Exploring different classification-dependent QSAR modelling strategies for HDAC3 inhibitors in search of meaningful structural contributors.

Histone deacetylase 3 (HDAC3), a Zn2+-dependent class I HDACs, contributes to numerous disorders such as neurodegenerative disorders, diabetes, cardiovascular disease, kidney disease and several types of cancers. Therefore, the development of novel and selective HDAC3 inhibitors might be promising to combat such diseases. Here, different classification-based molecular modelling studies such as Bayesian classification, recursive partitioning (RP), SARpy and linear discriminant analysis (LDA) were conducted on a set of HDAC3 inhibitors to pinpoint essential structural requirements contributing to HDAC3 inhibition followed by molecular docking study and molecular dynamics (MD) simulation analyses. The current study revealed the importance of hydroxamate function for Zn2+ chelation as well as hydrogen bonding interaction with Tyr298 residue. The importance of hydroxamate function for higher HDAC3 inhibition was noticed in the case of Bayesian classification, recursive partitioning and SARpy models. Also, the importance of substituted thiazole ring was revealed, whereas the presence of linear alkyl groups with carboxylic acid function, any type of ester function, benzodiazepine moiety and methoxy group in the molecular structure can be detrimental to HDAC3 inhibition. Therefore, this study can aid in the design and discovery of effective novel HDAC3 inhibitors in the future.

Exploring crucial structural attributes of quinolinyl methoxyphenyl sulphonyl-based hydroxamate derivatives as ADAM17 inhibitors through classification-dependent molecular modelling approaches.

A Disintegrin and Metalloproteinase 17 (ADAM17), a Zn2+-dependent metalloenzyme of the adamalysin family of the metzincin superfamily, is associated with various pathophysiological conditions including rheumatoid arthritis and cancer. However, no specific inhibitors have been marketed yet for ADAM17-related disorders. In this study, 94 quinolinyl methoxyphenyl sulphonyl-based hydroxamates as ADAM17 inhibitors were subjected to classification-based molecular modelling and binding pattern analysis to identify the significant structural attributes contributing to ADAM17 inhibition. The statistically validated classification-based models identified the importance of the P1' substituents such as the quinolinyl methoxyphenyl sulphonyl group of these compounds for occupying the S1' - S3' pocket of the enzyme. The quinolinyl function of these compounds was found to explore stable binding of the P1' substituents at the S1' - S3' pocket whereas the importance of the sulphonyl and the orientation of the P1' moiety also revealed stable binding. Based on the outcomes of the current study, four novel compounds of different classes were designed as promising ADAM17 inhibitors. These findings regarding the crucial structural aspects and binding patterns of ADAM17 inhibitors will aid the design and discovery of novel and effective ADAM17 inhibitors for therapeutic advancements of related diseases.

Assessing structural insights into in-house arylsulfonyl L-(+) glutamine MMP-2 inhibitors as promising anticancer agents through structure-based computational modelling approaches.

MMP-2 is potentially contributing to several cancer progressions including leukaemias. Therefore, considering MMP-2 as a promising target, novel anticancer compounds may be designed. Here, 32 in-house arylsulfonyl L-(+) glutamines were subjected to various structure-based computational modelling approaches to recognize crucial structural attributes along with the spatial orientation for higher MMP-2 inhibition. Again, the docking-based 2D-QSAR study revealed that the Coulomb energy conferred by Tyr142 and total interaction energy conferred by Ala84 was crucial for MMP-2 inhibition. Importantly, the docking-dependent CoMFA and CoMSIA study revealed the importance of favourable steric, electrostatic, and hydrophobic substituents at the terminal phenyl ring. The MD simulation study revealed a lower fluctuation in the RMSD, RMSF, and Rg values indicating stable binding interactions of MMP-2 and these molecules. Moreover, the residual hydrogen bond and their interaction analysis disclosed crucial amino acid residues responsible for forming potential hydrogen bonding for higher MMP-2 inhibition. The results can effectively aid in the design and discovery of promising small-molecule drug-like MMP-2 inhibitors with greater anticancer potential in the future.

