scholarly journals Molecular Docking in Drug Discovery

2021 ◽  
pp. 46-58
Author(s):  
Rani T. Bhagat ◽  
Santosh R. Butle ◽  
Deepak S. Khobragade ◽  
Sagar B. Wankhede ◽  
Chandani C. Prasad ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Drug Discovery ◽  
Drug Design ◽  
De Novo ◽  
Structural Information ◽  
Screening Method ◽  
3D Structure ◽  
Protein Docking ◽  
Lead Discovery ◽  
Pharmaceutical Industries

In last few years the Computer Aided Drug Design and Discovery is many success rates. In academics and many pharmaceutical industries for drug lead discovery they adopt the Computational Drug Design. The modern era of drug discovery and development structural information play an important role. For visualization of 3D-structure of molecule different docking program are developed. The docking score is analysed by using computer-based drug design software. It is structure based virtual screening method for the orientation, conformation, position into a structure of target molecule. Ligand and Protein docking is new concept. Molecular docking method complication is optimization of lead molecule, biological pathway evaluation and de Novo drug design.

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

Towards structure-based protein drug design

2011 ◽  
Vol 39 (5) ◽  
pp. 1382-1386 ◽  
Author(s):  
Changsheng Zhang ◽  
Luhua Lai
Keyword(s):  
Drug Discovery ◽  
Drug Design ◽  
Small Molecule ◽  
Protein Interactions ◽  
De Novo ◽  
Protein Docking ◽  
Erythropoietin Receptor ◽  
Protein Drug ◽  
Protein Protein Interactions ◽  
Structure Based Drug Design

Structure-based drug design for chemical molecules has been widely used in drug discovery in the last 30 years. Many successful applications have been reported, especially in the field of virtual screening based on molecular docking. Recently, there has been much progress in fragment-based as well as de novo drug discovery. As many protein–protein interactions can be used as key targets for drug design, one of the solutions is to design protein drugs based directly on the protein complexes or the target structure. Compared with protein–ligand interactions, protein–protein interactions are more complicated and present more challenges for design. Over the last decade, both sampling efficiency and scoring accuracy of protein–protein docking have increased significantly. We have developed several strategies for structure-based protein drug design. A grafting strategy for key interaction residues has been developed and successfully applied in designing erythropoietin receptor-binding proteins. Similarly to small-molecule design, we also tested de novo protein-binder design and a virtual screen of protein binders using protein–protein docking calculations. In comparison with the development of structure-based small-molecule drug design, we believe that structure-based protein drug design has come of age.

scholarly journals A Structure-Based Drug Discovery Paradigm

2019 ◽  
Vol 20 (11) ◽  
pp. 2783 ◽  
Author(s):  
Maria Batool ◽  
Bilal Ahmad ◽  
Sangdun Choi
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Drug Discovery ◽  
Drug Design ◽  
De Novo ◽  
Learning Tools ◽  
Statistical Machine Learning ◽  
Lead Discovery ◽  
De Novo Drug Design ◽  
Structure Based Drug Design

Structure-based drug design is becoming an essential tool for faster and more cost-efficient lead discovery relative to the traditional method. Genomic, proteomic, and structural studies have provided hundreds of new targets and opportunities for future drug discovery. This situation poses a major problem: the necessity to handle the “big data” generated by combinatorial chemistry. Artificial intelligence (AI) and deep learning play a pivotal role in the analysis and systemization of larger data sets by statistical machine learning methods. Advanced AI-based sophisticated machine learning tools have a significant impact on the drug discovery process including medicinal chemistry. In this review, we focus on the currently available methods and algorithms for structure-based drug design including virtual screening and de novo drug design, with a special emphasis on AI- and deep-learning-based methods used for drug discovery.

Combating Diseases with Computational Strategies Used for Drug Design and Discovery

2019 ◽  
Vol 18 (32) ◽  
pp. 2743-2773 ◽  
Author(s):  
Farahnaz R. Makhouri ◽  
Jahan B. Ghasemi
Keyword(s):  
Drug Discovery ◽  
Drug Design ◽  
Computational Methods ◽  
Drug Targets ◽  
Structural Information ◽  
Target Identification ◽  
Lead Discovery ◽  
Human Proteins ◽  
Almost All ◽  
Discovery Pipeline

Computer-aided drug discovery (CADD) tools have provided an effective way in the drug discovery pipeline for expediting of this long process and economizing the cost of research and development. Due to the dramatic increase in the availability of human proteins as drug targets and small molecule information due to the advances in bioinformatics, cheminformatics, genomics, proteomics, and structural information, the applicability of in silico drug discovery has been extended. Computational approaches have been used at almost all stages in the drug discovery pipeline including target identification and validation, lead discovery and optimization, and pharmacokinetic and toxicity profiles prediction. As each area covers a variety of computational methods, it is unmanageable to assess comprehensively all areas of CADD applications or every aspect of an area in one review article. However, in this article, we tried to present an overview of computational methods used in almost all the areas concerned with drug design and highlight some of the recent successes.

