In silico Methods for Identification of Potential Therapeutic Targets

AbstractAt the initial stage of drug discovery, identifying novel targets with maximal efficacy and minimal side effects can improve the success rate and portfolio value of drug discovery projects while simultaneously reducing cycle time and cost. However, harnessing the full potential of big data to narrow the range of plausible targets through existing computational methods remains a key issue in this field. This paper reviews two categories of in silico methods—comparative genomics and network-based methods—for finding potential therapeutic targets among cellular functions based on understanding their related biological processes. In addition to describing the principles, databases, software, and applications, we discuss some recent studies and prospects of the methods. While comparative genomics is mostly applied to infectious diseases, network-based methods can be applied to infectious and non-infectious diseases. Nonetheless, the methods often complement each other in their advantages and disadvantages. The information reported here guides toward improving the application of big data-driven computational methods for therapeutic target discovery. Graphical abstract

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Computational methods on food contact chemicals: Big data and in silico screening on nuclear receptors family

Chemosphere ◽

10.1016/j.chemosphere.2021.133422 ◽

2021 ◽

pp. 133422

Author(s):

Pietro Cozzini ◽

Francesca Cavaliere ◽

Giulia Spaggiari ◽

Gianluca Morelli ◽

Marco Riani

Keyword(s):

Big Data ◽

Nuclear Receptors ◽

Computational Methods ◽

In Silico ◽

In Silico Screening ◽

Food Contact ◽

Contact Chemicals

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Chemogenomic Approaches for Revealing Drug target Interactions in Drug Discovery

Current Genomics ◽

10.2174/1389202922666210920125800 ◽

2021 ◽

Vol 22 ◽

Author(s):

Harshita Bhargava ◽

Amita Sharma ◽

Prashanth Suravajhala

Keyword(s):

Drug Discovery ◽

In Silico ◽

Drug Target ◽

Drug Repositioning ◽

Personalised Medicine ◽

Classification Problem ◽

Target Interaction ◽

Advantages And Disadvantages

: The drug discovery process has been a crucial and cost-intensive process. This cost is not only monetary but also involves risks, time, and labour that are incurred while introducing a drug in the market. In order to reduce this cost and the risks associated with the drugs that may result in severe side effects, the in silico methods have gained popularity in recent years. These methods have had a significant impact on not only drug discovery but also the related areas such as drug repositioning, drug-target interaction prediction, drug side effect prediction, personalised medicine, etc. Amongst these research areas predicting interactions between drugs and targets forms the basis for drug discovery. The availability of big data in the form of bioinformatics, genetic databases, along with computational methods, have further supported data-driven decision-making. The results obtained through these methods may be further validated using in vitro or in vivo experiments. This validation step can further justify the predictions resulting from in silico approaches, further increasing the accuracy of the overall result in subsequent stages. A variety of approaches are used in predicting drug-target interactions, including ligand-based, molecular docking based and chemogenomic-based approaches. This paper discusses the chemogenomic methods, considering drug target interaction as a classification problem on whether or not an interaction between a particular drug and target would serve as a basis for understanding drug discovery/drug repositioning. We present the advantages and disadvantages associated with their application.

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In Silico Prediction Tool for Drug-likeness of Compounds based on Ligand Based Screening

International Journal of Research in Pharmaceutical Sciences ◽

10.26452/ijrps.v11i4.3310 ◽

2020 ◽

Vol 11 (4) ◽

pp. 6273-6281

Author(s):

Ani R ◽

Anand P S ◽

Sreenath B ◽

Deepa O S

Keyword(s):

Drug Discovery ◽

In Silico ◽

Machine Learning Algorithms ◽

Prediction Tool ◽

Lipinski’S Rule ◽

Advantages And Disadvantages ◽

Online Tools ◽

Long Time ◽

To Come

Drug Likeness prediction is a time-consuming and tedious process. An in-vitro method the drug development takes a long time to come to market. The failure rate is also another one to think about in this method. There are many in-silico methods currently available and developing to help the drug discovery and development process. Many online tools are available for predicting and classifying a drug after analyzing the drug-likeness properties of compounds. But most tools have their advantages and disadvantages. In this study, a tool is developed to predict the drug-likeness of compounds given as input to this software. This may help the chemists in analyzing a compound before actually preparing a compound for the drug discovery process. The tool includes both descriptor-based calculation and fingerprint-based calculation of the particular compounds. The descriptor-calculation also includes a set of rules and filters like Lipinski’s rule, Ghose filter, Veber filter and BBB likeness. The previous studies proved that the fingerprint-based prediction is more accurate than descriptor-based prediction. So, in the current study, the drug-likeness prediction tool incorporated the molecular descriptors and fingerprint-based calculations based on five different fingerprint types. The current study incorporated five different machine learning algorithms for prediction of drug-likeness and selected the algorithm, which has a high accuracy rate. When a chemist inputs a particular compound in SMILES format, the drug-likeness prediction tool predicts whether the given candidate compound is drug or non-drug.