Exploration of structural alerts and fingerprints for novel anticancer therapeutics: a robust classification-QSAR dependent structural analysis of drug-like MMP-9 inhibitors.

Among various matrix metalloproteinases (MMPs), overexpression of MMP9 has been established as a key player in a variety of cancers. Therefore, MMP9 has emerged as a promising biomolecule that may be targeted to design potent inhibitors as novel anticancer therapeutics. In this study, a large database containing 1,123 drug-like MMP-9 inhibitors was considered for robust classification-dependent fragment-based QSAR study through SARpy, Bayesian classification, and recursive partitioning analyses and were validated by both internal and external validation techniques. In a nutshell, all these classification-dependent techniques revealed some common structural alerts and sub-structural fingerprints responsible for modulating MMP-9 inhibition. These observations are in agreement with the interactions obtained from the ligand-bound co-crystal structures of MMP-9 justifying the robustness of the current study. Finally, based on these crucial structural fragments, some new lead compounds were designed and further validated by the binding mode of interaction analysis. Therefore, these findings may be beneficial in designing novel and potential MMP-9 inhibitors in the future as a weapon to combat several cancers.

A comparative quantitative structural assessment of benzothiazine-derived HDAC8 inhibitors by predictive ligand-based drug designing approaches.

Histone deacetylase 8 (HDAC8) is a verified biomolecular target associated with diverse diseases including cancer. Though several HDAC inhibitors emerged effective against such diseases, no selective HDAC8 inhibitor is approved to date. Therefore, the development of potent HDAC8-selective inhibitors is inevitable to combat such diseases. Here, some benzothiazine-derived HDAC8 inhibitors were considered for a comparative QSAR analysis which may elucidate the prime structural components responsible for modulating their efficacy. Several outcomes from these diverse modelling techniques justified one another and thus validated each other. The ligand-based pharmacophore modelling study identified ring aromatic, positive ionizable, and hydrophobic features as essential structural attributes for HDAC8 inhibition. Besides, MLR, HQSAR and field-based 3D-QSAR studies signified the utility of the positive ionizable and hydrophobic features for potent HDAC8 inhibition. Again, the field-based 3D-QSAR study provided useful insight regarding the substitution in the fused phenyl ring. Moreover, the current observations also validated the previously reported molecular docking observations. Based on the outcomes, some new molecules were designed and predicted. Therefore, this comparative structural analysis of these HDAC8 inhibitors will surely assist in the development of potent HDAC8 inhibitors as promising anticancer therapeutics in the future.

A quantitative structural analysis of AR-42 derivatives as HDAC1 inhibitors for the identification of promising structural contributors.

Alteration and abnormal epigenetic mechanisms can lead to the aberration of normal biological functions and the occurrence of several diseases. The histone deacetylase (HDAC) family of enzymes is one of the prime regulators of epigenetic functions modifying the histone proteins, and thus, regulating epigenetics directly. HDAC1 is one of those HDACs which have important contributions to cellular epigenetics. The abnormality of HDAC is correlated to the occurrence, progression, and poor prognosis in several disease conditions namely neurodegenerative disorders, cancer cell proliferation, metastasis, chemotherapy resistance, and survival in various cancers. Therefore, the progress of potent and effective HDAC1 inhibitors is one of the prime approaches to combat such diseases. In this study, both regression and classification-based molecular modelling studies were conducted on some AR-42 derivatives as HDAC1 inhibitors to elucidate the crucial structural aspects that are responsible for regulating their biological responses. This study revealed that the molecular polarizability, van der Waals volume, the presence of aromatic rings as well as the higher number of hydrogen bond acceptors might affect prominently their inhibitory activity and might be responsible for proper fitting and interactions at the HDAC1 active site to pertain effective inhibition.

Insight into the structural requirements of gelatinases (MMP-2 and MMP-9) inhibitors by multiple validated molecular modelling approaches: Part II.

