Methods for Docking and Drug Designing

2016 ◽  
pp. 39-53
Author(s):  
Ahmad Abu Turab Naqvi ◽  
Md. Imtaiyaz Hassan
Keyword(s):  
Molecular Docking ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Force Field ◽  
De Novo ◽  
Scoring Functions ◽  
Receptor Molecule ◽  
Knowledge Based ◽  
Ligand Screening ◽  
Novel Drug

Molecular docking is the prediction of conformational complementarity between ligand and receptor molecule. The process of docking integrates two schematic approaches namely sampling of ligand conformations and ranking of selected conformations based on scoring functions. The authors have discussed established methodologies for molecular docking and well-known tools implementing these methods. A brief account of different classes of scoring functions such as force field based, empirical, knowledge based, and descriptor based scoring functions is given along with the exemplary implementations of these scoring functions. By replacing test and trial based ligand screening with structure based virtual screening, molecular docking has helped in shortening the duration of novel drug discovery up to some extent. However, the developments made in the field of drug discovery are assisted by the advances in the techniques of molecular docking, but there is strong need of enrichment in the techniques, especially in scoring functions, to tackle the inbound problems of de novo drug discovery.

Methods for Docking and Drug Designing

Oncology ◽  
2017 ◽  
pp. 876-890
Author(s):  
Ahmad Abu Turab Naqvi ◽  
Md. Imtaiyaz Hassan
Keyword(s):  
Molecular Docking ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Force Field ◽  
De Novo ◽  
Scoring Functions ◽  
Receptor Molecule ◽  
Knowledge Based ◽  
Ligand Screening ◽  
Novel Drug

Molecular docking is the prediction of conformational complementarity between ligand and receptor molecule. The process of docking integrates two schematic approaches namely sampling of ligand conformations and ranking of selected conformations based on scoring functions. The authors have discussed established methodologies for molecular docking and well-known tools implementing these methods. A brief account of different classes of scoring functions such as force field based, empirical, knowledge based, and descriptor based scoring functions is given along with the exemplary implementations of these scoring functions. By replacing test and trial based ligand screening with structure based virtual screening, molecular docking has helped in shortening the duration of novel drug discovery up to some extent. However, the developments made in the field of drug discovery are assisted by the advances in the techniques of molecular docking, but there is strong need of enrichment in the techniques, especially in scoring functions, to tackle the inbound problems of de novo drug discovery.

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>

scholarly journals Prospect into therapeutic potentials of Moringa oleifera phytocompounds against cancer upsurge: de novo synthesis of test compounds, molecular docking, and ADMET studies

2021 ◽  
Vol 45 (1) ◽  
Author(s):  
P. M. Aja ◽  
P. C. Agu ◽  
E. M. Ezeh ◽  
J. N. Awoke ◽  
H. A. Ogwoni ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Drug Discovery ◽  
Cancer Chemotherapy ◽  
Moringa Oleifera ◽  
De Novo ◽  
Gas Chromatography Mass Spectrometry ◽  
Molecular Docking Study ◽  
Cancer Proliferation ◽  
Target Proteins ◽  
Potential Inhibitors

Abstract Background Cancer chemotherapy is difficult because current medications for the treatment of cancer have been linked to a slew of side effects; as a result, researchers are tasked with developing greener cancer chemotherapies. Moringa oleifera has been reported with several bioactive compounds which confirm its application for various ailments by traditional practitioners. In this study, we aim to prospect the therapeutic potentials of M. oleifera phytocompounds against cancer proliferation as a step towards drug discovery using a computational approach. Target proteins: dihydrofolate reductase (DHFR) and B-Cell Lymphoid-2 (BCL-2), were retrieved from the RCSB PDB web server. Sixteen and five phytocompounds previously reported in M. oleifera leaves (ML) and seeds (MS), respectively, by gas chromatography–mass spectrometry were synthesized and used in the molecular docking study. For accurate prediction of binding sites of the target proteins; standard inhibitors, Methotrexate (MTX) for DHFR, and Venetoclax (VTC) for BCL-2, were docked together with the test compounds. We further predicted the ADMET profile of the potential inhibitors for an insight into their chance of success as candidates in drug discovery. Results Results for the binding affinities, docking poses, and the interactions showed that ML2, ML4-6, ML8-15, and MS1-5 are potential inhibitors of DHFR and BCL-2, respectively. In the ADMET profile, ML2 and ML4 showed the best drug-likeness by non-violation of Lipski Rule of Five. ML4-6, ML8, ML11, ML14-15, and MS1, MS3-5 exhibit high GI absorption; ML2, ML4-6, ML8, MS1, and MS5 are blood–brain barrier permeants. ML2, ML4, ML9, ML13, and MS2 do not interfere with any of the CYP450 isoforms. The toxicity profile showed that all the potential inhibitors are non-carcinogenic and non-hERG I (human ether-a-go-go related gene I) inhibitors. ML4, ML11, and MS4 are hepatotoxic and ML7, ML10, and MS4 are hERG II inhibitors. A plethora of insights on the toxic endpoints and lethal concentration values showed that ML5, ML13, and MS2 are comparatively less lethal than other potential inhibitors. Conclusion This study has demonstrated that M. oleifera phytocompounds are potential inhibitors of the disease proteins involved in cancer proliferation, thus, an invaluable step toward the discovery of cancer chemotherapy with lesser limitations.

