The Application of Machine Learning in Optimizing Pharmaceutical Drug Discovery

The importance of drug development and discovery in the biopharmaceutical industry is growing. Nowadays, the process of developing new drugs is laborious, expensive, ineffective, and prone to errors. A very valuable method for drug discovery applications might be a digital microfluidic platform. Algorithms for ML have also shown to be a helpful tool for drug development. This study develops and evaluates various ML models, specifically SVM and ANN, to classify compound activity effectively. These models were created and assessed using the ChEMBL dataset, and performance metrics such as accuracy, precision, recall, and F1-score were used. The findings were obtained using confusion matrices. The ANN model demonstrated a remarkable accuracy of 91%, complemented by high precision 95% and recall of 87%, indicating robust predictive capabilities. In comparison, the SVM model exhibited a lower accuracy of 70%, highlighting the ANN's superior ability to capture complex patterns. These outcomes underscore the significant potential of ML, particularly ANN, in optimizing drug discovery processes and facilitating more effective identification of active compounds.