Improving ensemble docking for drug discovery by machine learning

2019 ◽  
Vol 18 (03) ◽  
pp. 1920001 ◽  
Author(s):  
Chung F. Wong
Keyword(s):  
Machine Learning ◽  
Molecular Docking ◽  
Drug Discovery ◽  
Inexpensive Method ◽  
Receptor Flexibility ◽  
Ensemble Docking ◽  
Ensemble Machine Learning ◽  
Multiple Structures

Ensemble docking has provided an inexpensive method to account for receptor flexibility in molecular docking. However, it is still unclear how best to use the docking scores from multiple structures to classify compounds into actives and inactives. Previous studies have also found that the performance of classification could decrease rather than increase with the number of structures included in the ensemble. Machine learning could help to alleviate these problems.

scholarly journals Retrospective Ensemble Docking of Allosteric Modulators in an Adenosine G-Protein-Coupled Receptor

2020 ◽  
Author(s):  
Apurba Bhattarai ◽  
Jinan Wang ◽  
Yinglong Miao
Keyword(s):  
Molecular Docking ◽  
Drug Discovery ◽  
G Protein ◽  
Drug Targets ◽  
Ischemia Reperfusion ◽  
Renal Diseases ◽  
Allosteric Modulators ◽  
Receptor Flexibility ◽  
Ensemble Docking ◽  
G Protein Coupled

AbstractBackgroundEnsemble docking has proven useful in drug discovery and development. It increases the hit rate by incorporating receptor flexibility into molecular docking as demonstrated on important drug targets including G-protein-coupled receptors (GPCRs). Adenosine A1 receptor (A1AR) is a key GPCR that has been targeted for treating cardiac ischemia-reperfusion injuries, neuropathic pain and renal diseases. Development of allosteric modulators, compounds binding to distinct and less conserved GPCR target sites compared with agonists and antagonists, has attracted increasing interest for designing selective drugs of the A1AR. Despite significant advances, more effective approaches are needed to discover potent and selective allosteric modulators of the A1AR.MethodsEnsemble docking that integrates Gaussian accelerated molecular dynamic (GaMD) simulations and molecular docking using Autodock has been implemented for retrospective docking of known positive allosteric modulators (PAMs) in the A1AR.ResultsEnsemble docking outperforms docking of the receptor cryo-EM structure. The calculated docking enrichment factors (EFs) and the area under the receiver operating characteristic curves (AUC) are significantly increased.ConclusionsReceptor ensembles generated from GaMD simulations are able to increase the success rate of discovering PAMs of A1AR. It is important to account for receptor flexibility through GaMD simulations and flexible docking.General SignificanceEnsemble docking is a promising approach for drug discovery targeting flexible receptors.

scholarly journals Using machine learning to improve ensemble docking for drug discovery

2020 ◽  
Vol 88 (10) ◽  
pp. 1263-1270 ◽  
Author(s):  
Tanay Chandak ◽  
John P. Mayginnes ◽  
Howard Mayes ◽  
Chung F. Wong
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Ensemble Docking

A combined drug discovery strategy based on machine learning and molecular docking

2019 ◽  
Vol 93 (5) ◽  
pp. 685-699 ◽  
Author(s):  
Yanmin Zhang ◽  
Yuchen Wang ◽  
Weineng Zhou ◽  
Yuanrong Fan ◽  
Junnan Zhao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Molecular Docking ◽  
Drug Discovery

Structure-Based Virtual Screening of Perfluoroalkyl and Polyfluoroalkyl Substances (PFASs) as Endocrine Disruptors of Androgen Receptor Activity Using Molecular Docking and Machine Learning

2020 ◽  
Author(s):  
Azhagiya Singam Ettayapuram Ramaprasad ◽  
Phum Tachachartvanich ◽  
Denis Fourches ◽  
Anatoly Soshilov ◽  
Jennifer C.Y. Hsieh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Molecular Docking ◽  
Androgen Receptor ◽  
Endocrine Disruptors ◽  
Hormone Receptors ◽  
Steroid Hormone Receptors ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Polyfluoroalkyl Substances ◽  
Perfluoroalkyl And Polyfluoroalkyl Substances

