Developed QSAR Models Research Articles

QSAR Models for Nitrogen Containing Monophosphonate and Bisphosphonate Derivatives as Human Farnesyl Pyrophosphate Synthase Inhibitors Based on Monte Carlo Method.

Human farnesyl pyrophosphate synthase (hFPPS) is a well-settled therapeutic target and it is an enzyme of the mevalonate pathway which catalyzes the biosynthesis of the C-15 isoprenoid farnesyl pyrophosphate. QSAR studies by using Monte Carlo method for human farnesyl pyrophosphate synthase inhibitors has been carried out using balance of correlation technique with Index of ideality correlation. For construction of QSAR models, six random splits were prepared from the data of 73 phosphonates and hybrid optimal descriptors procured from graph (HFG) and SMILES based notations were employed. The developed QSAR models have robustness, good fitting ability, generalizability and internal predictive ability. The external predictive ability has been certified by testing various precedents. The values of R2, IIC, Q2 and ∆R2 m for the best model are 0.9304, 0.9614, 0.9061 and 0.0861 respectively. The developed QSAR models met with the specified standards given in OECD guideline and applicability domain. The structural feature promoters for the end point increase and promoters for end point decrease have been extracted. The predicted pIC50 for the new proposed compounds have also been reported.

QSAR and QAAR Studies on Mixtures of 3-(Benzylidene)Indolin-2-One Isomers as Leads to Develop PET Radiotracers for Detection of Parkinson's Disease

Deposition of α–synuclein, tau and β–amyloid protein plaques in brain leads to neurodegeneration. A series of indolin derivatives, which can bind to α–synuclein and detect Parkinson's disease (PD), were used for development of QSAR and QAAR models. It is the first attempt of QSAR for any radiotracer agents used for detection of PD. The binding affinity against α–synuclein was used as dependent variable while independent variables, such as structural, topological, E-state keys, electronic, molecular shape analysis and spatial molecular descriptors were used for QSAR modeling. For QAAR modeling, the binding affinities of molecules for tau and β–amyloid along with different molecular descriptors were used as independent variables. All models were successfully developed using multiple linear regression method, and validated internally and externally, based on different standard criteria. This article describes how the derived models postulate that conformation of molecules and presence of unsaturated hydrocarbon chains, nitro, methoxy and amine functionalities play an important role in determining binding affinity.

Activity and toxicity modelling of some NCI selected compounds against leukemia P388ADR cell line using genetic algorithm-multiple linear regressions

Cancer-causing nature is one of the toxicological endpoints bringing about the most elevated concern. Likewise, the standard bioassays in rodents used to survey the cancer-mitigating capability of chemicals and medications are expensive and require the sacrifice of animals. Thus, we have endeavored the development of a worldwide QSAR model utilizing an information set of 85 compounds, including drugs for their anti-leukemia potential. Considering expansive number of information focuses with different structural elements utilized for model development (ntraining = 68) and model validation (ntest = 17), the model developed in this study has an encouraging statistical quality (leave-one-out Q2 = 0.833, R2pred = 0.716) for pLC50 and (leave-one-out Q2 = 0.744, R2pred = 0.614) for pGI50. Our developed model suggests that the absence of methanal fragments, low dipole moment and presence of some 2D autocorrelated molecular descriptors reduces the carcinogenicity. Branching, size and shape are found to be crucial factors for drug-mitigating carcinogenicity.

QSARpy: A new flexible algorithm to generate QSAR models based on dissimilarities. The log Kow case study

Several methods exist to develop QSAR models automatically. Some are based on indices of the presence of atoms, other on the most similar compounds, other on molecular descriptors. Here we introduce QSARpy v1.0, a new QSAR modeling tool based on a different approach: the dissimilarity. This tool fragments the molecules of the training set to extract fragments that can be associated to a difference in the property/activity value, called modulators. If the target molecule share part of the structure with a molecule of the training set and differences can be explained with one or more modulators, the property/activity value of the molecule of the training set is adjusted using the value associated to the modulator(s). This tool is tested here on the n-octanol/water partition coefficient (Kow, usually expressed in logarithmic units as log Kow). It is a key parameter in risk assessment since it is a measure of hydrophobicity. Its wide spread use makes these estimation methods very useful to reduce testing costs. Using QSARpy v1.0, we obtained a new model to predict log Kow with accurate performance (RMSE 0.43 and R2 0.94 for the external test set), comparing favorably with other programs. QSARpy is freely available on request.

