scholarly journals Ligand and structure-based virtual screening applied to the SARS-CoV-2 main protease: an in silico repurposing study

2020 ◽  
Vol 12 (20) ◽  
pp. 1815-1828 ◽  
Author(s):  
Witor Ribeiro Ferraz ◽  
Renan Augusto Gomes ◽  
Andre Luis S Novaes ◽  
Gustavo Henrique Goulart Trossini
Keyword(s):  
Drug Discovery ◽  
Virtual Screening ◽  
Drug Repurposing ◽  
Binding Modes ◽  
Discovery Process ◽  
Drug Discovery Process ◽  
Future Studies ◽  
Main Protease ◽  
Promising Target ◽  
Approved Drugs

Aim: The identification of drugs for the coronavirus disease-19 pandemic remains urgent. In this manner, drug repurposing is a suitable strategy, saving resources and time normally spent during regular drug discovery frameworks. Essential for viral replication, the main protease has been explored as a promising target for the drug discovery process. Materials & methods: Our virtual screening pipeline relies on the known 3D properties of noncovalent ligands and features of crystalized complexes, applying consensus analyses in each step. Results: Two oral (bedaquiline and glibenclamide) and one buccal drug (miconazole) presented 3D similarity to known ligands, reasonable predicted binding modes and micromolar predicted binding affinity values. Conclusion: We identified three approved drugs as promising inhibitors of the main viral protease and suggested design insights for future studies for development of novel selective inhibitors.

scholarly journals Screening of Potential Indonesia Herbal Compounds Based on Multi-Label Classification for 2019 Coronavirus Disease

2021 ◽  
Vol 5 (4) ◽  
pp. 75
Author(s):  
Aulia Fadli ◽  
Wisnu Ananta Kusuma ◽  
Annisa ◽  
Irmanida Batubara ◽  
Rudi Heryanto
Keyword(s):  
Drug Discovery ◽  
Drug Target ◽  
Deep Neural Network ◽  
Drug Repurposing ◽  
Discovery Process ◽  
Drug Discovery Process ◽  
Herbal Plants ◽  
Approved Drugs ◽  
Pubchem Fingerprint ◽  
F Measure

Coronavirus disease 2019 pandemic spreads rapidly and requires an acceleration in the process of drug discovery. Drug repurposing can help accelerate the drug discovery process by identifying new efficacy for approved drugs, and it is considered an efficient and economical approach. Research in drug repurposing can be done by observing the interactions of drug compounds with protein related to a disease (DTI), then predicting the new drug-target interactions. This study conducted multilabel DTI prediction using the stack autoencoder-deep neural network (SAE-DNN) algorithm. Compound features were extracted using PubChem fingerprint, daylight fingerprint, MACCS fingerprint, and circular fingerprint. The results showed that the SAE-DNN model was able to predict DTI in COVID-19 cases with good performance. The SAE-DNN model with a circular fingerprint dataset produced the best average metrics with an accuracy of 0.831, recall of 0.918, precision of 0.888, and F-measure of 0.89. Herbal compounds prediction results using the SAE-DNN model with the circular, daylight, and PubChem fingerprint dataset resulted in 92, 65, and 79 herbal compounds contained in herbal plants in Indonesia respectively.

scholarly journals Selecting Machine-Learning Scoring Functions for Structure-Based Virtual Screening

2020 ◽  
Author(s):  
Pedro Ballester
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Predictive Accuracy ◽  
Scoring Function ◽  
3D Models ◽  
Large Datasets ◽  
Scoring Functions ◽  
Discovery Process ◽  
Drug Discovery Process

Interest in docking technologies has grown parallel to the ever increasing number and diversity of 3D models for macromolecular therapeutic targets. Structure-Based Virtual Screening (SBVS) aims at leveraging these experimental structures to discover the necessary starting points for the drug discovery process. It is now established that Machine Learning (ML) can strongly enhance the predictive accuracy of scoring functions for SBVS by exploiting large datasets from targets, molecules and their associations. However, with greater choice, the question of which ML-based scoring function is the most suitable for prospective use on a given target has gained importance. Here we analyse two approaches to select an existing scoring function for the target along with a third approach consisting in generating a scoring function tailored to the target. These analyses required discussing the limitations of popular SBVS benchmarks, the alternatives to benchmark scoring functions for SBVS and how to generate them or use them using freely-available software.

scholarly journals Repurposing of FDA Approved Drugs for the Identification of Potential Inhibitors of SARS-CoV-2 Main Protease

2020 ◽  
Author(s):  
Abhik Kumar Ray ◽  
Parth Sarthi Sen Gupta ◽  
Saroj Kumar Panda ◽  
Satyaranjan Biswal ◽  
Malay Kumar Rana
Keyword(s):  
Virtual Screening ◽  
Binding Energies ◽  
Great Promise ◽  
Main Protease ◽  
Promising Target ◽  
Inhibitory Potential ◽  
Novel Coronavirus ◽  
Approved Drugs ◽  
Potential Inhibitors ◽  
Fda Approved Drugs

