Artificial intelligence-driven prediction of multiple drug interactions

Abstract

When a drug is administered to exert its efficacy, it will encounter multiple barriers and go through multiple interactions. Predicting the drug-related multiple interactions is critical for drug development and safety monitoring because it provides foundations for practical, safe compatibility and rational use of multiple drugs. With the progress of artificial intelligence (AI) technology, a variety of novel prediction methods for single interaction have emerged and shown great advantages compared to the traditional, expensive and time-consuming laboratory research. To promote the comprehensive and simultaneous predictions of multiple interactions, we systematically reviewed the application of AI in drug–drug, drug–food (excipients) and drug–microbiome interactions. We began by outlining the model methods, evaluation indicators, algorithms and databases commonly used to build models for three types of drug interactions. The models based on the metabolic enzyme P450, drug similarity and drug targets have empathized among the machine learning models of drug–drug interactions. In particular, we discussed the limitations of current approaches and identified potential areas for future research. It is anticipated the in-depth review will be helpful for the development of the next-generation of systematic prediction models for simultaneous multiple interactions.

Multimodal CNN-DDI: using multimodal CNN for drug to drug interaction associated events

“…This research 37 Predicting multiple interactions that a drug may encounter is crucial for drug development and safety. Artificial Intelligence (AI) has offered innovative methods to predict these interactions efficiently compared to traditional labor-intensive approaches.…”

Artificial intelligence in clinical pharmacology: A case study and scoping review of large language models and bioweapon potential

“…In the realm of clinical pharmacology, AI has become an indispensable tool, aiding in various tasks such as drug discovery, prediction of drug interactions, personalized medicine and pharmacovigilance. [8][9][10][11] Among the AI subfields, LLMs stand out for their ability to understand and generate humanlike text. Trained on vast amounts of textual data, LLMs such as Generative Pre-trained Transformers (GPT)-3 and GPT-4, developed by OpenAI, can generate meaningful and coherent text based on the provided input, making them a powerful tool for processing and interpreting large-scale biomedical literature and clinical records.…”

“…The inception of AI can be traced back to the mid‐20th century, but it was not until the start of the 21st century that its potential began to be fully realized, with advancements in computational power and the availability of large datasets. In the realm of clinical pharmacology, AI has become an indispensable tool, aiding in various tasks such as drug discovery, prediction of drug interactions, personalized medicine and pharmacovigilance 8–11 …”

KGE-UNIT: toward the unification of molecular interactions prediction based on knowledge graph and multi-task learning on drug discovery

“…For instance, the mechanism of action for drugs often involves specific interactions with protein targets, and the interaction between drugs and protein targets can be influenced by other drugs or proteins. This implies the presence of correlations and mutual influences between drug–drug interactions (DDIs) and drug–protein target interactions [ 19 ]. However, the current focus of research in molecular relationship prediction tasks is predominantly on individual tasks, often disregarding the potential relationships and interactions between them.…”