DISCOVERY OF NOVEL AND SELECTIVE C-JUN NH2-TERMINAL KINASES 2 INHIBITORS BY TWO-DIMENSIONAL QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP MODEL DEVELOPMENT, MOLECULAR DOCKING AND ABSORPTION, DISTRIBUTION, METABOLISM, ELIMINATION PREDICTION STUDIES: AN IN SILICO APPROACH

Objective: Incited by the dearth of selective c-Jun NH2-terminal kinases (JNK) inhibitors, efforts have been made to design novel JNK2 inhibitors with good selectivity profile by utilizing a set of in silico tools.Methods: The present study involved 2D QSAR model development through multiple linear regression and partial least square methods. Further, the information unveiled through the above model was meticulously utilized to design novel JNK2 inhibitors (compound a and compound b). The selectivity of the novel molecules was ascertained through the molecular docking experiments. Determination of Tanimoto similarity index and absorption, distribution, metabolism, and elimination (ADME) properties was performed to ascertain the novelty and drug-like properties of the designed molecules, respectively.Results: Four explanatory variables or descriptors, moment of inertia 2 length (subst.1), moment of inertia 3 length (subst.3), Kier Chi4 (path/ cluster) index (whole molecule), and vamp highest occupied molecular orbital (whole molecule), were found to possess profound influence on the biological activity with the values of standard parameters, s: 0.28, r: 0.96, r2: 0.92, r2CV: 0.82, and f=59.69. Tanimoto index values were found to be 0.095 and 0.075 for compound a and compound b, respectively.Conclusion: The values of the statistical parameters proved the developed model to be of excellent quality. The results obtained for ADME prediction studies proved both the compounds to be better than the most potent compound (compound 45) of the set of JNK2 inhibitors selected for model development. In addition, extremely low Tanimoto similarity index values for both the compounds provided sufficient evidence for the novelty of the designed molecules.Objective: Incited by the dearth of selective c-Jun NH2-terminal kinases (JNK) inhibitors, efforts have been made to design novel JNK2 inhibitors with good selectivity profile by utilizing a set of in silico tools.Methods: The present study involved 2D QSAR model development through multiple linear regression and partial least square methods. Further, the information unveiled through the above model was meticulously utilized to design novel JNK2 inhibitors (compound a and compound b). The selectivity of the novel molecules was ascertained through the molecular docking experiments. Determination of Tanimoto similarity index and absorption, distribution, metabolism, and elimination (ADME) properties was performed to ascertain the novelty and drug-like properties of the designed molecules, respectively.Results: Four explanatory variables or descriptors, moment of inertia 2 length (subst.1), moment of inertia 3 length (subst.3), Kier Chi4 (path/ cluster) index (whole molecule), and vamp highest occupied molecular orbital (whole molecule), were found to possess profound influence on the biological activity with the values of standard parameters, s: 0.28, r: 0.96, r2: 0.92, r2CV: 0.82, and f=59.69. Tanimoto index values were found to be 0.095 and 0.075 for compound a and compound b, respectively.Conclusion: The values of the statistical parameters proved the developed model to be of excellent quality. The results obtained for ADME prediction studies proved both the compounds to be better than the most potent compound (compound 45) of the set of JNK2 inhibitors selected for model development. In addition, extremely low Tanimoto similarity index values for both the compounds provided sufficient evidence for the novelty of the designed molecules.

QSAR classification and regression models for β-secretase inhibitors using relative distance matrices

The development of robust QSAR models to predict the activity of molecules of β-secretase inhibitors is an area of interest due to the increase of Alzheimer’s disease in patients in the global population. In this paper, we present a proposal based on the use of relative distance matrices as input data to the QSAR algorithms. These matrices store measurements of distances between the structural characteristics of pairs of molecules and between the molecules and a structural pattern extracted from the whole data set, thus efficiently representing a correlation between structural changes and activity. For the building of the classification and regression models support vector machine, tree complex and Gaussian process algorithms have been used; and for the validation of the models cross-validation, bootstrapping and y-randomizing techniques have been applied. The results obtained are close to 100% in accuracy and area under receiver operating characteristic values in classification, and close to 1.0 for r2 and 0.1 for root mean square error in regression in training and in external validation, proving the ‘goodness’ of the proposal.