Perfluoroalkyl and Polyfluoroalkyl Substances (PFASs) pose a substantial threat as endocrine disruptors, and thus early identification of those that may interact with steroid hormone receptors, such as the androgen receptor (AR), is critical. In this study we screened 5,206 PFASs from the CompTox database against the different binding sites on the AR using both molecular docking and machine learning techniques. We developed support vector machine models trained on Tox21 data to classify the active and inactive PFASs for AR using different chemical fingerprints as features. The maximum accuracy was 95.01% and Matthew’s correlation coefficient (MCC) was 0.76 respectively, based on MACCS fingerprints (MACCSFP). The combination of docking-based screening and machine learning models identified 29 PFASs that have strong potential for activity against the AR and should be considered priority chemicals for biological toxicity testing.

Dose COVID-19 Uncovered a New Feature of Metronidazole Drug?

2020 ◽  
Author(s):  
Mohammad Seyedhamzeh ◽  
Bahareh Farasati Far ◽  
Mehdi Shafiee Ardestani ◽  
Shahrzad Javanshir ◽  
Fatemeh Aliabadi ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Active Sites ◽  
Initial Plasma ◽  
New Feature ◽  
Metronidazole Benzoate ◽  
Global Health Problem ◽  
A Current ◽  
Pro Inflammatory Cytokines

Studies of coronavirus disease 2019 (COVID-19) as a current global health problem shown the initial plasma levels of most pro-inflammatory cytokines increased during the infection, which leads to patient countless complications. Previous studies also demonstrated that the metronidazole (MTZ) administration reduced related cytokines and improved treatment in patients. However, the effect of this drug on cytokines has not been determined. In the present study, the interaction of MTZ with cytokines was investigated using molecular docking as one of the principal methods in drug discovery and design. According to the obtained results, the IL12-metronidazole complex is more stable than other cytokines, and an increase in the surface and volume leads to prevent to bind to receptors. Moreover, ligand-based virtual screening of several libraries showed metronidazole phosphate, metronidazole benzoate, 1-[1-(2-Hydroxyethyl)-5- nitroimidazol-2-yl]-N-methylmethanimine oxide, acyclovir, and tetrahydrobiopterin (THB or BH4) like MTZ by changing the surface and volume prevents binding IL-12 to the receptor. Finally, the inhibition of the active sites of IL-12 occurred by modifying the position of the methyl and hydroxyl functional groups in MTZ. <br>

Design, Synthesis and Docking Studies of Some Novel Fluoroquinolone Compounds with Antibacterial Activity

2018 ◽  
Vol 69 (4) ◽  
pp. 815-822 ◽  
Author(s):  
Lucia Pintilie ◽  
Amalia Stefaniu ◽  
Alina Ioana Nicu ◽  
Maria Maganu ◽  
Miron Teodor Caproiu
Keyword(s):  
Antimicrobial Activity ◽  
Antibacterial Activity ◽  
Molecular Docking ◽  
Drug Discovery ◽  
Elemental Analysis ◽  
Docking Studies ◽  
Receptor Protein ◽  
Chemical Methods ◽  
Design Synthesis ◽  
Gram Negative

A new series of fluoroquinolone compounds have been obtained by Gould-Jacobs method. The compounds have been characterized by physic-chemical methods (elemental analysis, FTIR, NMR, UV-Vis) and by antimicrobial activity against Gram-positive and Gram-negative microorganisms. For the synthesized compounds have been performed calculations of characteristics and molecular properties, using Spartan�14 Software from Wavefunction, Inc. Irvine, CA. and molecular docking studies using CLC Drug Discovery Workbench 2.4 software, to identify and visualize the most likely interaction ligand (fluoroquinolone) with the receptor protein.

Molecular Docking and Drug Discovery in β-Adrenergic Receptors

2017 ◽  
Vol 24 (39) ◽  
Author(s):  
Santiago Vilar ◽  
Eduardo Sobarzo-Sanchez ◽  
Lourdes Santana ◽  
Eugenio Uriarte
Keyword(s):  
Molecular Docking ◽  
Drug Discovery ◽  
Adrenergic Receptors ◽  
Β Adrenergic Receptors
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