Drug-drug interaction is an important method in bioinformatics, especially in the case of treatment of serious diseases such as cancer. It tells us about the drug effect modified by another drug. In this article, we summarize a few useful tools for drug-drug interaction prediction.
There are quite a few predictors available for drug-drug interactions. We have discussed below the most widely used software including their availability.
NDD is a standalone tool for drug-drug interaction prediction based on neural networks . It first calculates the drug similarities and Gaussian Interaction Profile similarities for each drug pair. After that, it selects a subset of similarities based on more information and less redundancy. Later, it creates a single matrix by integrating all selected matrices. Finally, for each drug pair, the two corresponding rows of the integrated matrix are fed to a two-layer neural network for classification .
The software is freely accessible at Github.
DPDDI is another software for drug-drug interaction prediction . It predicts drug-drug interactions by extracting the network structure features of drugs from the network with a graph-convolutional network and a deep neural network as a predictor . It does not consider the drug properties such as drugs’ biological and chemical properties. Since the software is a Tensorflow implementation of the DPDDI model, therefore, it requires TensorFlow (1.0 or later), python 2.7, networkx, scikit-learn, and scipy to be installed.
The software is freely available to download at Github.
- Rohani, N., Eslahchi, C. (2019). Drug-Drug Interaction Predicting by Neural Network Using Integrated Similarity. Sci Rep 9, 13645.
- Feng, YH., Zhang, SW. & Shi, JY. (2020). DPDDI: a deep predictor for drug-drug interactions. BMC Bioinformatics 21, 419.
CNN-DDI: A drug-drug interaction prediction method using convolutional neural networks
Drug-drug interaction (DDI) prediction is gaining much more interest in the drug development process and disease diagnosis. Recently, a novel algorithm is proposed based on convolutional neural networks (CNN). (more…)
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Prepare receptor and ligand files for docking using Python scripts
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Virtual Screening Methodology for Structure-based Drug Designing
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Beginner’s Guide for Docking using Autodock Vina
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Vina output analysis using Discovery Studio visualizer
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Raccoon2: A GUI facilitating virtual screenings with Autodock and Autodock Vina
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How to install Raccoon plugin on Ubuntu for virtual screening using Autodock?
As mentioned in our previous articles, Autodock Vina  is a very useful bioinformatics tool for molecular docking and provides various options for site-specific docking and blind docking. But it seemed to be challenging to perform virtual screening using Autodock. Recently, Autodock has developed a plugin known as ‘Raccoon’ , which serves for this purpose. (more…)
Video Tutorial: How to perform docking using Autodock Vina
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A review on the effects of CMPF binding with Human Serum Albumin
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Systems pharmacology and drug development
Systems pharmacology is an emerging area in the field of medicinal chemistry and pharmacology which utilizes systems network to understand drug action at the organ and organism level. It applies the computational and experimental systems biology approaches to pharmacology, which includes network analyzes at multiple biological organization levels facilitating the understanding of both therapeutic and adverse effects of the drugs. Nearly a decade ago, the term systems pharmacology was used to define the drug action in a specific organ system such as reproductive pharmacology , but to date, it has been expanded to different organ and organism levels . (more…)