On the Virtues of Automated Quantitative Structure–Activity Relationship: The New Kid on the Block

Oliveira, Marcelo Tavares de; Katekawa, Edson

doi:10.4155/fmc-2017-0170

Cited by 16 publications

(9 citation statements)

References 45 publications

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“…For AChE, kpl_radial_17 was the best model chosen based on the prediction ranking of the all-model outcomes; BChE, pls_38 and MAO, kpls_desc_44 was chosen respectively. Furthermore, the predictive precision of the model is assessed utilizing different indices like ranking score, root mean square error (RMSE), standard deviation (SD), Q 2 and R 2 values [81]. Furthermore, the predictive capability of a QSAR model can be assessed by the accompanying statistical attributes of the test set which was suggested by [57]: namely the correlation coefficient R between the predicted and observed activities.…”

Section: Machine Learning Development Of Automated Qsar Model Dataset Generation and Preparationmentioning

confidence: 99%

Deciphering the Interactions of Bioactive Compounds in Selected Traditional Medicinal Plants against Alzheimer’s Diseases via Pharmacophore Modeling, Auto-QSAR, and Molecular Docking Approaches

Ojo

Okolie

et al. 2021

Molecules

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Neurodegenerative diseases, for example Alzheimer’s, are perceived as driven by hereditary, cellular, and multifaceted biochemical actions. Numerous plant products, for example flavonoids, are documented in studies for having the ability to pass the blood-brain barrier and moderate the development of such illnesses. Computer-aided drug design (CADD) has achieved importance in the drug discovery world; innovative developments in the aspects of structure identification and characterization, bio-computational science, and molecular biology have added to the preparation of new medications towards these ailments. In this study we evaluated nine flavonoid compounds identified from three medicinal plants, namely T. diversifolia, B. sapida, and I. gabonensis for their inhibitory role on acetylcholinesterase (AChE), butyrylcholinesterase (BChE) and monoamine oxidase (MAO) activity, using pharmacophore modeling, auto-QSAR prediction, and molecular studies, in comparison with standard drugs. The results indicated that the pharmacophore models produced from structures of AChE, BChE and MAO could identify the active compounds, with a recuperation rate of the actives found near 100% in the complete ranked decoy database. Moreso, the robustness of the virtual screening method was accessed by well-established methods including enrichment factor (EF), receiver operating characteristic curve (ROC), Boltzmann-enhanced discrimination of receiver operating characteristic (BEDROC), and area under accumulation curve (AUAC). Most notably, the compounds’ pIC50 values were predicted by a machine learning-based model generated by the AutoQSAR algorithm. The generated model was validated to affirm its predictive model. The best models achieved for AChE, BChE and MAO were models kpls_radial_17 (R2 = 0.86 and Q2 = 0.73), pls_38 (R2 = 0.77 and Q2 = 0.72), kpls_desc_44 (R2 = 0.81 and Q2 = 0.81) and these externally validated models were utilized to predict the bioactivities of the lead compounds. The binding affinity results of the ligands against the three selected targets revealed that luteolin displayed the highest affinity score of −9.60 kcal/mol, closely followed by apigenin and ellagic acid with docking scores of −9.60 and −9.53 kcal/mol, respectively. The least binding affinity was attained by gallic acid (−6.30 kcal/mol). The docking scores of our standards were −10.40 and −7.93 kcal/mol for donepezil and galanthamine, respectively. The toxicity prediction revealed that none of the flavonoids presented toxicity and they all had good absorption parameters for the analyzed targets. Hence, these compounds can be considered as likely leads for drug improvement against the same.

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Section: Machine Learning Development Of Automated Qsar Model Dataset Generation and Preparationmentioning

confidence: 99%

Deciphering the Interactions of Bioactive Compounds in Selected Traditional Medicinal Plants against Alzheimer’s Diseases via Pharmacophore Modeling, Auto-QSAR, and Molecular Docking Approaches

Ojo

Okolie

et al. 2021

Molecules

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“…AutoQSAR, a machine-learning algorithm provided by Schrödinger suite computed about 497 physicochemical and topological descriptors, alongside a variety of Canvas fingerprints (de Oliveira and Katekawa 2017 ), giving out a large pool of independent variables from which to build models. The AutoQSAR splits the experimental compounds randomly into 75% training set, and 25% test set (Table 8 ).…”

Section: Discussionmentioning

confidence: 99%

“…An online converting tool was employed to convert the compounds IC 50 to pIC50 (Selvaraj et al 2011 ). A machine-learning algorithm called AutoQSAR was used to build the QSAR model through automation (de Oliveira and Katekawa 2017 ).…”

Section: Preparation Of Dataset and Generation Of Automated Qsarmentioning

confidence: 99%

Identification of lead compounds from large natural product library targeting 3C-like protease of SARS-CoV-2 using E-pharmacophore modelling, QSAR and molecular dynamics simulation

Elekofehinti

Iwaloye

Molehin

et al. 2021

In Silico Pharmacol.

