<p>Medication recommendation aims to predict effective drug combinations for patients with complex conditions. Critically-ill patients often experience polypharmacy, which increases the risk of adverse drug–drug interactions (DDIs). In this paper, we use a BERT-enhanced variation of Bayesian Personalized Ranking (BPR) algorithm for providing Toxic-free Medication Recommendations. Our model, denoted as ToxicFreeMed, integrates patients’ electronic discharge notes, drug descriptions, and the DDI knowledge graph to recommend both accurate and safe drug combinations. ToxicFreeMed leverages pretrained discharge notes embeddings-capturing patient’s disease, therapy, and medication context-and pretrained drug descriptions embeddings within a multitask learning framework that jointly optimizes ranking accuracy and a toxicity-weighted DDI loss. We have evaluated ToxicFreeMed on the MIMIC-III dataset augmented with DrugBank knowledge and show that it effectively balances accuracy and safety, outperforming other strong recommendation algorithms while reducing DDIs. Our results indicate that ToxicFreeMed is a promising approach that has the potential to assist clinicians in identifying therapeutically appropriate and pharmacologically safer treatment options.</p>