Consensus QSAR modelling of SIRT1 activators using simplex representation of molecular structure

Hierarchical QSAR technology (HiT QSAR) was used for consensus QSAR modelling of 65 SIRT1 activators. Simplex representation of molecular structure (SiRMS) has been used for descriptor generation. The predictive QSAR models were developed using the partial least squares (PLS) method. The QSAR models were built up according to OECD principles. One hundred rounds of Y-scrambling were performed for each selected model to exclude chance correlations. A successful consensus model (r2 = 0.830, = 0.754) was obtained from the five best QSAR models. Leverage, ellipsoid and local tree domain of applicability (DA) approaches have been used for evaluation of the quality of predictions. Molecular fragments responsible for an increase and decrease of the activation properties have been determined by mechanistic interpretation of the developed QSAR model.

Is it possible to improve the quality of predictions from an “intelligent” use of multiple QSAR/QSPR/QSTR models?

AbstractQuantitative structure‐activity/property/toxicity relationship (QSAR/QSPR/QSTR) models are effectively employed to fill data gaps by predicting a given response from known structural features or physicochemical properties of new query compounds. The performance of a model should be assessed based on the quality of predictions checked through diverse validation metrics, which confirm the reliability of the developed QSAR models along with the acceptability of their prediction quality for untested compounds. There is an ongoing effort by QSAR modelers to improve the quality of predictions by lowering the predicted residuals for query compounds. In this endeavor, consensus models integrating all validated individual models were found to be more externally predictive than individual models in many previous studies. The objective of this work has been to explore whether the quality of predictions of external compounds can be enhanced through an “intelligent” selection of multiple models. The consensus predictions used in this study are not simple average of predictions from multiple models. It has been considered in the present study that a particular QSAR model may not be equally effective for prediction of all query compounds in the list. Our approach is different from the previous ones in that none of the previously reported methods considered selection of predictive models in a query compound specific way while at the same time using all or most of the valid models for the total set of query chemicals. We have implemented our approach in a software tool that is freely available via the web http://teqip.jdvu.ac.in/QSAR_Tools/ and http://dtclab.webs.com/software‐tools.

Enalos Suite: New Cheminformatics Platform for Drug Discovery and Computational Toxicology.

In this chapter we present and discuss, with the aid of several representative case studies from drug discovery and computational toxicology, a new cheminformatics platform, Enalos Suite, that was developed with open source and freely available software. Enalos Suite ( http://enalossuite.novamechanics.com/ ) was designed and developed as a useful tool to address a variety of cheminformatics problems, given that it expedites tasks performed in predictive modeling and allows access, data mining and manipulation for multiple chemical databases (PubChem, UniChem, etc.). Enalos Suite was carefully designed to permit its extension and adjustment to the special field of interest of each user, including, for instance, nanoinformatics, biomedical, and other applications. To demonstrate the functionalities of Enalos Suite that are useful in different cheminformatics applications, we present indicative case studies that include the exploitation of chemical databases within a drug discovery project, the calculation of molecular descriptors, and finally the development of a predictive QSAR model validated according to OECD principles. We aspire that at the end of this chapter, the reader will capture the effectiveness of different functionalities included in the Enalos Suite that could be of significant value in a multitude of cheminformatics applications.

Development of a multitask deep learning QSAR model using data from individual cytochrome P450 isozymes

Development of a multitask deep learning QSAR model using data from individual cytochrome P450 isozymes

QSAR models for describing the toxicological effects of ILs against Staphylococcus aureus based on norm indexes

The hazardous potential of ionic liquids (ILs) is becoming an issue of great concern due to their important role in many industrial fields as green agents. The mathematical model for the toxicological effects of ILs is useful for the risk assessment and design of environmentally benign ILs. The objective of this work is to develop QSAR models to describe the minimal inhibitory concentration (MIC) and minimal bactericidal concentration (MBC) of ILs against Staphylococcus aureus (S. aureus). A total of 169 and 101 ILs with MICs and MBCs, respectively, are used to obtain multiple linear regression models based on matrix norm indexes. The norm indexes used in this work are proposed by our research group and they are first applied to estimate the antibacterial toxicity of these ILs against S. aureus. These two models precisely and reliably calculated the IL toxicities with a square of correlation coefficient (R2) of 0.919 and a standard error of estimate (SE) of 0.341 (in log unit of mM) for pMIC, and an R2 of 0.913 and SE of 0.282 for pMBC.