Experimental and Computational Approaches to Improve Binding Affinity in Chemical Biology and Drug Discovery

2020 ◽  
Vol 20 (19) ◽  
pp. 1651-1660
Author(s):  
Anuraj Nayarisseri
Keyword(s):  
Drug Discovery ◽  
Drug Design ◽  
Binding Affinity ◽  
Chemical Biology ◽  
De Novo ◽  
Structure Based Drug Design ◽  
Computational Approaches ◽  
Drug Designing ◽  
Traditional Drug ◽  
Computer Based

Drug discovery is one of the most complicated processes and establishment of a single drug may require multidisciplinary attempts to design efficient and commercially viable drugs. The main purpose of drug design is to identify a chemical compound or inhibitor that can bind to an active site of a specific cavity on a target protein. The traditional drug design methods involved various experimental based approaches including random screening of chemicals found in nature or can be synthesized directly in chemical laboratories. Except for the long cycle design and time, high cost is also the major issue of concern. Modernized computer-based algorithm including structure-based drug design has accelerated the drug design and discovery process adequately. Surprisingly from the past decade remarkable progress has been made concerned with all area of drug design and discovery. CADD (Computer Aided Drug Designing) based tools shorten the conventional cycle size and also generate chemically more stable and worthy compounds and hence reduce the drug discovery cost. This special edition of editorial comprises the combination of seven research and review articles set emphasis especially on the computational approaches along with the experimental approaches using a chemical synthesizing for the binding affinity in chemical biology and discovery as a salient used in de-novo drug designing. This set of articles exfoliates the role that systems biology and the evaluation of ligand affinity in drug design and discovery for the future.

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.

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 Comprehensive Survey of Recent Drug Discovery Using Deep Learning

2021 ◽  
Vol 22 (18) ◽  
pp. 9983
Author(s):  
Jintae Kim ◽  
Sera Park ◽  
Dongbo Min ◽  
Wankyu Kim
Keyword(s):  
Deep Learning ◽  
Drug Discovery ◽  
Drug Design ◽  
De Novo ◽  
Molecular Structures ◽  
De Novo Drug Design ◽  
Related Data ◽  
Benchmark Datasets ◽  
Comprehensive Survey ◽  
Model Training

Drug discovery based on artificial intelligence has been in the spotlight recently as it significantly reduces the time and cost required for developing novel drugs. With the advancement of deep learning (DL) technology and the growth of drug-related data, numerous deep-learning-based methodologies are emerging at all steps of drug development processes. In particular, pharmaceutical chemists have faced significant issues with regard to selecting and designing potential drugs for a target of interest to enter preclinical testing. The two major challenges are prediction of interactions between drugs and druggable targets and generation of novel molecular structures suitable for a target of interest. Therefore, we reviewed recent deep-learning applications in drug–target interaction (DTI) prediction and de novo drug design. In addition, we introduce a comprehensive summary of a variety of drug and protein representations, DL models, and commonly used benchmark datasets or tools for model training and testing. Finally, we present the remaining challenges for the promising future of DL-based DTI prediction and de novo drug design.

scholarly journals REINVENT 2.0 – an AI Tool for De Novo Drug Design

2020 ◽  
Author(s):  
Thomas Blaschke ◽  
Josep Arús-Pous ◽  
Hongming Chen ◽  
Christian Margreitter ◽  
Christian Tyrchan ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Drug Design ◽  
De Novo ◽  
Chemical Space ◽  
De Novo Design ◽  
Interaction Point ◽  
De Novo Drug Design ◽  
Generative Methods ◽  
Collaborative Efforts ◽  
Scientific Collaborations

With this application note we aim to offer the community a production-ready tool for de novo design. It can be effectively applied on drug discovery projects that are striving to resolve either exploration or exploitation problems while navigating the chemical space. By releasing the code we are aiming to facilitate the research on using generative methods on drug discovery problems and to promote the collaborative efforts in this area so that it can be used as an interaction point for future scientific collaborations.

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