scholarly journals Multi-Target Chemometric Modelling, Fragment Analysis and Virtual Screening with ERK Inhibitors as Potential Anticancer Agents

Molecules ◽  
2019 ◽  
Vol 24 (21) ◽  
pp. 3909 ◽  
Author(s):  
Amit Kumar Halder ◽  
Amal Kanta Giri ◽  
Maria Natália Dias Soeiro Cordeiro
Keyword(s):  
Molecular Docking ◽  
Virtual Screening ◽  
Linear Model ◽  
Anticancer Agents ◽  
Quantitative Structure Activity Relationship ◽  
Experimental Conditions ◽  
Fragment Analysis ◽  
Linear Discriminant ◽  
Qsar Models ◽  
Dynamics Simulations

Two isoforms of extracellular regulated kinase (ERK), namely ERK-1 and ERK-2, are associated with several cellular processes, the aberration of which leads to cancer. The ERK-1/2 inhibitors are thus considered as potential agents for cancer therapy. Multitarget quantitative structure–activity relationship (mt-QSAR) models based on the Box–Jenkins approach were developed with a dataset containing 6400 ERK inhibitors assayed under different experimental conditions. The first mt-QSAR linear model was built with linear discriminant analysis (LDA) and provided information regarding the structural requirements for better activity. This linear model was also utilised for a fragment analysis to estimate the contributions of ring fragments towards ERK inhibition. Then, the random forest (RF) technique was employed to produce highly predictive non-linear mt-QSAR models, which were used for screening the Asinex kinase library and identify the most potential virtual hits. The fragment analysis results justified the selection of the hits retrieved through such virtual screening. The latter were subsequently subjected to molecular docking and molecular dynamics simulations to understand their possible interactions with ERK enzymes. The present work, which utilises in-silico techniques such as multitarget chemometric modelling, fragment analysis, virtual screening, molecular docking and dynamics, may provide important guidelines to facilitate the discovery of novel ERK inhibitors.

scholarly journals AKT Inhibitors: The Road Ahead to Computational Modeling-Guided Discovery

2021 ◽  
Vol 22 (8) ◽  
pp. 3944
Author(s):  
Amit Kumar Halder ◽  
M. Natália D. S. Cordeiro
Keyword(s):  
Linear Model ◽  
Inhibitory Activity ◽  
Kinase Inhibitor ◽  
External Validation ◽  
Systematic Evaluation ◽  
Gradient Boosting ◽  
Experimental Conditions ◽  
Linear Discriminant ◽  
Non Linear ◽  
Qsar Models

AKT, is a serine/threonine protein kinase comprising three isoforms—namely: AKT1, AKT2 and AKT3, whose inhibitors have been recognized as promising therapeutic targets for various human disorders, especially cancer. In this work, we report a systematic evaluation of multi-target Quantitative Structure-Activity Relationship (mt-QSAR) models to probe AKT’ inhibitory activity, based on different feature selection algorithms and machine learning tools. The best predictive linear and non-linear mt-QSAR models were found by the genetic algorithm-based linear discriminant analysis (GA-LDA) and gradient boosting (Xgboost) techniques, respectively, using a dataset containing 5523 inhibitors of the AKT isoforms assayed under various experimental conditions. The linear model highlighted the key structural attributes responsible for higher inhibitory activity whereas the non-linear model displayed an overall accuracy higher than 90%. Both these predictive models, generated through internal and external validation methods, were then used for screening the Asinex kinase inhibitor library to identify the most potential virtual hits as pan-AKT inhibitors. The virtual hits identified were then filtered by stepwise analyses based on reverse pharmacophore-mapping based prediction. Finally, results of molecular dynamics simulations were used to estimate the theoretical binding affinity of the selected virtual hits towards the three isoforms of enzyme AKT. Our computational findings thus provide important guidelines to facilitate the discovery of novel AKT inhibitors.

scholarly journals Identification of Anti-SARS-CoV-2 Compounds from Food Using QSAR-Based Virtual Screening, Molecular Docking, and Molecular Dynamics Simulation Analysis

2021 ◽  
Vol 14 (4) ◽  
pp. 357
Author(s):  
Magdi E. A. Zaki ◽  
Sami A. Al-Hussain ◽  
Vijay H. Masand ◽  
Siddhartha Akasapu ◽  
Sumit O. Bajaj ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Docking ◽  
Virtual Screening ◽  
Simulation Analysis ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Dynamics Simulation ◽  
Multilinear Regression ◽  
Qsar Analysis ◽  
Main Protease