Discovery of molecular mechanism of a clinical herbal formula upregulating serum HDL-c levels in treatment of metabolic syndrome by in vivo and computational studies

Decreased HDL cholesterol (HDL-c) is considered as an independent risk factor of cardiovascular disease in metabolic syndrome (Mets). Wendan decoction (WDD), a famous clinical traditional Chinese medicine formula in Mets in China, which can obviously up-regulate serum HDL-c levels in Mets. However, till now, the molecular mechanism of up-regulation still remained unclear. In this study, an integrated approach that combined serum ABCA1 in vivo assay, QSAR modeling and molecular docking was developed to explore the molecular mechanism and chemical substance basis of WDD upregulating HDL-c levels. Compared with Mets model group, serum ABCA1 and HDL-c levels intervened by two different doses of WDD for two weeks were significantly up-regulated. Then, kohonen and LDA were applied to develop QSAR models for ABCA1 up-regulators based flavonoids. The derived QSAR model produced the overall accuracy of 100%, a very powerful tool for screening ABCA1 up-regulators. The QSAR model prediction revealed 67 flavonoids in WDD were ABCA1 up-regulators. Finally, they were subjected to the molecular docking to understand their roles in up-regulating ABCA1 expression, which led to discovery of 23 ABCA1 up-regulators targeting LXR beta. Overall, QSAR modeling and docking studies well accounted for the observed in vivo activities of ABCA1 affected by WDD.

QSAR Modeling of Aminopeptidase N/CD13 (APN) Inhibitory Activity of some Leucine Ureido Derivatives by GA-MLR and SW-MLR Methods

Background: Aminopeptidase N also known as APN/CD13 is a zinc-dependent type II membrane-bond ectopeptidase that is overexpressed on cancer cells. APN/CD13 is considered as an attractive target for anti-cancer drug design due to key roles in tumor invasion, angiogenesis and metastasis. Therefore, study of quantitative relationships between activity and structures of APN/CD13 inhibitors can provide useful information for the designing and synthesis of novel APN inhibitors. Methods: In this study, the linear method was used to develop QSAR models in order to predict the activities of APN/CD13 inhibitors. A dataset that consisted of 39 leucine ureido derivatives was divided into the training and test subsets. Genetic algorithm and stepwise methods have been employed for selection of relevant descriptors. Results: Multiple linear regression analysis with GA selection was used to model the structureactivity relationships. In this model, R2 was 0.84 for the training set and 0.67 for the test set. We also applied the SW technique as variable selection procedure to the same data set. With the use of SW, the 6 most relevant descriptors were selected to build the model. The value of R2 of the SWMLR model was 0.87 for the training set and 0.77 for the test set. Both models were validated by leave-one-out (LOO) cross-validation. Conclusion: The results of SW-MLR and of GA-MLR were confirmed and is appeared that the SW-MLR model had more power to predict APN inhibitory activity. On the basis of QSAR models, charge polarization, the atomic masses, polarizability, the atomic van der Waals volumes, the molecular symmetry and aromaticity index were found to be important factors controlling the APN inhibitory activity. Keywords: Aminopeptidase, QSAR, multiple linear regressions, stepwise, genetic algorithm, leucine ureido derivatives.

In silico approaches to identify novel myeloid cell leukemia-1 (Mcl-1) inhibitors for treatment of cancer

