Monte Carlo based modelling approach for designing and predicting cytotoxicity of 2-phenylindole derivatives against breast cancer cell line MCF7

How the structural properties of the indole derivatives are important in kinase targeted drug design?: A case study on tyrosine kinase inhibitors

Kinases are considered as important signalling enzymes that illustrate 20% of the druggable genome. Human kinase family comprises >500 protein kinases and about 20 lipid kinases. Protein kinases are responsible for the mechanism of protein phosphorylation. These are necessary for regulation of various cellular activities including proliferation, cell cycle, apoptosis, motility, growth, differentiation, etc. Their deregulation leads to disruption of many cellular processes leading to different diseases most importantly cancer. Thus, kinases are considered as valuable targets in different types of cancer as well as other diseases. Researchers around the world are actively engaged in developing inhibitors based on distinct chemical scaffolds. Indole represents as a versatile scaffold in the naturally occurring and bioactive molecules. It is also used as a privileged scaffold for the target-based drug design against different diseases. This present article aim to review the applications of indole scaffold in the design of inhibitors against different tyrosine kinases such as epidermal growth factor receptors (EGFRs), vascular endothelial growth factor receptors (VEGFRs), platelet-derived growth factor receptors (PDGFRs), etc. Important structure activity relationships (SARs) of indole derivatives were discussed. The present work is an attempt to summarize all the crucial structural information which is essential for the development of indole based tyrosine kinase inhibitors with improved potency.

Unmasking of crucial structural fragments for coronavirus protease inhibitors and its implications in COVID-19 drug discovery

Fragment based drug discovery (FBDD) by the aid of different modelling techniques have been emerged as a key drug discovery tool in the area of pharmaceutical science and technology. The merits of employing these methods, in place of other conventional molecular modelling techniques, endorsed clear detection of the possible structural fragments present in diverse set of investigated compounds and can create alternate possibilities of lead optimization in drug discovery. In this work, two fragment identification tools namely SARpy and Laplacian-corrected Bayesian analysis were used for previous SARS-CoV PLpro and 3CLpro inhibitors. A robust and predictive SARpy based fragments identification was performed which have been validated further by Laplacian-corrected Bayesian model. These comprehensive approaches have advantages since fragments are straight forward to interpret. Moreover, distinguishing the key molecular features (with respect to ECFP_6 fingerprint) revealed good or bad influences for the SARS-CoV protease inhibitory activities. Furthermore, the identified fragments could be implemented in the medicinal chemistry endeavors of COVID-19 drug discovery.

The Monte Carlo Method as a Tool to Build up Predictive QSPR/QSAR

Background:

The Monte Carlo method has a wide application in various scientific researches. For the development of predictive models in a form of the quantitative structure-property / activity relationships (QSPRs/QSARs), the Monte Carlo approach also can be useful. The CORAL software provides the Monte Carlo calculations aimed to build up QSPR/QSAR models for different endpoints.

Methods:

Molecular descriptors are a mathematical function of so-called correlation weights of various molecular features. The numerical values of the correlation weights give the maximal value of a target function. The target function leads to a correlation between endpoint and optimal descriptor for the visible training set. The predictive potential of the model is estimated with the validation set, i.e. compounds that are not involved in the process of building up the model.

Results:

The approach gave quite good models for a large number of various physicochemical, biochemical, ecological, and medicinal endpoints. Bibliography and basic statistical characteristics of several CORAL models are collected in the present review. In addition, the extended version of the approach for more complex systems (nanomaterials and peptides), where behaviour of systems is defined by a group of conditions besides the molecular structure is demonstrated.

Conclusion:

The Monte Carlo technique available via the CORAL software can be a useful and convenient tool for the QSPR/QSAR analysis.