Due to the genetic similarity between SARS-CoV-2 and SARS-CoV, the present work endeavored to derive a balanced Quantitative Structure−Activity Relationship (QSAR) model, molecular docking, and molecular dynamics (MD) simulation studies to identify novel molecules having inhibitory potential against the main protease (Mpro) of SARS-CoV-2. The QSAR analysis developed on multivariate GA–MLR (Genetic Algorithm–Multilinear Regression) model with acceptable statistical performance (R2 = 0.898, Q2loo = 0.859, etc.). QSAR analysis attributed the good correlation with different types of atoms like non-ring Carbons and Nitrogens, amide Nitrogen, sp2-hybridized Carbons, etc. Thus, the QSAR model has a good balance of qualitative and quantitative requirements (balanced QSAR model) and satisfies the Organisation for Economic Co-operation and Development (OECD) guidelines. After that, a QSAR-based virtual screening of 26,467 food compounds and 360 heterocyclic variants of molecule 1 (benzotriazole–indole hybrid molecule) helped to identify promising hits. Furthermore, the molecular docking and molecular dynamics (MD) simulations of Mpro with molecule 1 recognized the structural motifs with significant stability. Molecular docking and QSAR provided consensus and complementary results. The validated analyses are capable of optimizing a drug/lead candidate for better inhibitory activity against the main protease of SARS-CoV-2.

Υπολογιστικά εργαλεία για την αναζήτηση νέων βιοδραστικών ενώσεων σε επιλεγμένους πρωτεϊνικούς στόχους

2017 ◽  
Author(s):  
Ευτυχία Κρίτση
Keyword(s):  
Molecular Dynamics ◽  
Molecular Docking ◽  
Virtual Screening ◽  
Molecular Dynamics Simulations ◽  
In Silico ◽  
Pharmacophore Modeling ◽  
Lead Optimization ◽  
Dynamics Simulations ◽  
Hiv 1

Στην παρούσα διατριβή πραγματοποιήθηκε εκτενής μελέτη για την αναζήτηση πρόδρομων βιοδραστικών ενώσεων (hits) από χημικές βιβλιοθήκες για τρείς βιολογικούς στόχους, μέσω της εφαρμογής εμπορικά διαθέσιμων in silico τεχνικών και μεθοδολογιών.Οι στόχοι που επιλέχθηκαν ανήκουν σε διαφορετικές κατηγορίες πρωτεϊνών με μεγάλο φαρμακευτικό ενδιαφέρον, που όμως παρουσιάζουν διαφορετικό επίπεδο ωριμότητας όσον αφορά την εφαρμογή υπολογιστικών εργαλείωνγια την ανακάλυψη νέων φαρμακευτικών ενώσεων. Συγκεριμένα, οι στόχοι που μελετήθηκαν είναι οι ακόλουθοι:•το ένζυμο της 14-α διμεθυλάσης της λανοστερόλης (CYP51) για την αναζήτηση νέων πρόδρομων βιοδραστικών ενώσεων με αντιμικροβιακές ιδιότητες,•το ένζυμο της HIV τύπου 1 πρωτεάσης (HIV-1 PR) για την αναζήτηση νέων πρόδρομων βιοδραστικών ενώσεων με αντι-HIV δράση,•ο διαμεμβρανικός υποδοχέας της Αγγειοτασίνης ΙΙ (ΑΤ1) για την αναζήτηση νέων πρόδρομων βιοδραστικών με αντιυπερτασική δράσηΟι κυριότερες τεχνικές που χρησιμοποιήθηκαν για την αναζήτηση πρόδρομων βιοδραστικών ενώσεων περιλαμβάνουν την Εικονική Σάρωση (Virtual Screening) με χρήση Φαρμακοφόρων Μοντέλων (Pharmacophore modeling), τη Μοριακή Πρόσδεση (Molecular Docking), την πρόβλεψη μοριακών ιδιοτήτων καθώς και Προσομοιώσεις Μοριακής Δυναμικής (Molecular Dynamics Simulations). Η στρατηγική που ακολουθήθηκε διαφέρει σημαντικά ανά στόχο όσον αφορά τη μεθοδολογική προσέγγιση και την επιλογή των υπολογιστικών εργαλείων-αλγορίθμων, δίνοντας έμφαση στη συμπληρωματικότητα των αποτελεσμάτων τους. Για την ανάδειξη των πρόδρομων βιοδραστικών ενώσεων, πραγματοποιήθηκαν in vitro βιολογικές δοκιμές των ενώσεων που προτάθηκαν μέσω των υπολογιστικών τεχνικών. Οι ενώσεις που επιλέχθηκαν παρουσίασαν ανασταλτική δράση (ή συγγένεια πρόσδεσης) σε ικανοποιητικό εύρος τιμών 102 nM–μΜ για να χαρακτηριστούν πρόδρομες βιοδραστικές. Μείζονος σημασίας είναι και το γεγονός ότι οι δομικοί σκελετοί των προτεινόμενων ενώσεων για κάθε στόχο, είναι διαφορετικοί τόσο μεταξύ τους όσο και συγκρινόμενοι με τα υφιστάμενα φαρμακευτικά μόρια. Ως εκ τούτου, μπορούν να αποτελέσουν κατάλληλα "υποστρώματα" για το επόμενο στάδιο που αφορά τη βελτιστοποίησή τους προς ενώσεις-οδηγούς (hit to lead optimization) και δυνητικά προς νέα φαρμακευτικά προϊόντα.

