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

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.

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An Overview of Computational Approaches in Structure Based Drug Design

Nepal Journal of Biotechnology ◽

10.3126/njb.v2i1.5680 ◽

1970 ◽

Vol 2 (1) ◽

pp. 53-61 ◽

Cited By ~ 1

Author(s):

Dipali Singh ◽

Anushree Tripathi ◽

Gautam Kumar

Keyword(s):

Drug Design ◽

Computer Aided Drug Design ◽

Structure Based Drug Design ◽

Computational Approaches ◽

Ras Protein ◽

Design Structure ◽

Drug Lead ◽

Computer Aided ◽

Drug Designing

Drug design is a costly and difficult process. Drug must fulfill several criteria of being active, nontoxic and bioavailable. The conventional way of synthesizing drugs is a monotonous process. But computer aided drug design is a proficient way to overcome the tedious process of conventional method. Drugs can be designed computationally by structure or target based drug designing (SBDD). This review summarizes the methods of structure based drug design, usage of related softwares and a case study that explores to find a suitable drug (lead) molecule for the mutated state of H-Ras protein in order to prevent complex formation with Raf protein.Keywords: computer aided drug design; structure based drug design; Ras-proteinDOI: http://dx.doi.org/10.3126/njb.v2i1.5680Nepal Journal of Biotechnology Jan.2012, Vol.2(1): 53-61

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Towards structure-based protein drug design

Biochemical Society Transactions ◽

10.1042/bst0391382 ◽

2011 ◽

Vol 39 (5) ◽

pp. 1382-1386 ◽

Cited By ~ 21

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.

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A Structure-Based Drug Discovery Paradigm

International Journal of Molecular Sciences ◽

10.3390/ijms20112783 ◽

2019 ◽

Vol 20 (11) ◽

pp. 2783 ◽

Cited By ~ 50

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.

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Improving the Accuracy of Protein-Ligand Binding Affinity Prediction by Deep Learning Models: Benchmark and Model

10.26434/chemrxiv.9866912 ◽

2019 ◽

Author(s):

Mohammad Rezaei ◽

Yanjun Li ◽

Xiaolin Li ◽

Chenglong Li

Keyword(s):

Deep Learning ◽

Drug Design ◽

Binding Affinity ◽

Benchmark Dataset ◽

Rational Drug Design ◽

Learning Models ◽

Structure Based Drug Design ◽

Binding Affinity Prediction ◽

Affinity Prediction ◽

Rational Drug

Introduction: The ability to discriminate among ligands binding to the same protein target in terms of their relative binding affinity lies at the heart of structure-based drug design. Any improvement in the accuracy and reliability of binding affinity prediction methods decreases the discrepancy between experimental and computational results. Objectives: The primary objectives were to find the most relevant features affecting binding affinity prediction, least use of manual feature engineering, and improving the reliability of binding affinity prediction using efficient deep learning models by tuning the model hyperparameters. Methods: The binding site of target proteins was represented as a grid box around their bound ligand. Both binary and distance-dependent occupancies were examined for how an atom affects its neighbor voxels in this grid. A combination of different features including ANOLEA, ligand elements, and Arpeggio atom types were used to represent the input. An efficient convolutional neural network (CNN) architecture, DeepAtom, was developed, trained and tested on the PDBbind v2016 dataset. Additionally an extended benchmark dataset was compiled to train and evaluate the models. Results: The best DeepAtom model showed an improved accuracy in the binding affinity prediction on PDBbind core subset (Pearson’s R=0.83) and is better than the recent state-of-the-art models in this field. In addition when the DeepAtom model was trained on our proposed benchmark dataset, it yields higher correlation compared to the baseline which confirms the value of our model. Conclusions: The promising results for the predicted binding affinities is expected to pave the way for embedding deep learning models in virtual screening and rational drug design fields.