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COVID-19 is a novel disease caused by SARS-CoV-2 and has made a catastrophic impact on the global economy. As it is, there is no officially FDA approved drug to alleviate the negative impact of SARS-CoV-2 on human health. Numerous drug targets for neutralizing coronavirus infection have been identified, among them is 3-chymotrypsin-like-protease (3CL pro ), a viral protease responsible for the viral replication is chosen for this study. This study aimed at finding novel inhibitors of SARS-CoV-2 3C-like protease from the natural library using computational approaches. A total of 69,000 compounds from natural product library were screened to match a minimum of 3 features from the five sites e-pharmacophore model. Compounds with fitness score of 1.00 and above were consequently filtered by executing molecular docking studies via Glide docking algorithm. Qikprop also predicted the compounds drug-likeness and pharmacokinetic features; besides, the QSAR model built from KPLS analysis with radial as binary fingerprint was used to predict the compounds inhibition properties against SARS-CoV-2 3C-like protease. Fifty ns molecular dynamics (MD) simulation was carried out using GROMACS software to understand the dynamics of binding. Nine (9) lead compounds from the natural products library were discovered; seven among them were found to be more potent than lopinavir based on energies of binding. STOCK1N-98687 with docking score of −9.295 kcal/mol had considerable predicted bioactivity (4.427 µM) against SARS-CoV-2 3C-like protease and satisfactory drug-like features than the experimental drug lopinavir. Post-docking analysis by MM-GBSA confirmed the stability of STOCK1N-98687 bound 3CL pro crystal structure. MD simulation of STOCKIN-98687 with 3CL pro at 50 ns showed high stability and low fluctuation of the complex. This study revealed compound STOCK1N-98687 as potential 3CL pro inhibitor; therefore, a wet experiment is worth exploring to confirm the therapeutic potential of STOCK1N-98687 as an antiviral agent.

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“…It will then compute the fingerprints and descriptors using machine‐learning statistical methods for creating a predictive QSAR model. The predictive accuracy of the model is evaluated using various parameters such as ranking score, root mean square error (RMSE), standard deviation (SD), Q 2 , and R 2 values 27 . It is worth mentioning that the present analysis utilizes a total of 100 3C‐like proteinase inhibitors for predictive model development.…”

Section: Methodsmentioning

confidence: 99%

“…The predictive accuracy of the model is evaluated using various parameters such as ranking score, root mean square error (RMSE), standard deviation (SD), Q 2 , and R 2 values. 27 It is worth mentioning that the present analysis utilizes a total of 100 3C-like proteinase inhibitors for predictive model development. The details of the molecules along with their pIC 50 values are presented in Table S1 in the Supporting Information.…”

Section: Machine Learning Principles Using Autoqsarmentioning

confidence: 99%

Discovery of potent Covid‐19 main protease inhibitors using integrated drug‐repurposing strategy

Rohini

James

et al. 2021

Biotech and App Biochem

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The emergence and rapid spreading of novel SARS-CoV-2 across the globe represent an imminent threat to public health. Novel antiviral therapies are urgently needed to overcome this pandemic. Given the significant role of the main protease of Covid-19 for virus replication, we performed a drug-repurposing study using the recently deposited main protease structure, 6LU7. For instance, pharmacophore-and e-pharmacophore-based hypotheses such as AARRH and AARR, respectively, were developed using available small molecule inhibitors and utilized in the screening of the DrugBank repository. Further, a hierarchical docking protocol was implemented with the support of the Glide algorithm. The resultant compounds were then examined for their binding free energy against the main protease of Covid-19 by means of the Prime-MM/GBSA algorithm. Most importantly, the machine learning-based AutoQSAR algorithm was used to predict the antiviral activities of resultant compounds. The hit molecules were also examined for their drug-likeness and toxicity parameters through the QikProp algorithm. Finally, the hit compounds activity against the main protease was validated using molecular dynamics simulation studies. Overall, the present analysis yielded two potential inhibitors (DB02986 and DB08573) that are predicted to bind with the main protease of Covid-19 better than currently used drug molecules such as N3 (cocrystallized native ligand), lopinavir, and ritonavir.

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On the Virtues of Automated Quantitative Structure–Activity Relationship: The New Kid on the Block

Cited by 16 publications

References 45 publications

Deciphering the Interactions of Bioactive Compounds in Selected Traditional Medicinal Plants against Alzheimer’s Diseases via Pharmacophore Modeling, Auto-QSAR, and Molecular Docking Approaches

Deciphering the Interactions of Bioactive Compounds in Selected Traditional Medicinal Plants against Alzheimer’s Diseases via Pharmacophore Modeling, Auto-QSAR, and Molecular Docking Approaches

Identification of lead compounds from large natural product library targeting 3C-like protease of SARS-CoV-2 using E-pharmacophore modelling, QSAR and molecular dynamics simulation

Discovery of potent Covid‐19 main protease inhibitors using integrated drug‐repurposing strategy

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