Drug-drug interactions (DDIs) are a significant source of morbidity and adverse drug events (ADEs), particularly in situations of polypharmacy and complex medication regimens. While rules-based softwa Show more
Drug-drug interactions (DDIs) are a significant source of morbidity and adverse drug events (ADEs), particularly in situations of polypharmacy and complex medication regimens. While rules-based software integrated in electronic health records (EHRs) has demonstrated proficiency in identifying DDIs present in medication regimens, large language model (LLM) based identification requires thorough benchmarking and performance evaluation using high-quality datasets for safe use. The purpose of this study was to develop a series of performance benchmarking experiments specifically for LLM performance in identification and management of DDIs using a specifically curated clinician-annotated dataset of clinically-relevant DDIs. Show less
Adverse drug reactions (ADRs) are harmful side effects of medications. Social media provides real-time, patient-generated data, though its unstructured format presents challenges. Natural language pro Show more
Adverse drug reactions (ADRs) are harmful side effects of medications. Social media provides real-time, patient-generated data, though its unstructured format presents challenges. Natural language processing and transfer learning offer promising solutions. Show less
Despite the vast number of enzymatic kinetic measurements reported across decades of biochemical literature, the majority of relational enzyme kinetic data—linking amino acid sequence, substrate ident Show more
Despite the vast number of enzymatic kinetic measurements reported across decades of biochemical literature, the majority of relational enzyme kinetic data—linking amino acid sequence, substrate identity, kinetic parameters, and assay conditions—remains uncollected and inaccessible in structured form. This constitutes a significant portion of the “dark matter” of enzymology. Unlocking these hidden data through automated extraction offers an opportunity to expand enzyme dataset diversity and size, critical Show less
Drug-drug interactions (DDIs) can produce unpredictable pharmacological effects and lead to adverse
events that have the potential to cause irreversible damage to the organism. Traditional methods to
Show more
Drug-drug interactions (DDIs) can produce unpredictable pharmacological effects and lead to adverse
events that have the potential to cause irreversible damage to the organism. Traditional methods to
detect DDIs through biological or pharmacological analysis are time-consuming and expensive, therefore,
there is an urgent need to develop computational methods to effectively predict drug-drug interactions.
Currently, deep learning and knowledge graph techniques which can effectively extract features of entities have been widely utilized to develop DDI prediction methods. In this research, we aim to systematically review DDI prediction researches applying deep learning and graph knowledge. The available
biomedical data and public databases related to drugs are firstly summarized in this review. Then, we
discuss the existing drug-drug interactions prediction methods which have utilized deep learning and
knowledge graph techniques and group them into three main classes: deep learning-based methods,
knowledge graph-based methods, and methods that combine deep learning with knowledge graph.
We comprehensively analyze the commonly used drug related data and various DDI prediction methods,
and compare these prediction methods on benchmark datasets. Finally, we briefly discuss the challenges
related to drug-drug interactions prediction, including asymmetric DDIs prediction and high-order DDI
prediction. Show less