Identification of structural fingerprints for ABCG2 inhibition by using Monte Carlo optimization, Bayesian classification, and structural and physicochemical interpretation (SPCI) analysis

The human breast cancer resistance protein (BCRP), one of the members of the large ATP binding cassette (ABC) transporter superfamily, is crucial for resistance against chemotherapeutic agents. Currently, it has been emerged as one of the best biological targets for the designing of small molecule drugs capable of eliminating multidrug resistance in breast cancer. In order to gain insights into the relationship between the molecular structure of compounds and the ABCG2 inhibition, a multi-QSAR approach using different methods was performed on a dataset of 294 ABCG2 inhibitors with diverse scaffolds. The best models obtained by different chemometric methods have the following statistical characteristics: Monte Carlo Optimization-based QSAR (sensitivity = 0.905, specificity = 0.6255, accuracy = 0.756, and MCC = 0.545), Bayesian classification model (sensitivity = 0.735, specificity = 0.775, and concordance = 0.757); structural and physicochemical interpretation analysis-random forest method (balance accuracy = 0.750, sensitivity = 0.810, and specificity = 0.700). Additionally, structural fingerprints modulating the ABCG2 inhibitory properties were identified from the best models of each method and also validated with each other. The current modelling study is an attempt to get a deep insight into the different important structural fingerprints modulating ABCG2 inhibition.

Development of novel therapeutics for the treatment of glaucoma based on actin-binding kinase inhibition – in silico approach

QSAR modeling with computer-aided drug design were used for the in silico development of novel therapeutics for glaucoma treatment.

Good and bad molecular fingerprints for human rhinovirus 3C protease inhibition: identification, validation, and application in designing of new inhibitors through Monte Carlo-based QSAR study

HRV 3 C protease (HRV 3Cpro) is an important target for common cold and upper respiratory tract infection. Keeping in view of the non-availability of drug for the treatment, newer computer-based modelling strategies should be applied to rationalize the process of antiviral drug discovery in order to decrease the valuable time and huge expenditure of the process. The present work demonstrates a structure wise optimization using Monte Carlo-based QSAR method that decomposes ligand compounds (in SMILES format) into several molecular fingerprints/descriptors. The current state-of-the-art in QSAR study involves the balance of correlation approach using four different sets: training, invisible training, calibration, and validation. The final models were also validated through mean absolute error, index of ideality of correlation, Y-randomization and applicability domain analysis. R2 and Q2 values for the best model were 0.8602, 0.8507 (training); 0.8435, 0.8331 (invisible training); 0.7424, 0.7020 (calibration); 0.5993, 0.5216 (validation), respectively. The process identified some molecular substructures as good and bad fingerprints depending on their effect to increase or decrease the HRV 3Cpro inhibition. Finally, new inhibitors were designed based on the fundamental concept to replace the bad fragments with the good fragments as well as including more good fragments into the structure. The study points out the importance of the fingerprint based drug design strategy through Monte Carlo optimization method in the modelling of HRV 3Cpro inhibitors.

In silico modelling, identification of crucial molecular fingerprints, and prediction of new possible substrates of human organic cationic transporters 1 and 2

The cation membrane transporters are crucial to regulate movement of foreign molecules within the body. The present study found out structural fingerprints within molecules to be recognized as substrate/non-substrate against these transporters.

Design and development of novel therapeutics for coronary heart disease treatment based on cholesteryl ester transfer protein inhibition - in silico approach

Cholesteryl ester transfer protein (CETP) belongs to the group of enzymes which inhibition have the application in the treatment of cardiovascular diseases. This study presents QSAR modeling for a set of compounds acting as CETP inhibitors based on the Monte Carlo optimization with SMILES notation and molecular graph-based descriptors, and field-based 3D modeling. A 3D QSAR model was developed for one random split into the training and test sets, whereas conformation independent QSAR models were developed for three random splits, with the results suggesting there is an excellent correlation between them. Various statistical approaches were used to assess the statistical quality of the developed models, including robustness and predictability, and the obtained results were very good. This study used a novel statistical metric known as the index of ideality of correlation for the final assessment of the model, and the results that were obtained suggested that the model was good. Also, molecular fragments which account for the increases and/or decreases of a studied activity were defined and then used for the computer-aided design of new compounds as potential CETP inhibitors. The final assessment of the developed QSAR model and designed inhibitors was done using molecular docking, which revealed an excellent correlation with the results from QSAR modeling.Communicated by Ramaswamy H. Sarma.

