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Review
. 2019 Nov 20:7:782.
doi: 10.3389/fchem.2019.00782. eCollection 2019.

Comparison Study of Computational Prediction Tools for Drug-Target Binding Affinities

Maha Thafar  1   2 , Arwa Bin Raies  1 , Somayah Albaradei  1   3 , Magbubah Essack  1 , Vladimir B Bajic  1
Affiliations
Review

Comparison Study of Computational Prediction Tools for Drug-Target Binding Affinities

Maha Thafar et al. Front Chem. .

Abstract

The drug development is generally arduous, costly, and success rates are low. Thus, the identification of drug-target interactions (DTIs) has become a crucial step in early stages of drug discovery. Consequently, developing computational approaches capable of identifying potential DTIs with minimum error rate are increasingly being pursued. These computational approaches aim to narrow down the search space for novel DTIs and shed light on drug functioning context. Most methods developed to date use binary classification to predict if the interaction between a drug and its target exists or not. However, it is more informative but also more challenging to predict the strength of the binding between a drug and its target. If that strength is not sufficiently strong, such DTI may not be useful. Therefore, the methods developed to predict drug-target binding affinities (DTBA) are of great value. In this study, we provide a comprehensive overview of the existing methods that predict DTBA. We focus on the methods developed using artificial intelligence (AI), machine learning (ML), and deep learning (DL) approaches, as well as related benchmark datasets and databases. Furthermore, guidance and recommendations are provided that cover the gaps and directions of the upcoming work in this research area. To the best of our knowledge, this is the first comprehensive comparison analysis of tools focused on DTBA with reference to AI/ML/DL.

Keywords: artificial intelligence; bioinformatics; deep learning; drug repurposing; drug-target binding affinity; drug-target interaction; information integration; machine learning.

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Figures

Figure 1
Figure 1
An overview of the different types of computational methods developed to predict drug-target interactions (DTIs) and drug-target binding affinity (DTBA) categories.
Figure 2
Figure 2
A hypothetical example of a binding curve for ligand 1 and ligand 2. The x-axis shows the concentration of the ligand, and the y-axis shows the percentage of available binding sites (Θ) in a protein that is occupied by the ligand.
Figure 3
Figure 3
Relationship between concentration of inhibitors and enzymes activity.
Figure 4
Figure 4
Flowchart of the general framework of deep learning (DL) models used for drug-target binding affinity (DTBA) prediction.
See this image and copyright information in PMC

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