formalizes their presentation for computer systems. The utility and benefits of QsarDB have been thoroughly tested by solving everyday QSAR and predictive modeling problems, with examples in the field of predictive toxicology, and can be applied for a wide variety of other endpoints. The work is accompanied with open source reference implementation and tools.ConclusionsThe proposed open data, open source, and open standards design is open to public and proprietary extensions on many levels. Selected use cases exemplify the benefits of the proposed QsarDB data format. General ideas for future development are discussed.