Design and development of novel therapeutics for brucellosis treatment based on carbonic anhydrase inhibition

Carbonic anhydrase is a metalloprotein, an enzyme with strong inhibition in antibacterial treatment. This study presents QSAR modeling for a series of 41 chemical compounds, 40 sulfonamides and one sulfamate, including 13 clinically tested drugs as carbonic anhydrase inhibitors based on the Monte Carlo optimization with molecular descriptors based on the SMILES notation and local invariants of the molecular graph, and field 3D based methods. Conformation independent QSAR models were developed for three random splits and a 3D QSAR model for one random split into the training and test sets. The statistical quality of the developed models, including robustness and predictability, was tested using various statistical approaches and the results that were obtained were very good. An excellent correlation between the results from the conformation independent and the 3D QSAR model was obtained. A novel statistical metric known as the index of ideality of correlation was used for the final assessment of the model, and the obtained results were good. Molecular fragments responsible for the increases and decreases of a studied activity were defined and further used for the computer-aided design of new compounds as potential carbonic anhydrase inhibitors. Molecular docking was applied for the final assessment of the developed QSAR model and designed inhibitors, and an excellent correlation between the results from QSAR modeling and molecular docking studies was obtained.Communicated by Ramaswamy H. Sarma.

Exploration of good and bad structural fingerprints for inhibition of indoleamine-2,3-dioxygenase enzyme in cancer immunotherapy using Monte Carlo optimization and Bayesian classification QSAR modeling

Indoleamine-2,3-dioxygenase 1 (IDO1) is an extrahepatic, heme-containing and tryptophan-catalyzing enzyme responsible for causing blockade of T-cell proliferation and differentiation by depleting tryptophan level in cancerous cells. Therefore, inhibition of IDO1 may be a useful strategy for immunotherapy against cancer. In this study, 448 structurally diverse IDO1 inhibitors with a wide range of activity has been taken into consideration for classification QSAR analysis through Monte Carlo Optimization by using different splits as well as different combinations of SMILES-based, graph-based and hybrid descriptors. The best model from Monte Carlo optimization was interpreted to find out the good and bad structural fingerprints for IDO1 and further justified by using Bayesian classification QSAR modeling. Among the three splits in Monte Carlo optimization, the statistics of the best model was obtained from Split 3: sensitivity = 0.87, specificity = 0.91, accuracy = 0.89 and MCC = 0.78. In Bayesian classification modeling, the ROC scores for training and test set were found to be 0.91 and 0.86, respectively. The combined modeling analysis revealed that the presence of aryl hydrazyl sulphonyl moiety, furazan ring, halogen substitution, nitro group and hetero atoms in aromatic system can be very useful in designing IDO1 inhibitors. All the good and bad structural fingerprints for IDO1 were identified and are justified by correlating these fragments to the inhibition of IDO1 enzyme. These structural fingerprints will guide the researchers in this field to design better inhibitors against IDO1 enzyme for cancer immunotherapy.Communicated by Ramaswamy H. Sarma.

Development of QSAR machine learning-based models to forecast the effect of substances on malignant melanoma cells

SK-MEL-5 is a human melanoma cell line that has been used in various studies to explore new therapies against melanoma in different in vitro experiments. Based on this study we report on the development of quantitative structure-activity relationship (QSAR) models able to predict the cytotoxic effect of diverse chemical compounds on this cancer cell line. The dataset of cytotoxic and inactive compounds were downloaded from the PubChem database. It contains the data for all chemical compounds for which cytotoxicity results expressed by GI₅₀ was recorded. In total 13 blocks of molecular descriptors were computed and used, after appropriate pre-processing in building QSAR models with four machine learning classifiers: Random forest (RF), gradient boosting, support vector machine and random k-nearest neighbors. Among the 186 models reported none had a positive predictive value (PPV) higher than 0.90 in both nested cross-validation and on an external dataset testing, but 7 models had a PPV higher than 0.85 in both evaluations, all seven using the RFs algorithm as a classifier, and topological descriptors, information indices, 2D-autocorrelation descriptors, P-VSA-like descriptors, and edge-adjacency descriptors as sets of features used for classification. The y-scrambling test was associated with considerably worse performance (confirming the non-random character of the models) and the applicability domain was assessed through three different methods.