Inhibition of the matrix metalloproteinases (MMPs) is effective against metastasis of secondary tumours. Previous MMP inhibitors have failed in clinical trials due to their off-target toxicity in solid tumours. Thus, newer MMP inhibitors now have paramount importance. Here, different molecular modelling techniques were applied on a dataset of 110 gelatinase (MMP-2 and MMP-9) inhibitors. The objectives of the present study were to identify structural fingerprints for gelatinase inhibition and also to develop statistically validated QSAR models for the screening and prediction of different derivatives as MMP-2 (gelatinase A) and MMP-9 (gelatinase B) inhibitors. The Bayesian classification study provided the ROC values for the training set of 0.837 and 0.815 for MMP-2 and MMP-9, respectively. The linear model also produced the leave-one-out cross-validated Q2 of 0.805 (eq. 1, MMP-2) and 0.724 (eq. 2, MMP-9), an r2 of 0.845 (eq. 1, MMP-2) and 0.782 (eq. 2, MMP-9), an r2Pred of 0.806 (eq. 1, MMP-2) and 0.732 (eq. 2, MMP-9). Similarly, non-linear learning models were also statistically significant and reliable. Overall, this study may help in the rational design of newer compounds with higher gelatinase inhibition to fight against both primary and secondary cancers in future.

Applying comparative molecular modelling techniques on diverse hydroxamate-based HDAC2 inhibitors: an attempt to identify promising structural features for potent HDAC2 inhibition.

Histone deacetylase 2 (HDAC2) has been implicated in a variety of cardiovascular and neurodegenerative disorders as well as in cancers. Thus, HDAC2 has become an exclusive target for anticancer drug development. Therefore, the development of newer HDAC2 inhibitors in disease conditions is a prime goal to restrain such a scenario. Although a handful of HDAC inhibitors was accepted for the treatment of HDAC-related disease conditions, the non-selective nature of these entities is one of the major setbacks in the treatment of specific HDAC isoform-related pathophysiology. In this framework, the analyses of pre-existing molecules are essential to identify the important structural features that can fulfil the requirements for the cap and linker moieties to obtain potent and effective HDAC2 inhibition. Thus, in this study, the implementation of a combined comparative 2D and 3D molecular modelling techniques was done on a group of 92 diverse hydroxamate derivatives having a wide range of HDAC2 inhibitory potency. Besides other crucial features, this study upheld the importance of groups like triazole and benzyl moieties along with the molecular fields that are crucial for regulating HDAC2 inhibition. The outcomes of this study may be employed for the designing of HDAC2 inhibitors in future.

First molecular modelling report on tri-substituted pyrazolines as phosphodiesterase 5 (PDE5) inhibitors through classical and machine learning based multi-QSAR analysis.

Phosphodiesterase 5 (PDE5) falls under a broad category of metallohydrolase enzymes responsible for the catalysis of the phosphodiesterase bond, and thus it can terminate the action of cyclic guanosine monophosphate (cGMP). Overexpression of this enzyme leads to development of a number of pathological conditions. Thus, targeting the enzyme to develop inhibitors could be useful for the treatment of erectile dysfunction as well as pulmonary hypertension. In the current study, several molecular modelling techniques were utilized including Bayesian classification, single tree and forest tree recursive partitioning, and genetic function approximation to identify crucial structural fingerprints important for optimization of tri-substituted pyrazoline derivatives as PDE5 inhibitors. Later, various machine learning models were also developed that could be utilized to predict and screen PDE5 inhibitors in the future.

A robust classification-dependent multi-molecular modelling study on some biphenyl sulphonamide based MMP-8 inhibitors.

