Background: Artificial intelligence (AI) provides opportunities to identify the health risks of
patients and thus influence patient safety outcomes. Objective: The purpose of this …

Over the past decade, polypharmacy instances have been common in multi-diseases
treatment. However, unwanted drug-drug interactions (DDIs) that might cause unexpected …

The use and production of chemical compounds are subjected to strong legislative pressure.
Chemical toxicity and adverse effects derived from exposure to chemicals are key regulatory …

Artificial intelligence (AI) is a transformative technology used in various industrial sectors
including healthcare. In pharmacy practice, AI has the potential to significantly improve …

Many machine learning techniques provide a simple prediction for drug-drug interactions
(DDIs). However, a systematically constructed database with pharmacokinetic (PK) DDI …

Deep learning approaches have become popular in recent years in the field of de novo
molecular design. While a variety of different methods are available, it is still a challenge to …

Background: Adverse drug reactions (ADRs) are unintended negative drug-induced
responses. Determining the association between drugs and ADRs is crucial, and several …

The prevalence of multi_drug therapies has been increasing in recent years, particularly
among the elderly who are suffering from several diseases. However, unexpected …

The use of artificial intelligence (AI) and machine learning (ML) in pharmaceutical research
and development has to date focused on research: target identification; docking-, fragment …

Pharmacovigilance improves patient safety by detecting and preventing adverse drug
events. However, challenges exist that limit adverse drug event detection, resulting in many …