Conventional approaches often fail to detect rare or delayed effects of medicinal products.
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-characterised 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, whereas 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 the performance of the infrastructure could be further enhanced by incorporating factors such as dose levels and patient-specific parameters.