Matrix metalloproteinases (MMPs) are a group of zinc and calcium-dependent endopeptidases, which contribute to different physiological and biological activities via extracellular matrix (ECM) degradation. Matrix metalloproteinase-8 (MMP-8) belongs to type-II collagenases of the MMP family that has contribution in several physiological disorders such as cardiovascular diseases, joint, renal, digestive and respiratory disorders as well as in cancer. In clinical study, MMP-8 is found to be associated with periodontal disease condition. Therefore, MMP-8 specific inhibitors should be developed to target these disorders. The biphenyl sulphonamide (BPS) moiety is one of the crucial structural characteristics found in several MMP-8 inhibitors. Here, different classification-based molecular modelling methods were used to explore the structural features that lead to the activity variation of a series of MMP-8 inhibitors possessing a BPS moiety. Our current classification-based structural analysis of these BPS-derived MMP-8 inhibitors was able to identify the importance of several structural features such as the tetrahydroisoquinoline and N-Boc pyridyl groups, which have positive influences on MMP-8 inhibition. This study was also reflected the importance of the zinc-binding groups (ZBGs) like the hydroxamate and phosphonate for potent and sub-nanomolar range MMP-8 inhibition, which may benefit the development of highly potent MMP-8 inhibitors.

Insight into the structural requirement of aryl sulphonamide based gelatinases (MMP-2 and MMP-9) inhibitors - Part I: 2D-QSAR, 3D-QSAR topomer CoMFA and Naïve Bayes studies - First report of 3D-QSAR Topomer CoMFA analysis for MMP-9 inhibitors and jointly inhibitors of gelatinases together.

Gelatinases [gelatinase A - matrix metalloproteinase-2 (MMP-2), gelatinase B - matrix metalloproteinase-9 (MMP-9)] play key roles in many disease conditions including cancer. Despite some research work on gelatinases inhibitors both jointly and individually had been reported, challenges still exist in achieving potency as well as selectivity. Here in part I of a series of work, we have reported the structural requirement of some arylsulfonamides. In particular, regression-based 2D-QSARs, topomer CoMFA (comparative molecular field analysis) and Bayesian classification models were constructed to refine structural features for attaining better gelatinase inhibitory activity. The 2D-QSAR models exhibited good statistical significance. The descriptors nsssN, SHBint6, SHBint7, PubchemFP629 were directly correlated with the MMP-2 binding affinities whereas nsssN, SHBint10 and AATS2i were directly proportional to MMP-9 binding affinities. The topomer CoMFA results indicated that the steric and electrostatic fields play key roles in gelatinase inhibition. The established Naïve Bayes prediction models were evaluated by fivefold cross validation and an external test set. Furthermore, important molecular descriptors related to MMP-2 and MMP-9 binding affinities and some active/inactive fragments were identified. Thus, these observations may be helpful for further work of aryl sulphonamide based gelatinase inhibitors in future.

Robust classification-based molecular modelling of diverse chemical entities as potential SARS-CoV-2 3CLpro inhibitors: theoretical justification in light of experimental evidences.

COVID-19 is the most unanticipated incidence of 2020 affecting the human population worldwide. Currently, it is utmost important to produce novel small molecule anti-SARS-CoV-2 drugs urgently that can save human lives globally. Based on the earlier SARS-CoV and MERS-CoV infection along with the general characters of coronaviral replication, a number of drug molecules have been proposed. However, one of the major limitations is the lack of experimental observations with different drug molecules. In this article, 70 diverse chemicals having experimental SARS-CoV-2 3CLproinhibitory activity were accounted for robust classification-based QSAR analysis statistically validated with 4 different methodologies to recognize the crucial structural features responsible for imparting the activity. Results obtained from all these methodologies supported and validated each other. Important observations obtained from these analyses were also justified with the ligand-bound crystal structure of SARS-CoV-2 3CLpro enzyme. Our results suggest that molecules should contain a 2-oxopyrrolidine scaffold as well as a methylene (hydroxy) sulphonic acid warhead in proper orientation to achieve higher inhibitory potency against SARS-CoV-2 3CLpro. Outcomes of our study may be able to design and discover highly effective SARS-CoV-2 3CLpro inhibitors as potential anticoronaviral therapy to crusade against COVID-19.

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.

Exploring the structural aspects of ureido-amino acid-based APN inhibitors: a validated comparative multi-QSAR modelling study.