Chemometric Modeling of the Ecotoxicity of Industrial Chemicals to an Avian Species Anas Platyrhynchos

2020 ◽  
Vol 5 (2) ◽  
pp. 1-16
Author(s):  
Shinjita Ghosh ◽  
Supratik Kar ◽  
Jerzy Leszczynski
Keyword(s):  
Anas Platyrhynchos ◽  
External Validation ◽  
Quantitative Structure Activity Relationship ◽  
Randomization Test ◽  
Avian Species ◽  
Industrial Chemicals ◽  
Linear Discriminant ◽  
Topological Distance ◽  
Qsar Models ◽  
Classification And Regression

Birds or avians have been imperative species in the ecology, having been evaluated in an effort to understand the toxic effects of endocrine disruption. The ecotoxicity of 56 industrial chemicals classified as endocrine disruptors were modeled employing classification and regression-based quantitative structure-activity relationship (QSAR) models to an important avian species, Anas platyrhynchos. The classification- and regression-based QSAR models were developed using linear discriminant analysis (LDA) and partial least squares (PLS) tools, respectively. All models were validated meticulously by employing internal and external validation metrics followed by randomization test, applicability domain (AD) study, and intelligent consensus prediction of all individual models. Features like topological distance of 1, 3, and 5 between atoms O-P, C-P, and N-S, correspondingly, along with the CR3X fragment, can be responsible for an increase in toxicity. On the contrary, the presence of S-Cl with topological distance 6 is accountable for lowering the toxicity of towards A. platyrhynchos. The developed chemometric models can offer significant evidence and guidance in the framework of virtual screening as well as a toxicity prediction of new and/or untested chemical libraries towards this specific avian species.

scholarly journals Network-based piecewise linear regression for QSAR modelling

2019 ◽  
Vol 33 (9) ◽  
pp. 831-844
Author(s):  
Jonathan Cardoso-Silva ◽  
Lazaros G. Papageorgiou ◽  
Sophia Tsoka
Keyword(s):  
Virtual Screening ◽  
Linear Regression ◽  
Predictive Accuracy ◽  
Piecewise Linear ◽  
Chemical Properties ◽  
Structural Similarity ◽  
Quantitative Structure Activity Relationship ◽  
Support Vector ◽  
Piecewise Linear Regression ◽  
Qsar Models

Abstract Quantitative Structure-Activity Relationship (QSAR) models are critical in various areas of drug discovery, for example in lead optimisation and virtual screening. Recently, the need for models that are not only predictive but also interpretable has been highlighted. In this paper, a new methodology is proposed to build interpretable QSAR models by combining elements of network analysis and piecewise linear regression. The algorithm presented, modSAR, splits data using a two-step procedure. First, compounds associated with a common target are represented as a network in terms of their structural similarity, revealing modules of similar chemical properties. Second, each module is subdivided into subsets (regions), each of which is modelled by an independent linear equation. Comparative analysis of QSAR models across five data sets of protein inhibitors obtained from ChEMBL is reported and it is shown that modSAR offers similar predictive accuracy to popular algorithms, such as Random Forest and Support Vector Machine. Moreover, we show that models built by modSAR are interpretatable, capable of evaluating the applicability domain of the compounds and serve well tasks such as virtual screening and the development of new drug leads.