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In silico synteny based comparative genomics approach for identification and characterization of novel therapeutic targets in Chlamydophila pneumoniae

Bioinformation ◽

10.6026/97320630009506 ◽

2013 ◽

Vol 9 (10) ◽

pp. 506-510 ◽

Cited By ~ 3

Author(s):

Bilachi S Ravindranath ◽

◽

Venkatappa Krishnamurthy ◽

Venkatarangaiah Krishna ◽

Sunil Kumar

Keyword(s):

Comparative Genomics ◽

In Silico ◽

Therapeutic Targets ◽

Chlamydophila Pneumoniae ◽

Identification And Characterization ◽

Novel Therapeutic

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A Skyline-Based Decision Boundary Estimation Method for Binominal Classification in Big Data

Computation ◽

10.3390/computation8030080 ◽

2020 ◽

Vol 8 (3) ◽

pp. 80

Author(s):

Christos Kalyvas ◽

Manolis Maragoudakis

Keyword(s):

Big Data ◽

Estimation Method ◽

Current Approach ◽

Large Datasets ◽

Support Vector ◽

Full Potential ◽

Decision Boundary ◽

K Nearest Neighbor ◽

Advantages And Disadvantages ◽

Very Large Datasets

One of the most common tasks nowadays in big data environments is the need to classify large amounts of data. There are numerous classification models designed to perform best in different environments and datasets, each with its advantages and disadvantages. However, when dealing with big data, their performance is significantly degraded because they are not designed—or even capable—of handling very large datasets. The current approach is based on a novel proposal of exploiting the dynamics of skyline queries to efficiently identify the decision boundary and classify big data. A comparison against the popular k-nearest neighbor (k-NN), support vector machines (SVM) and naïve Bayes classification algorithms shows that the proposed method is faster than the k-NN and the SVM. The novelty of this method is based on the fact that only a small number of computations are needed in order to make a prediction, while its full potential is revealed in very large datasets.

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Drugs Polypharmacology by In Silico Methods: New Opportunities in Drug Discovery

Current Pharmaceutical Design ◽

10.2174/1381612822666160224142323 ◽

2016 ◽

Vol 22 (21) ◽

pp. 3073-3081 ◽

Cited By ~ 6

Author(s):

Antonino Lauria ◽

Riccardo Bonsignore ◽

Roberta Bartolotta ◽

Ugo Perricone ◽

Annamaria Martorana ◽

...

Keyword(s):

Drug Discovery ◽

In Silico ◽

In Silico Methods

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Advancing Computer-Aided Drug Discovery (ACADD): In-Silico Approach towards Nuclear Receptors by Big Data

Journal of Pharmaceutical Research International ◽

10.9734/jpri/2021/v33i30a31612 ◽

2021 ◽

pp. 40-46

Author(s):

K. Palaniammal ◽

M. Saravana Roentgen Mani ◽

R. Mohan Kumar

Keyword(s):

Big Data ◽

Biological Activity ◽

Drug Discovery ◽

Binding Energy ◽

Nuclear Receptors ◽

In Silico ◽

Structural Parameters ◽

Receptor Interaction ◽

Drug Discovery And Development ◽

Computer Aided

The progression of drug discovery and development is time consuming and costly. Advancing Computer-aided drug discovery (ACADD) is an effective tool in reducing the time and cost of research and development. This study deals with the evaluation of the nuclear receptors for the in-silico biological activity using ligand betulinic acid and dexamethasone. Docking results showed that binding energy was -74.190 kcal/mol when compared with that of the standard (-51.551 kcal/mol). Interaction energy -44.16 & -25.14 kcal/mol) of the ligands also coincide with the binding energy. These ligands have shown the best ligand-receptor interaction based on their structural parameters.

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