The pharmaceutical research field is experiencing a fundamental change because scientists now face more difficult biological data challenges, while patients increasingly need customized medications, and scientists require better methods to discover new drugs. Pharmaceutical research progress is hampered by traditional computational methods, which fail to analyze extensive datasets, thus reducing both research speed and accuracy. The brain-inspired architecture of neuromorphic computing enables organizations to implement real-time adaptive systems that make decisions and process data more effectively. The research paper examines how neuromorphic computing functions within the pharmaceutical sector by demonstrating its usage in drug development processes, personalized medicine treatments, clinical trial research, and biosecurity measures. The brain-inspired computing models used by neuromorphic systems include spiking neural networks (SNNs) and memristor-based hardware, which help researchers improve molecular simulations and drug interaction predictions while creating personalized treatment plans. The systems enable real-time analysis and adaptive learning, which boosts drug screening efficiency and speeds up clinical trials while developing intelligent drug delivery systems. The main obstacles that obstruct neuromorphic computing development include two main issues that scientists must solve. These issues involve two main challenges: researchers need to improve computational efficiency, while AI pharmaceutical decisions require assessment of scalability and ethical implications. The implementation of neuromorphic models into current pharmaceutical systems needs scientists from three fields: AI research, neuroscience research, and regulatory authority work. Neuromorphic computing has the potential to transform pharmaceutical research by solving current computational problems while creating more precise and affordable therapies that focus on patient needs. The pharmaceutical sector waits for a major shift in drug development and healthcare transformation because scientists are developing neuromorphic hardware and hybrid AI systems.
Yadav et al. (Tue,) studied this question.