scholarly journals Pengaruh Minimisasi Energi MMFF94 dengan MarvinSketch dan Open Babel PyRx pada Penambatan Molekular Turunan Oksindola Tersubstitusi

ALCHEMY ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 33-40
Author(s):  
Atika Umi Hanif ◽  
Prima Agusti Lukis ◽  
Arif Fadlan
Keyword(s):  
Molecular Docking ◽  
Drug Discovery ◽  
Force Field ◽  
Binding Affinity ◽  
Energy Minimization ◽  
In Silico ◽  
Initial State ◽  
Molecular Force ◽  
Molecular Force Field ◽  
Docking Energy

 In silico technique is widely used for drug discovery because it can predict the conformation of ligands in protein macromolecules and it can calculate the binding affinity. The energy minimization is carried out to make the ligand more stable near the initial state during molecular docking process. The Merck Molecular Force Field (MMFF94) is one type of energy minimization process often used in organic compounds. The molecular docking of substituted oxindole derivatives on indoleamine macromolecules 2,3-dioxygenase (IDO-1, PDB: 2D0T) by MMFF94 minimization operated by MarvinSketch and Open Babel in PyRx showed different results. The binding affinity energy obtained was also quite different, but the ligands have the same conformation and bind the same residue with slightly different bond distances. Keywords: Molecular docking, energy minimization, substituted oxindole, Merck Molecular Force Field 94  Teknik in silico banyak digunakan untuk penemuan senyawa obat karena dapat memprediksi konformasi suatu ligan dalam makromolekul protein dan mampu menghitung nilai afinitas ikatan. Proses minimisasi energi dilakukan untuk menjadikan ligan lebih stabil mendekati keadaan awal selama penambatan molekular berlangsung. Merck Molecular Force Field (MMFF94) adalah salah satu jenis persamaan minimisasi energi yang sering digunakan pada senyawa organik. Hasil pengujian pengaruh minimisasi energi dengan MMFF94 menggunakan program MarvinSketch dan Open Babel dalam PyRx pada turunan oksindola tersubstitusi alkil terhadap makromolekul 2,3-dioxygenase indoleamine (IDO-1, PDB: 2D0T) menunjukkan hasil dengan nilai yang berbeda. Energi afinitas ikatan yang didapatkan juga cukup berbeda, namun ligan memiliki konformasi yang sama dan mengikat residu yang sama dengan jarak ikatan yang sedikit berbeda. Kata kunci: Penambatan molekular, minimisasi energi, oksindola tersubstitusi, Merck Molecular Force Field 94

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.

Protein-Ligand Docking Methodologies and Its Application in Drug Discovery

2016 ◽  
pp. 196-219
Author(s):  
Sanchaita Rajkhowa ◽  
Ramesh C. Deka
Keyword(s):  
Molecular Docking ◽  
Drug Discovery ◽  
Drug Design ◽  
High Throughput Screening ◽  
Docking Studies ◽  
Stable Complex ◽  
Scoring Functions ◽  
Binding Modes ◽  
Structure Based Drug Design ◽  
Modern Drug

Molecular docking is a key tool in structural biology and computer-assisted drug design. Molecular docking is a method which predicts the preferred orientation of a ligand when bound in an active site to form a stable complex. It is the most common method used as a structure-based drug design. Here, the authors intend to discuss the various types of docking methods and their development and applications in modern drug discovery. The important basic theories such as sampling algorithm and scoring functions have been discussed briefly. The performances of the different available docking software have also been discussed. This chapter also includes some application examples of docking studies in modern drug discovery such as targeted drug delivery using carbon nanotubes, docking of nucleic acids to find the binding modes and a comparative study between high-throughput screening and structure-based virtual screening.

scholarly journals COMPUTER AIDED DRUG DESIGN: AN OVERVIEW

2018 ◽  
Vol 8 (5) ◽  
pp. 504-509 ◽  
Author(s):  
Surabhi Surabhi ◽  
BK Singh
Keyword(s):  
Molecular Docking ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Drug Design ◽  
Computer Aided Drug Design ◽  
Structure Based Drug Design ◽  
Drug Discovery And Development ◽  
Research Areas ◽  
Computer Aided ◽  
Pharmaceutical Industries

Discovery and development of a new drug is generally known as a very complex process which takes a lot of time and resources. So now a day’s computer aided drug design approaches are used very widely to increase the efficiency of the drug discovery and development course. Various approaches of CADD are evaluated as promising techniques according to their need, in between all these structure-based drug design and ligand-based drug design approaches are known as very efficient and powerful techniques in drug discovery and development. These both methods can be applied with molecular docking to virtual screening for lead identification and optimization. In the recent times computational tools are widely used in pharmaceutical industries and research areas to improve effectiveness and efficacy of drug discovery and development pipeline. In this article we give an overview of computational approaches, which is inventive process of finding novel leads and aid in the process of drug discovery and development research. Keywords: computer aided drug discovery, structure-based drug design, ligand-based drug design, virtual screening and molecular docking

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.

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