Computational Approaches to Counterfeit Drug Detection Using SMILE Representation: A Review

Abstract

Public health is seriously threatened by the worldwide distribution of fake medications, especially in areas with lenient regulatory control. Despite their effectiveness, traditional detection techniques including spectroscopy, High-Performance Liquid chromatography (HPLC), and packaging inspection are frequently costly, time-consuming, and insufficient for large-scale or real-time deployment. For the purpose of overcoming these constraints, this paper investigates the possibilities of computational techniques utilizing the Simplified Molecular Input Line Entry System (SMILES). SMILES facilitates the smooth integration of sophisticated Machine Learning (ML) and Deep Learning (DL) models by encoding chemical structures as machine-readable strings. To be able to distinguish between real and fake medications, methods like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and transformed-based models like ChemBERTa and SMILES Transformer may learn complex molecular patterns. According to comparative analyses, CNNs perform better than RNNs at identifying structural submotifs, which are essential for precise categorization. Moreover, hybrid learning techniques and data augmentation techniques like randomized SMILES improve model robustness. In addition to highlighting the revolutionary potential of SMILES-based DL techniques in the detection of counterfeit drugs, this paper promotes more investigation into computational frameworks that are scalable, explicable, and compliant with regulations.