Adverse drug reactions (ADRs) are a significant cause of hospital admissions and treatment discontinuation worldwide. Conventional approaches often fail to detect rare or delayed effects of medicinal products. In order to improve early detection, a research team from the Medical University of Sofia developed a deep learning model to predict the likelihood of ADRs based solely on a drug’s chemical structure.
The model was built using a neural network trained using reference pharmacovigilance data. Input features were derived from SMILES codes – a standard format representing molecular structure. Predictions were generated for six major ADRs: hepatotoxicity, nephrotoxicity, cardiotoxicity, neurotoxicity, hypertension, and photosensitivity.
“We could conclude that it successfully identified many expected reactions while producing relatively few false positives,” the researchers write in their paper published in the journal Pharmacia , concluding it “demonstrates acceptable accuracy in predicting ADRs.”
Testing of the model with well-characterized drugs resulted in predictions consistent with known side-effect profiles. For example, it estimated a 94.06% probability of hepatotoxicity for erythromycin, 88.44% for nephrotoxicity and 75.8% for hypertension in cisplatin. Additionally, 22% photosensitivity was predicted for cisplatin, while 64.8% photosensitivity was estimated for the experimental compound ezeprogind. For enadoline, a novel molecule, the model returned low probability scores across all ADRs, suggesting minimal risk.
Notably, these results demonstrate the model’s potential as a decision-support tool in early-phase drug discovery and regulatory safety monitoring. The authors acknowledge that performance of the infrastructure could be further enhanced by incorporating factors such as dose levels and patient-specific parameters.
Original source
Ruseva V, Dobrev S, Getova-Kolarova V, Peneva A, Getov I, Dimitrova M, Petkova V (2025) In situ development of an artificial intelligence (AI) model for early detection of adverse drug reactions (ADRs) to ensure drug safety. Pharmacia 72: 1–8. https://doi.org/10.3897/pharmacia.72.e160997
Pharmacia
30-Jul-2025