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

Background and objective: Adverse drug reactions (ADRs) are the main safety concerns of
clinically used medications. Accumulating evidence has shown that ADRs can affect men …

One of the largest spontaneous adverse events reporting databases in the world is the Food
and Drug Administration (FDA) Adverse Event Reporting System (FAERS). Unfortunately …

With their diverse biological activities, peptides are promising candidates for therapeutic
applications, showing antimicrobial, antitumour and hormonal signalling capabilities …

This research study explores the potential of leveraging the FDA Adverse Event Reporting
System (FAERS), combined with big data analytics and machine learning techniques, to …

Protein–ligand interactions are essential to drug discovery and drug development efforts.
Desirable on-target or multitarget interactions are the first step in finding an effective …

We apply a heterogeneous graph convolution network (GCN) combined with a multi-layer
perceptron (MLP) denoted by GCNMLP to explore the potential side effects of drugs. Here …

Prescribing medications is a fundamental practice in the management of illnesses that
necessitates in-depth knowledge of clinical pharmacology. Polypharmacy, or the concurrent …

Drug-induced liver injury (DILI) is the most common trigger for acute liver failure and the
leading cause of attrition in drug development. In this study, we developed an in-silico …

Since side-effects of drugs are one of the primary reasons for their failure in clinical trials,
predicting their side-effects can help reduce drug development costs. We proposed a …