scholarly journals Molecular modelling studies of quinazolinone derivatives as MMP-13 inhibitors by QSAR, molecular docking and molecular dynamics simulations techniques

MedChemComm ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 101-115 ◽  
Author(s):  
Shanshan Huang ◽  
Kairui Feng ◽  
Yujie Ren
Keyword(s):  
Molecular Dynamics ◽  
Molecular Docking ◽  
Molecular Dynamics Simulations ◽  
Molecular Modelling ◽  
Molecular Modelling Studies ◽  
Qsar Models ◽  
Modelling Studies ◽  
Dynamics Simulations ◽  
Quinazolinone Derivatives

Reliable QSAR models for quinazolinones were constructed and eight novel MMP-13 inhibitors with higher predictive activity were identified.

Molecular Modeling Studies of Anti-Alzheimer Agents by QSAR, Molecular Docking and Molecular Dynamics Simulations Techniques

2020 ◽  
Vol 16 (7) ◽  
pp. 903-927 ◽  
Author(s):  
Rahman Abdizadeh ◽  
Farzin Hadizadeh ◽  
Tooba Abdizadeh
Keyword(s):  
Molecular Dynamics ◽  
Molecular Docking ◽  
Molecular Modeling ◽  
Molecular Dynamics Simulations ◽  
Data Set ◽  
Ache Inhibitors ◽  
Modeling Studies ◽  
Qsar Models ◽  
Molecular Modeling Studies ◽  
Dynamics Simulations

Background: Acetylcholinesterase (AChE), a serine hydrolase, is an important drug target in the treatment of Alzheimer's disease (AD). Thus, novel AChE inhibitors were designed and developed as potential drug candidates, for significant therapy of AD. Objective: In this work, molecular modeling studies, including CoMFA, CoMFA-RF, CoMSIA, HQSAR and molecular docking and molecular dynamics simulations were performed on a series of AChE inhibitors to get more potent anti-Alzheimer drugs. Methods: 2D/3D-QSAR models including CoMFA, CoMFA-RF, CoMSIA, and HQSAR methods were carried out on 40 pyrimidinylthiourea derivatives as data set by the Sybylx1.2 program. Molecular docking and molecular dynamics simulations were performed using the MOE software and the Sybyl program, respectively. Partial least squares (PLS) model as descriptors was used for QSAR model generation. Results: The CoMFA (q2, 0.629; r2ncv, 0.901; r2pred, 0.773), CoMFA-RF (q2, 0.775; r2ncv, 0.910; r2pred, 0.824), CoMSIA (q2, 0.754; r2ncv, 0.919; r2pred, 0.874) and HQSAR models (q2, 0.823; r2ncv, 0.976; r2pred, 0.854) for training and test set yielded significant statistical results. Conclusion: These QSAR models were excellent, robust and had good predictive capability. Contour maps obtained from the QSAR models were validated by molecular dynamics simulationassisted molecular docking study. The resulted QSAR models could be useful for the rational design of novel potent AChE inhibitors in Alzheimer's treatment.

scholarly journals Ligand-Based and Docking-Based Virtual Screening of MDM2 Inhibitors as Potent Anticancer Agents

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Bing-Hui Li ◽  
Jun-Qi Ge ◽  
Ya-Li Wang ◽  
Li-Jun Wang ◽  
Qi Zhang ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Virtual Screening ◽  
Cell Lines ◽  
Anticancer Agents ◽  
Antiproliferative Activity ◽  
Pharmacophore Model ◽  
Inhibition Assay ◽  
Ic50 Values

A ligand-based and docking-based virtual screening was carried out to identify novel MDM2 inhibitors. A pharmacophore model with four features was used for virtual screening, followed by molecular docking. Seventeen compounds were selected for an in vitro MDM2 inhibition assay, and compounds AO-476/43250177, AG-690/37072075, AK-968/15254441, AO-022/43452814, and AF-399/25108021 showed promising MDM2 inhibition activities with K i values of 9.5, 8.5, 23.4, 3.2, and 23.1 μM, respectively. Four compounds also showed antiproliferative activity, and compound AO-022/43452814 was the most potent hit with IC50 values of 19.35, 26.73, 12.63, and 24.14 μM against MCF7 (p53 +/+), MCF7 (p53 -/-), HCT116 (p53 +/+), and HCT116 (p53 -/-) cell lines, respectively. Compound AO-022/43452814 could be used as a scaffold for the development of anticancer agents targeting MDM2.