The zinc-dependent enzyme aminopeptidase N (APN) is a member of the M1 metalloenzyme family. The multi-functionality of APN as a peptidase, a receptor and a signalling molecule has provided it the access to influence a number of disease conditions namely viral diseases, angiogenesis, cellular metastasis and invasion including different cancer conditions. Hence, the development of potent APN inhibitors is a possible route for the treatment of diseases related to the activity of APN. In this study, different QSAR approaches have been adopted to identify the structural features of a group of hydroxamate-based ureido-amino acid derivative APN inhibitors. This study was able to identify different constitutional aspects of these APN inhibitors which are important for their inhibitory potency. Additionally, some of these observations were also aligned with the observations of previously performed QSAR studies conducted on different APN inhibitors. Therefore, the results of this study may help to design potent and effective APN inhibitors in the future.

Discriminations of active from inactive HDAC8 inhibitors Part II: Bayesian classification study to find molecular fingerprints.

In continuation of our earlier work (Doi: 10.1080/07391102.2019.1661876), a statistically validated and robust Bayesian model was developed on a large diverse set of HDAC8 inhibitors. The training set comprised of 676 small molecules and 293 compounds were considered as test set molecules. The findings of this analysis will help to explore some major directions regarding the HDAC8 inhibitor designing approach. Acrylamide (G1-G3, G9), N-substituted 2-phenylimidazole (G4-G8, G9, G12-G13, G16-G19), benzimidazole (G10-G11), piperidine substituted pyrrole (G13-G14) groups, alkyl/aryl amide (G15) and aryloxy carboxamide (G20) fingerprints were found to play a crucial role in HDAC8 inhibitory activity whereas -CH-N=CH- (B1, B4-B6, B14) motif, benzamide (B2-B3, B9-B13, B16-B17) groups and heptazepine (B7-B8, B15, B18-B20) group were found to influence negatively the HDAC8 inhibitory activity. The importance of such fingerprints was further validated by the HDAC8 enzyme and related inhibitor interactions at the receptor level. These results are in close agreement with those of our previous work that validate each other. Moreover, this comparative learning may enrich future endeavours regarding the designing strategy of HDAC8 inhibitors.

Insight into structural features of phenyltetrazole derivatives as ABCG2 inhibitors for the treatment of multidrug resistance in cancer.

ABCG2 is the principal ABC transporter involved in the multidrug resistance of breast cancer. Looking at the current demand in the development of ABCG2 inhibitors for the treatment of multidrug-resistant cancer, we have explored structural requirements of phenyltetrazole derivatives for ABCG2 inhibition by combining classical QSAR, Bayesian classification modelling and molecular docking studies. For classical QSAR, structural descriptors were calculated from the free software tool PaDEL-descriptor. Stepwise multiple linear regression (SMLR) was used for model generation. A statistically significant model was generated and validated with different parameters (For training set: r = 0.825; Q2 = 0.570 and for test set: r = 0.894, r2pred = 0.783). The predicted model was found to satisfy the Golbraikh and Trospha criteria for model acceptability. Bayesian classification modelling was also performed (ROC scores were 0.722 and 0.767 for the training and test sets, respectively). Finally, the binding interactions of phenyltetrazole type inhibitor with the ABCG2 receptor were mapped with the help of molecular docking study. The result of the docking analysis is aligned with the classical QSAR and Bayesian classification studies. The combined modelling study will guide the medicinal chemists to act faster in the drug discovery of ABCG2 inhibitors for the management of resistant breast cancer.

QSAR modelling on a series of arylsulfonamide-based hydroxamates as potent MMP-2 inhibitors.

Matrix metalloproteinase-2 (MMP-2) is a lucrative therapeutic target as far as anticancer drug discovery is concerned. Overexpression of MMP-2 is found to facilitate tumour propagation through the involvement of vascular endothelial growth factor (VEGF). However, even after different techniques, finding a target-specific MMP-2 inhibitor with respectable pharmacodynamic properties is still a challenging task. Regression-dependent quantitative structure-activity relationship (QSAR) strategies might be among the possible drug design methods to explore the essential structural features that would be valuable to find a suitable MMP-2 inhibitor. In this paper, 72 molecules were explored using the PaDEL descriptors and stepwise multiple linear regression (S-MLR). The partial least squares (PLS) method was also used to create a viable statistical model with an acceptable metric related to these models. The final statistical models were formed with statistical parameters within acceptable range (r2 = 0.797, Q2 = 0.725 and r2pred = 0.643 for the MLR model, and r2 = 0.780, Q2 = 0.685 and r2pred = 0.666 for the PLS model). The models were analysed and compared with those already published on the same endpoint.

