Protein Docking and Drug Design

2018 ◽  
pp. 207-241
Author(s):  
Aditi Gangopadhyay ◽  
Hirak Jyoti Chakraborty ◽  
Abhijit Datta
Keyword(s):  
Drug Design ◽  
De Novo ◽  
Low Cost ◽  
Protein Docking ◽  
Rapid Screening ◽  
Structure Based Drug Design ◽  
Attrition Rates ◽  
Time Investment ◽  
Novel Drug ◽  
Druggable Targets

Protein docking is integral to structure-based drug design and molecular biology. The recent surge of big data in biology, the demand for personalised medicines, evolving pathogens and increasing lifestyle-associated risks, asks for smart, robust, low-cost and high-throughput drug design. Computer-aided drug design techniques allow rapid screening of ultra-large chemical libraries within minutes. This is immensely necessary to the drug discovery pipeline, which is presently burdened with high attrition rates, failures, huge capital and time investment. With increasing drug resistance and difficult druggable targets, there is a growing need for novel drug scaffolds which is partly satisfied by fragment based drug design and de novo methods. The chapter discusses various aspects of protein docking and emphasises on its application in drug design.

Protein Docking and Drug Design

Biotechnology ◽  
2019 ◽  
pp. 889-922
Author(s):  
Aditi Gangopadhyay ◽  
Hirak Jyoti Chakraborty ◽  
Abhijit Datta
Keyword(s):  
Drug Design ◽  
De Novo ◽  
Low Cost ◽  
Protein Docking ◽  
Rapid Screening ◽  
Structure Based Drug Design ◽  
Attrition Rates ◽  
Time Investment ◽  
Novel Drug ◽  
Druggable Targets

Protein docking is integral to structure-based drug design and molecular biology. The recent surge of big data in biology, the demand for personalised medicines, evolving pathogens and increasing lifestyle-associated risks, asks for smart, robust, low-cost and high-throughput drug design. Computer-aided drug design techniques allow rapid screening of ultra-large chemical libraries within minutes. This is immensely necessary to the drug discovery pipeline, which is presently burdened with high attrition rates, failures, huge capital and time investment. With increasing drug resistance and difficult druggable targets, there is a growing need for novel drug scaffolds which is partly satisfied by fragment based drug design and de novo methods. The chapter discusses various aspects of protein docking and emphasises on its application in drug design.

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.

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.

Multi-objective Genetic Algorithm for De Novo Drug Design

2020 ◽  
Vol 16 ◽  
Author(s):  
R. Vasundhara Devi ◽  
S. Siva Sathya ◽  
S. Mohane Coumar
Keyword(s):  
Genetic Algorithm ◽  
Drug Design ◽  
De Novo ◽  
New Drugs ◽  
Weighted Sum ◽  
De Novo Drug Design ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Fragment Library ◽  
Novel Drug

Background: Genetic algorithm being a famous evolutionary algorithm, multiple objectives of drug design are solved using weighted sum approach. Objective: To design a computational tool for the de novo design of novel drug-like molecules to aid in the discovery of new drugs using Genetic algorithm and chemical fragment library and reference molecules. Method: Multi-objective optimization using genetic algorithm and weighted sum approach. Results: The drug-like molecules for the reference molecules such as Lidocaine, Furano-pyrimidine, Imatinib, Atorvastatin and Glipizide. Conclusion: The performance of the MOGADdrug tool is evaluated using 5 reference molecules and the designed molecules are compared with Zinc and Pubchem databases along with their docking investigations.

scholarly journals MOLECULAR DOCKING: AN EXPLANATORY APPROACH IN STRUCTURE-BASED DRUG DESIGNING AND DISCOVERY

2021 ◽  
pp. 6-12
Author(s):  
ADITI SHARMA ◽  
SALONI KUNWAR ◽  
VAISHALI ◽  
VAISHALI AGARWAL ◽  
CHHAYA SINGH ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Drug Design ◽  
De Novo ◽  
Scoring Function ◽  
Binding Mode ◽  
De Novo Drug Design ◽  
Structure Based Drug Design ◽  
Binding Characteristics ◽  
Stable Product ◽  
Novel Concept

