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TopThe DDI may essentially be split up into three distinct categories or groupings.
Basically,
- 1.
An interaction between two different drugs
- 2.
Interaction between medications and foods
- 3.
The interaction between the drug and the disease.
In this case, the pharmacokinetic (DDI) interaction is the most prevalent form of interaction, and it is the one responsible for the potentially lethal side effects. The process of taking many drugs increases the risk of making a serious mistake whenever one of those medications interacts with another. The primary goals of pharmacovigilance are to anticipate and evaluate the risks associated with the use of medications and to gain an understanding of the features of adverse drug reactions (ADRs). The pharmacokinetic and pharmacodynamic categorization systems are used for DDI prediction.
In order to discover medication groupings that are therapeutically useful for certain illnesses, a process based on networks is applied. In the human protein–protein interaction network, measuring the network-based relationship between drug targets and illness proteins revealed the presence of six distinct kinds of drug–drug–disease groupings (Cheng et al., 2019). These strategies, which were created on top of a network, are useful in elucidating the mechanism of action when a medication combination is utilised, and they also have the minimum adverse impact. CASTER is able to determine the chemical composition of the compounds, and the process that it uses to do so may be broken down into three distinct categories: sequential pattern mining, auto-encoding, and dictionary knowledge (Huang et al., 2020). LAGCN begins by integrating the known relationship between the compounds by using the graph convolution approach, then combines utilising the embedding techniques, and finally integrates the embedding layers by utilising the attention mechanism. In cases where the relationship cannot be determined, the embedding is used to determine the score. LAGCN is a method that may be used to make predictions about the associations between different chemicals (Yu et al., 2021).
Figure 1. Classes of drug interactions