Enhancing Pharmaceutical Target Prediction Through Intelligent Feature Optimization and Ensemble Classification

The pharmaceutical industry has been in big trouble as far as accelerating the drug discovery process is concerned. These issues involve high costs of development, a time-consuming development process and numerous failures. The context-aware mechanisms with metaheuristic-guided feature selection and hybrid classification can also be combined in this paper to describe a new computational method that enhances drug-target interaction prediction. The pharmaceutical data is processed by using several stages of preparation in our methodology that involve the standardisation of the text, the division into linguistic tokens and the extraction of semantic features. The overall new concept is to use feature refinement with Ant Colony Optimisation and a hybrid classifier that combines the Elements of a Random Forest and a Logistic Regression. Pharmaceutical dataset tests indicate that this approach is much more effective than the prior machine learning approaches, with a 98.6% accuracy and a 0.985 F1-score. This approach facilitates the process of discovering candidates more quickly, reduces development times, and finds real-world applications in precision medicine and drug optimization.