Myeloid cell leukemia-1 (Mcl-1) has been a validated and attractive target for cancer therapy. Over-expression of Mcl-1 in many cancers allows cancer cells to evade apoptosis and contributes to the resistance to current chemotherapeutics. Here, we identified new Mcl-1 inhibitors using a multi-step virtual screening approach. First, based on two different ligand-receptor complexes, 20 pharmacophore models were established by simultaneously using ‘Receptor-Ligand Pharmacophore Generation’ method and manual build feature method, and then carefully validated by a test database. Then, pharmacophore-based virtual screening (PB-VS) could be performed by using the 20 pharmacophore models. In addition, docking study was used to predict the possible binding poses of compounds, and the docking parameters were optimized before performing docking-based virtual screening (DB-VS). Moreover, a 3D QSAR model was established by applying the 55 aligned Mcl-1 inhibitors. The 55 inhibitors sharing the same scaffold were docked into the Mcl-1 active site before alignment, then the inhibitors with possible binding conformations were aligned. For the training set, the 3D QSAR model gave a correlation coefficient r2 of 0.996; for the test set, the correlation coefficient r2 was 0.812. Therefore, the developed 3D QSAR model was a good model, which could be applied for carrying out 3D QSAR-based virtual screening (QSARD-VS). After the above three virtual screening methods orderly filtering, 23 potential inhibitors with novel scaffolds were identified. Furthermore, we have discussed in detail the mapping results of two potent compounds onto pharmacophore models, 3D QSAR model, and the interactions between the compounds and active site residues.

Novel 2-(2-alkylthiobenzenesulfonyl)-3-(phenylprop-2-ynylideneamino)guanidine derivatives as potent anticancer agents – Synthesis, molecular structure, QSAR studies and metabolic stability

A series of new 2-(2-alkylthiobenzenesulfonyl)-3-(phenylprop-2-ynylideneamino)guanidine derivatives have been synthesized and evaluated in vitro by MTT assays for their antiproliferative activity against cell lines of colon cancer HCT-116, cervical cancer HeLa and breast cancer MCF-7. The obtained results indicated that these compounds display prominent cytotoxic effect. The best anticancer properties have been observed for derivatives 44 (IC50 = 6–18 μM) and 45 (IC50 = 8–14 μM). Very good results of antiproliferative assays have been also shown for compounds 26, 36, and 46 and noticeable anticancer profile has been found for set of derivatives 34–39. Based on results of MTT assays the structure-activity relationships have been drawn. More in-depth biological research revealed that compounds 26, 33, 37, 39, 41 and 43 display cytotoxic effect only against cancer cells and do not inhibit the growth of non-malignant HaCaT cells. Furthermore, the novel series of derivatives have shown good metabolic stability, especially among the pharmacologically active compounds. To obtain a deeper insight into the molecular description of compounds activity the QSAR studies have been applied. Support vector machines (SVM) have been used to developed QSAR models for predicting the anti-proliferative activity of novel derivatives. The obtained SVM models have shown prognostic ability for HCT-116 and HeLa cell lines and as a result these models may be useful for further development of structurally similar derivatives with better biological properties.

3D quantıtatıve structure actıvıty relatıonshıp of tetrahydroımıdazo [1,2-a] pyrımıdıne as antımıcrobıal agents

A series 24 tetrahydroimidazo[1,2-a] pyrimidine derivatives were subjected to 3D quantitative structure activity relationship for antimicrobial activity. Partial least square methodology was utilized for the development of QSAR models. Six different QSAR models have been generated for antimicrobial efficiency of tetrahydroimidazo[1,2-a] pyrimidine derivatives against three human pathogens. QSAR models revealed positive influence of steric and electronic parameters on antimicrobial potential - of tetrahydroimidazo[1,2-a] pyrimidine derivatives.

Assessing therapeutic potential of molecules: molecular property diagnostic suite for tuberculosis $$(\mathbf{MPDS}^{\mathbf{TB}})$$ ( MPDS TB )

Molecular Property Diagnostic Suite ( $$\text {MPDS}^{\mathrm{TB}}$$ ) is a web tool ( http://mpds.osdd.net ) designed to assist the in silico drug discovery attempts towards Mycobacterium tuberculosis (Mtb). $$\text {MPDS}^{\mathrm{TB}}$$ tool has nine modules which are classified into data library (1–3), data processing (4–5) and data analysis (6–9). Module 1 is a repository of literature and related information available on the Mtb. Module 2 deals with the protein target analysis of the chosen disease area. Module 3 is the compound library consisting of 110.31 million unique molecules generated from public domain databases and custom designed search tools. Module 4 contains tools for chemical file format conversions and 2D to 3D coordinate conversions. Module 5 helps in calculating the molecular descriptors. Module 6 specifically handles QSAR model development tools using descriptors generated in the Module 5. Module 7 integrates the AutoDock Vina algorithm for docking, while module 8 provides screening filters. Module 9 provides the necessary visualization tools for both small and large molecules. The workflow-based open source web portal, $$\text {MPDS}^{\mathrm{TB}}$$ 1.0.1 can be a potential enabler for scientists engaged in drug discovery in general and in anti-TB research in particular. SYNOPSIS: A web-based $$\text {MPDS}^{\mathrm{TB}}$$ Galaxy tool is developed for assessing therapeutic potential of molecules. $$\text {MPDS}^{\mathrm{TB}}$$ is categorized into Data Library, Data Processing and Data Analysis. It can be a potential enabler for scientists engaged in drug discovery in general and in anti-TB research in particular.