<p>COVID-19, responsible for several deaths, demands a cumulative effort of scientists worldwide to curb the pandemic. The main protease, responsible for the cleavage of the polyprotein and formation of replication complex in virus, is considered as a promising target for the development of potential inhibitors to treat the novel coronavirus. The effectiveness of FDA approved drugs targeting the main protease in previous SARS-COV (s) reported earlier indicates the chances of success for the repurposing of FDA drugs against SARS-COV-2. Therefore, in this study, molecular docking and virtual screening of FDA approved drugs, primarily of three categories: antiviral, antimalarial, and peptide, are carried out to investigate their inhibitory potential against the main protease. Virtual screening has identified 53 FDA drugs on the basis of their binding energies (< -7.0 kcal/mol), out of which the top two drugs Velpatasvir (-9.1 kcal/mol) and Glecaprevir (-9.0 kcal/mol) seem to have great promise. These drugs have a stronger affinity to the SARS-CoV-2 main protease than the crystal bound inhibitor α-ketoamide 13B (-6.7 kcal/mol) or Indinavir (-7.5 kcal/mol) that has been proposed in a recent study as one of the best drugs for SARS-CoV-2. The <i>in-silico</i> efficacies of the screened drugs could be instructive for further biochemical and structural investigation for repurposing. The molecular dynamics studies on the shortlisted drugs are underway. </p>

scholarly journals Selecting Machine-Learning Scoring Functions for Structure-Based Virtual Screening

2020 ◽  
Author(s):  
Pedro Ballester
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Predictive Accuracy ◽  
Scoring Function ◽  
3D Models ◽  
Large Datasets ◽  
Scoring Functions ◽  
Discovery Process ◽  
Drug Discovery Process

Interest in docking technologies has grown parallel to the ever increasing number and diversity of 3D models for macromolecular therapeutic targets. Structure-Based Virtual Screening (SBVS) aims at leveraging these experimental structures to discover the necessary starting points for the drug discovery process. It is now established that Machine Learning (ML) can strongly enhance the predictive accuracy of scoring functions for SBVS by exploiting large datasets from targets, molecules and their associations. However, with greater choice, the question of which ML-based scoring function is the most suitable for prospective use on a given target has gained importance. Here we analyse two approaches to select an existing scoring function for the target along with a third approach consisting in generating a scoring function tailored to the target. These analyses required discussing the limitations of popular SBVS benchmarks, the alternatives to benchmark scoring functions for SBVS and how to generate them or use them using freely-available software.

scholarly journals Virtual Screening of Drug Likeness using Tree Based Ensemble Classifier

2018 ◽  
Vol 11 (3) ◽  
pp. 1513-1519 ◽  
Author(s):  
R. Ani ◽  
Roshini Manohar ◽  
Gayathri Anil ◽  
O.S. Deepa
Keyword(s):  
Drug Discovery ◽  
Virtual Screening ◽  
Molecular Descriptor ◽  
Computational Procedure ◽  
Ensemble Classifier ◽  
Support Vector ◽  
Classification Algorithms ◽  
Discovery Process ◽  
Drug Discovery Process ◽  
Rotation Forest

In earlier years, the Drug discovery process took years to identify and process a Drug. It takes a normal of 12 years for a Drug to travel from the research lab to the patient. With the introduction of Machine Learning in Drug discovery, the whole process turned out to be simple. The utilization of computational tools in the early stages of Drug development has expanded in recent decades. A computational procedure carried out in Drug discovery process is Virtual Screening (VS). VS are used to identify the compounds which can bind to a Drug target. The preliminary process before analyzing the bonding of ligand and drug protein target is the prediction of drug likeness of compounds. The main objective of this study is to predict Drug likeness properties of Drug compounds based on molecular descriptor information using Tree based ensembles. In this study, many classification algorithms are analyzed and the accuracy for the prediction of drug likeness is calculated. The study shows that accuracy of rotation forest outperforms the accuracy of other classification algorithms in the prediction of drug likeness of chemical compounds. The measured accuracies of the Rotation Forest, Random Forest, Support Vector Machines, KNN, Decision Tree and Naïve Bayes are 98%, 97%, 94.8%, 92.8%, 91.4%, 89.5% respectively.

scholarly journals In Silico Identification and Docking-Based Drug Repurposing Against the Main Protease of SARS-CoV-2, Causative Agent of COVID-19

2020 ◽  
Author(s):  
Yogesh Kumar ◽  
Harvijay Singh
Keyword(s):  
Virtual Screening ◽  
Active Site ◽  
Viral Infections ◽  
Drug Repurposing ◽  
Docking Study ◽  
Docking Studies ◽  
Main Protease ◽  
Novel Coronavirus ◽  
Approved Drugs

