Advancing Personalized Medicine – An Innovative Feature Engineering for Tailored Drug Design

This paper introduces a pioneering approach integrating Advanced Encryption Standard (AES) security algorithms with multi-objective drug design, aimed at personalized medicine and optimized drug discovery. By leveraging comprehensive patient profiles including genetic markers and medical histories, machine learning and optimization algorithms generate personalized treatment plans and drug efficacy scores. Key objectives include establishing a robust multi-objective optimization framework, enhancing drug efficacy, ensuring data security with AES encryption, and elevating patient care quality. The methodology emphasizes customizing medicines based on individual patient data, employing AES security measures, and optimizing treatments for effectiveness, paving the way for a patient-centric, secure healthcare paradigm beyond traditional drug design methodologies.