Development of human biotransformation QSARs and application for PBT assessment refinement

Toxicokinetics heavily influence chemical toxicity as the result of Absorption, Distribution, Metabolism (Biotransformation) and Elimination (ADME) processes. Biotransformation (metabolism) reactions can lead to detoxification or, in some cases, bioactivation of parent compounds to more toxic chemicals. Moreover, biotransformation has been recognized as a key process determining chemical half-life in an organism and is thus a key determinant for bioaccumulation assessment for many chemicals. This study addresses the development of QSAR models for the prediction of in vivo whole body human biotransformation (metabolism) half-lives measured or empirically-derived for over 1000 chemicals, mainly represented by pharmaceuticals. Models presented in this study meet regulatory standards for fitting, validation and applicability domain. These QSARs were used, in combination with literature models for the prediction of biotransformation half-lives in fish, to refine the screening of the potential PBT behaviour of over 1300 Pharmaceuticals and Personal Care Products (PPCPs). The refinement of the PBT screening allowed, among others, for the identification of PPCPs, which were predicted as PBTs on the basis of their chemical structure, but may be easily biotransformed. These compounds are of lower concern in comparison to potential PBTs characterized by large predicted biotransformation half-lives.

QSAR APPROACH TO THE STUDY OF THE EGFR TYROSINE KINASE INHIBITORS: THIAZOLYL-PYRAZOLINE DERIVATIVES

A 2D-QSAR model was developed to identify key properties of thiazolyl-pyrazoline derivatives analogs involved in the inhibition of the EGFR protein tyrosine kinase. Variable selection was performed by multiple linear regression method using Build QSAR Vlife Science MDS software to develop QSAR model. The best QSAR model consists of four descriptors SddsN (nitro) count, T_2_Cl_1, SsBrE-index and T_O_F_1 descriptors, and has correlation coefficient of 0.8069 and a cross-validated squared correlation coefficient of 0.7332. All the compounds produce positive scores, which suggest that the compounds may have good kinase inhibitory profile. The developed models may be useful to predict EGFR inhibition activity for the newly synthesized thiazolyl-pyrazoline analogues.

Importance of functional groups in predicting the activity of small molecule inhibitors for Bcl-2 and Bcl-xL.

Evasion of apoptosis owing to aberrant expression of Bcl-2 (B-cell lymphoma-2) anti-apoptotic proteins is a promising hallmark of cancer. These proteins are associated with resistance to chemotherapy and radiation. Currently available QSAR models are limited to a set of inhibitors corresponding to a particular chemical scaffold, and unified models are required to identify the differential specificity of diverse compounds toward inhibiting these targets. In this study, we predicted the factors driving differential activity and specificity implementing multiplexed QSAR analysis for a dataset of 1,649 reported inhibitors of Bcl-2 (B-cell lymphoma-2) and Bcl-xL (B-cell lymphoma-extra large). We developed QSAR models for seven diverse scaffolds and critically analyzed the chemical space with coupling factors. The correlation values of QSAR models for Bcl-2 and Bcl-xL range from 0.95 to 0.985. The MAE and sMAPE of the models were in the range of 0.052-5.4nm and 0.41%-10%, respectively, signifying model robustness. The crucial descriptors and moieties accounting for the activity were benchmarked against experimentally determined binding patterns. The comprehensive analysis made in the study explores latent features of the chemical space in a broad perspective. Further, we have developed a user-friendly Web server for predicting a specific/dual inhibitor of Bcl-2 and Bcl-xL [http://www.iitm.ac.in/bioinfo/APPLE/].