<div>The rapidly enlarging COVID-19 pandemic caused by novel SARS-coronavirus 2 is a global</div><div>public health emergency of unprecedented level. Therefore the need of a drug or vaccine that</div><div>counter SARS-CoV-2 is an utmost requirement at this time. Upon infection the ssRNA genome</div><div>of SARS-CoV-2 is translated into large polyprotein which further processed into different</div><div>nonstructural proteins to form viral replication complex by virtue of virus specific proteases:</div><div>main protease (3-CL protease) and papain protease. This indispensable function of main protease</div><div>in virus replication makes this enzyme a promising target for the development of inhibitors and</div><div>potential treatment therapy for novel coronavirus infection. The recently concluded α-ketoamide</div><div>ligand bound X-ray crystal structure of SARS-CoV-2 Mpro (PDB ID: 6Y2F) from Zhang et al.</div><div>has revealed the potential inhibitor binding mechanism and the determinants responsible for</div><div>involved molecular interactions. Here, we have carried out a virtual screening and molecular</div><div>docking study of FDA approved drugs primarily targeted for other viral infections, to investigate</div><div>their binding affinity in Mpro active site. Virtual screening has identified a number of antiviral</div><div>drugs, top ten of which on the basis of their bending energy score are further examined through </div><div>molecular docking with Mpro. Docking studies revealed that drug Lopinavir-Ritonavir, Tipranavir</div><div>and Raltegravir among others binds in the active site of the protease with similar or higher</div><div>affinity than the crystal bound inhibitor α-ketoamide. However, the in-vitro efficacies of the drug</div><div>molecules tested in this study, further needs to be corroborated by carrying out biochemical and</div><div>structural investigation. Moreover, this study advances the potential use of existing drugs to be</div><div>investigated and used to contain the rapidly expanding SARS-CoV-2 infection.</div>

Drug repurposing: a promising tool to accelerate the drug discovery process

2019 ◽  
Vol 24 (10) ◽  
pp. 2076-2085 ◽  
Author(s):  
Vineela Parvathaneni ◽  
Nishant S. Kulkarni ◽  
Aaron Muth ◽  
Vivek Gupta
Keyword(s):  
Drug Discovery ◽  
Drug Repurposing ◽  
Discovery Process ◽  
Drug Discovery Process ◽  
Promising Tool

Virtual Screening and Its Applications in Drug Discovery Process

2019 ◽  
pp. 101-126
Author(s):  
Gurusamy Mariappan ◽  
Anju Kumari
Keyword(s):  
Drug Discovery ◽  
Virtual Screening ◽  
Drug Design ◽  
Screening Tool ◽  
Log P ◽  
Discovery Process ◽  
Drug Discovery Process ◽  
Financial Loss ◽  
Structure Based Drug Design ◽  
Modern Drug

Virtual screening plays an important role in the modern drug discovery process. The pharma companies invest huge amounts of money and time in drug discovery and screening. However, at the final stage of clinical trials, several molecules fail, which results in a large financial loss. To overcome this, a virtual screening tool was developed with super predictive power. The virtual screening tool is not only restricted tool small molecules but also to macromolecules such as protein, enzyme, receptors, etc. This gives an insight into structure-based and Ligand-based drug design. VS gives reliable information to direct the process of drug discovery (e.g., when the 3D image of the receptor is known, structure-based drug design is recommended). The pharmacophore-based model is advisable when the information about the receptor or any macromolecule is unknown. In this ADME, parameters such as Log P, bioavailability, and QSAR can be used as filters. This chapter shows both models with various representative examples that facilitate the scientist to use computational screening tools in modern drug discovery processes.

scholarly journals Identification of Potential Molecules Against COVID-19 Main Protease Through Structure-Guided Virtual Screening Approach

2020 ◽  
Author(s):  
Lovika Mittal ◽  
Anita Kumari ◽  
Mitul Srivastava ◽  
Mrityunjay Singh ◽  
Shailendra Asthana
Keyword(s):  
Virtual Screening ◽  
Design Method ◽  
Hot Spot ◽  
Drug Repurposing ◽  
Inhibitory Effect ◽  
Drug Molecules ◽  
Main Protease ◽  
Approved Drugs ◽  
Novel Drug

<p>In this work, computer-aided drug design method has been implemented to quickly identify promising drug repurposing candidates against COVID-19 main protease (M<sup>pro</sup>)<sup> </sup>. The world is facing an epidemic and in absence of vaccine or any effective treatment, it has created a sense of urgency for novel drug discovery approaches. We have made an immediate effort by performing virtual screening of clinically approved drugs or molecules under clinical trials against COVID-19 M<sup>pro</sup> to identify potential drug molecules. With given knowledge of this system, N3 and 13B compounds have shown inhibitory effect against COVID-19 M<sup>pro</sup>. Both the compounds were considered as control to filter out the screened molecules. Overall, we have identified six potential compounds, Leupeptin Hemisulphate, Pepstatin A, Nelfinavir , Birinapant, Lypression and Octeotide which have shown the docking energy > -8.0 kcal/mol and MMGBSA > -68.0 kcal/mol. The binding pattern of these compounds suggests that they interacted with key <i>hot-spot</i> residues. Also, their pharmacokinetic annotations and therapeutic importance have indicated that they possess drug-like properties and could pave their way for<i> in-vitro</i> studies. The findings of this work will be significant for structure and pharmacophore-based designing. </p>

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