Application of Docking Methodologies in QSAR-Based Studies

2016 ◽  
pp. 29-55 ◽  
Author(s):  
Omar Deeb ◽  
Heidy Martínez-Pachecho ◽  
Guillermo Ramírez-Galicia ◽  
Ramón Garduño-Juárez
Keyword(s):  
Molecular Docking ◽  
Virtual Screening ◽  
Structural Information ◽  
Quantitative Structure Activity Relationship ◽  
Quantitative Structure ◽  
Structure Based Drug Design ◽  
Drug Activity ◽  
Molecular Binding ◽  
Biochemical Processes ◽  
Docking Approach

The computational strategies permeate all aspects of drug discovery such as virtual screening techniques. Virtual screening can be classified into ligand based and structure based methods. The ligand based method such as Quantitative Structure Activity Relationship (QSAR) is used when a set of active ligand compounds is recognized and slight or no structural information is available for the receptors. In structure based drug design, the most widespread method is molecular docking. It is widely accepted that drug activity is obtained through the molecular binding of one ligand to receptor. In their binding conformations, the molecules exhibit geometric and chemical complementarity, both of which are essential for successful drug activity. The molecular docking approach can be used to model the interaction between a small drug molecule and a protein, which allow us to characterize the performance of small molecules in the binding site of target proteins as well as to clarify fundamental biochemical processes.

scholarly journals Evaluation of QSAR Equations for Virtual Screening

2020 ◽  
Vol 21 (21) ◽  
pp. 7828
Author(s):  
Jacob Spiegel ◽  
Hanoch Senderowitz
Keyword(s):  
Virtual Screening ◽  
Quantitative Structure Activity Relationship ◽  
Screening Tests ◽  
Support Vector ◽  
Small Scale ◽  
Active Compounds ◽  
Qsar Models ◽  
Continuous Responses ◽  
Test Sets ◽  
Binary Classifiers

Quantitative Structure Activity Relationship (QSAR) models can inform on the correlation between activities and structure-based molecular descriptors. This information is important for the understanding of the factors that govern molecular properties and for designing new compounds with favorable properties. Due to the large number of calculate-able descriptors and consequently, the much larger number of descriptors combinations, the derivation of QSAR models could be treated as an optimization problem. For continuous responses, metrics which are typically being optimized in this process are related to model performances on the training set, for example, R2 and QCV2. Similar metrics, calculated on an external set of data (e.g., QF1/F2/F32), are used to evaluate the performances of the final models. A common theme of these metrics is that they are context -” ignorant”. In this work we propose that QSAR models should be evaluated based on their intended usage. More specifically, we argue that QSAR models developed for Virtual Screening (VS) should be derived and evaluated using a virtual screening-aware metric, e.g., an enrichment-based metric. To demonstrate this point, we have developed 21 Multiple Linear Regression (MLR) models for seven targets (three models per target), evaluated them first on validation sets and subsequently tested their performances on two additional test sets constructed to mimic small-scale virtual screening campaigns. As expected, we found no correlation between model performances evaluated by “classical” metrics, e.g., R2 and QF1/F2/F32 and the number of active compounds picked by the models from within a pool of random compounds. In particular, in some cases models with favorable R2 and/or QF1/F2/F32 values were unable to pick a single active compound from within the pool whereas in other cases, models with poor R2 and/or QF1/F2/F32 values performed well in the context of virtual screening. We also found no significant correlation between the number of active compounds correctly identified by the models in the training, validation and test sets. Next, we have developed a new algorithm for the derivation of MLR models by optimizing an enrichment-based metric and tested its performances on the same datasets. We found that the best models derived in this manner showed, in most cases, much more consistent results across the training, validation and test sets and outperformed the corresponding MLR models in most virtual screening tests. Finally, we demonstrated that when tested as binary classifiers, models derived for the same targets by the new algorithm outperformed Random Forest (RF) and Support Vector Machine (SVM)-based models across training/validation/test sets, in most cases. We attribute the better performances of the Enrichment Optimizer Algorithm (EOA) models in VS to better handling of inactive random compounds. Optimizing an enrichment-based metric is therefore a promising strategy for the derivation of QSAR models for classification and virtual screening.

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