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Current Scenario in Structure and Ligand-Based Drug Design on Anti-colon Cancer Drugs

Current Pharmaceutical Design ◽

10.2174/1381612824666181114114513 ◽

2019 ◽

Vol 24 (32) ◽

pp. 3829-3841 ◽

Cited By ~ 1

Author(s):

Lakshmanan Loganathan ◽

Karthikeyan Muthusamy

Keyword(s):

Drug Discovery ◽

Drug Design ◽

Anticancer Agent ◽

Computational Power ◽

Cancer Drugs ◽

Recent Advances ◽

Anti Cancer ◽

Drug Designing ◽

Current Scenario ◽

Molecular Modeling Studies

Worldwide, colorectal cancer takes up the third position in commonly detected cancer and fourth in cancer mortality. Recent progress in molecular modeling studies has led to significant success in drug discovery using structure and ligand-based methods. This study highlights aspects of the anticancer drug design. The structure and ligand-based drug design are discussed to investigate the molecular and quantum mechanics in anti-cancer drugs. Recent advances in anticancer agent identification driven by structural and molecular insights are presented. As a result, the recent advances in the field and the current scenario in drug designing of cancer drugs are discussed. This review provides information on how cancer drugs were formulated and identified using computational power by the drug discovery society.

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Use of Molecular Dynamics Simulations in Structure-Based Drug Discovery

Current Pharmaceutical Design ◽

10.2174/1381612825666190903153043 ◽

2019 ◽

Vol 25 (31) ◽

pp. 3339-3349 ◽

Cited By ~ 6

Author(s):

Indrani Bera ◽

Pavan V. Payghan

Keyword(s):

Molecular Dynamics ◽

Drug Discovery ◽

Molecular Dynamics Simulations ◽

Drug Target ◽

Structural Information ◽

Drug Binding ◽

Structure Based Drug Design ◽

Drug Designing ◽

Target Binding ◽

Dynamics Simulations

Background: Traditional drug discovery is a lengthy process which involves a huge amount of resources. Modern-day drug discovers various multidisciplinary approaches amongst which, computational ligand and structure-based drug designing methods contribute significantly. Structure-based drug designing techniques require the knowledge of structural information of drug target and drug-target complexes. Proper understanding of drug-target binding requires the flexibility of both ligand and receptor to be incorporated. Molecular docking refers to the static picture of the drug-target complex(es). Molecular dynamics, on the other hand, introduces flexibility to understand the drug binding process. Objective: The aim of the present study is to provide a systematic review on the usage of molecular dynamics simulations to aid the process of structure-based drug design. Method: This review discussed findings from various research articles and review papers on the use of molecular dynamics in drug discovery. All efforts highlight the practical grounds for which molecular dynamics simulations are used in drug designing program. In summary, various aspects of the use of molecular dynamics simulations that underline the basis of studying drug-target complexes were thoroughly explained. Results: This review is the result of reviewing more than a hundred papers. It summarizes various problems that use molecular dynamics simulations. Conclusion: The findings of this review highlight how molecular dynamics simulations have been successfully implemented to study the structure-function details of specific drug-target complexes. It also identifies the key areas such as stability of drug-target complexes, ligand binding kinetics and identification of allosteric sites which have been elucidated using molecular dynamics simulations.

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Re-Designing Hazardous Chemicals with Target-Specific Toxicities by Learning from Structure-Based Drug Design

10.26434/chemrxiv.7001054 ◽

2018 ◽

Author(s):

Traci Clymer ◽

Vanessa Vargas ◽

Eric Corcoran ◽

Robin Kleinberg ◽

Jakub Kostal

Keyword(s):

Drug Discovery ◽

Drug Design ◽

Adverse Effects ◽

Environmental Health ◽

In Silico ◽

Adverse Outcomes ◽

Design Stage ◽

Hazardous Chemicals ◽

Structure Based Drug Design ◽

Computational Drug Discovery

Chemicals are the basis of our society and economy, yet many existing chemicals are known to have unintended adverse effects on human and environmental health. Testing all existing and new chemicals on animals is both economically and ethically unfeasible. In this paper, a new in silico framework is presented that affords redesign of existing hazardous chemicals in commerce based on specific molecular initiating events in their adverse outcomes pathways. Our approach is based on a successful methodology implemented in computational drug discovery, and promises to dramatically lower costs associated with new chemical development by synergistically addressing chemical function and safety at the design stage.

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Protein-Ligand Docking Methodologies and Its Application in Drug Discovery

Methods and Algorithms for Molecular Docking-Based Drug Design and Discovery - Advances in Medical Technologies and Clinical Practice ◽

10.4018/978-1-5225-0115-2.ch008 ◽

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.

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