Structural exploration of hydroxyethylamines as HIV-1 protease inhibitors: new features identified.

The current study deals with chemometric modelling strategies (Naïve Bayes classification, hologram-based quantitative structure-activity relationship (HQSAR), comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA)) to explore the important features of hydroxylamine derivatives for exerting potent human immunodeficiency virus-1 (HIV-1) protease inhibition. Depending on the statistically validated reliable and robust quantitative structure-activity relationship (QSAR) models, important and crucial structural features have been identified that may be responsible for enhancing the activity profile of these hydroxylamine compounds. Arylsulfonamide function along with methoxy or fluoro substitution is important for enhancing activity. Bulky steric substitution at the sulfonamide nitrogen disfavours activity whereas smaller hydrophobic substitution at the same position is found to be favourable. Apart from the crucial oxazolidinone moiety, pyrrolidine, cyclic urea and methyl ester functions are also responsible for increasing the HIV-1 protease inhibitory profile. Observations derived from these modelling studies may be utilized further in designing promising HIV-1 protease inhibitors of this class.

Multiple molecular modelling studies on some derivatives and analogues of glutamic acid as matrix metalloproteinase-2 inhibitors.

Matrix metalloproteinase-2 (MMP-2) is a potential target in anticancer drug discovery due to its association with angiogenesis, metastasis and tumour progression. In this study, 67 glutamic acid derivatives, synthesized and evaluated as MMP-2 inhibitors, were taken into account for multi-QSAR modelling study (regression-based 2D-QSAR, classification-based LDA-QSAR, Bayesian classification QSAR, HQSAR, 3D-QSAR CoMFA and CoMSIA as well as Open3DQSAR). All these QSAR studies were statistically validated individually. Regarding the 3D-QSAR analysis, the Open3DQSAR results were better than CoMFA and CoMSIA, although all these 3D-QSAR models supported each other. The importance of biphenylsulphonyl moiety over phenylacetyl/naphthylacetyl moieties was established due to its association with favourable steric and hydrophobic characters. HQSAR, LDA-QSAR and Bayesian classification QSAR studies also suggested that the biphenylsulphonamido group was better than the phenylacetylcarboxamido function. Additionally, glutamines were proven to be far better inhibitors than isoglutamines. Observations obtained from the current study were revalidated and supported by the earlier reported molecular modelling studies. Depending on these observations, newer glutamic acid-based compounds may be designed further in future for potent MMP-2 inhibitory activity.

Hydroxyethylamine derivatives as HIV-1 protease inhibitors: a predictive QSAR modelling study based on Monte Carlo optimization.

Application of HIV-1 protease inhibitors (as an anti-HIV regimen) may serve as an attractive strategy for anti-HIV drug development. Several investigations suggest that there is a crucial need to develop a novel protease inhibitor with higher potency and reduced toxicity. Monte Carlo optimized QSAR study was performed on 200 hydroxyethylamine derivatives with antiprotease activity. Twenty-one QSAR models with good statistical qualities were developed from three different splits with various combinations of SMILES and GRAPH based descriptors. The best models from different splits were selected on the basis of statistically validated characteristics of the test set and have the following statistical parameters: r2 = 0.806, Q2 = 0.788 (split 1); r2 = 0.842, Q2 = 0.826 (split 2); r2 = 0.774, Q2 = 0.755 (split 3). The structural attributes obtained from the best models were analysed to understand the structural requirements of the selected series for HIV-1 protease inhibitory activity. On the basis of obtained structural attributes, 11 new compounds were designed, out of which five compounds were found to have better activity than the best active compound in the series.