Molecular docking is a modeling tool of Bioinformatics which includes two or more molecules which interact to provide a stable product in the form of a complex. Molecular docking is helpful in predicting the 3-d structure of a complex which depends on the binding characteristics of Ligand and target. Also, it is a structure-based virtual screening (SBVS) utilized to keep the 3-d structures of small molecule which are generated by computers into a target structure in various types of conformations, positions and orientations. This molecular docking has come out to be a novel concept with various types of advantages. It behaves as a highly exploring domain due to its significant structure-based drug design (SBDD), Assessment of Biochemical pathways, Lead Optimization and in De Novo drug design. In spite of all potential approaches, there are certain challenges which are-scoring function (differentiate the true binding mode), ligand chemistry (tautomerism and ionization) and receptor flexibility (single conformation of rigid receptor). The area of computer-aided drug design and discovery (CADDD) has achieved large favorable outcomes in the past few years. CADD has been adopted by various big pharmaceutical companies for leading discoveries of drugs. Many researchers have worked in order to examine different docking algorithms and to predict molecules' active site. Hence, this Review article depicts the whole sole of Molecular Docking.

De Novo Structure-Based Drug Design Using Deep Learning

2021 ◽  
Author(s):  
Sowmya Ramaswamy Krishnan ◽  
Navneet Bung ◽  
Sarveswara Rao Vangala ◽  
Rajgopal Srinivasan ◽  
Gopalakrishnan Bulusu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Drug Design ◽  
De Novo ◽  
Structure Based Drug Design

scholarly journals Target2DeNovoDrug : a novel programmatic tool for deep learning based de novo drug design for a target of interest

2020 ◽  
Author(s):  
Rafal Madaj ◽  
Ben Geoffrey A S ◽  
Pavan Preetham Valluri ◽  
Akhil Sanker
Keyword(s):  
Deep Learning ◽  
Drug Design ◽  
In Silico ◽  
High Performance ◽  
Data Science ◽  
De Novo ◽  
Computationally Efficient ◽  
De Novo Drug Design ◽  
Necrosis Factor Alpha ◽  
Structure Based Drug Design

The on-going data-science and AI revolution offers researchers with fresh set of tools to approach structure-based drug design problems in the computer aided drug design space. A novel programmatic tool that can be used in aid of in silico-deep learning based de novo drug design for any target of interest has been reported. Once the user specifies the target of interest, the programmatic workflow of the tool generates novel SMILES of compounds that are likely to be active against the target. The tool also performs a computationally efficient In-Silico modeling of the target and the newly generated compounds and stores the results in the working folder of the user. A demonstrated use of the tool has been shown with the target signatures of Tumor Necrosis Factor-Alpha, an important therapeutic target in the case of anti-inflammatory treatment. The future scope of the tool involves, running the tool on a High Performance Cluster for all known target signatures to generate data that will be useful to drive AI and Big data driven drug discovery. The code is hosted, maintained and supported at the GitHub repository given in link below https://github.com/bengeof/Target2DeNovoDrug

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.

Remodelling structure-based drug design using machine learning

2021 ◽  
Author(s):  
Shubhankar Dutta ◽  
Kakoli Bose
Keyword(s):  
Machine Learning ◽  
Drug Design ◽  
Model Building ◽  
Leading Edge ◽  
Training Data ◽  
Computational Techniques ◽  
Structure Based Drug Design ◽  
Rational Drug ◽  
Wide Range ◽  
Novel Drug

To keep up with the pace of rapid discoveries in biomedicine, a plethora of research endeavors had been directed toward Rational Drug Development that slowly gave way to Structure-Based Drug Design (SBDD). In the past few decades, SBDD played a stupendous role in identification of novel drug-like molecules that are capable of altering the structures and/or functions of the target macromolecules involved in different disease pathways and networks. Unfortunately, post-delivery drug failures due to adverse drug interactions have constrained the use of SBDD in biomedical applications. However, recent technological advancements, along with parallel surge in clinical research have led to the concomitant establishment of other powerful computational techniques such as Artificial Intelligence (AI) and Machine Learning (ML). These leading-edge tools with the ability to successfully predict side-effects of a wide range of drugs have eventually taken over the field of drug design. ML, a subset of AI, is a robust computational tool that is capable of data analysis and analytical model building with minimal human intervention. It is based on powerful algorithms that use huge sets of ‘training data’ as inputs to predict new output values, which improve iteratively through experience. In this review, along with a brief discussion on the evolution of the drug discovery process, we have focused on the methodologies pertaining to the technological advancements of machine learning. This review, with specific examples, also emphasises the tremendous contributions of ML in the field of biomedicine, while exploring possibilities for future developments.

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