AIOps Observability and Performance Impact of AI and ML Applications for Central Nervous System Drug Discoveries

Abstract

It has been a massive challenge for academia, the scientific community, the medical community, and the pharmaceutical business to develop medications that can affect the central nervous system. Artificial intelligence for IT operations (AIOps) can facilitate this process by involving critical evaluation decisions with AI-driven technologies like Machine Learning Operations (MLOps) to eliminate repetitive inconsequential procedures. Statistically, neurological ailments/diseases are far greater in number than their counterparts or standard therapeutic diseases. The discovery/invention of drugs for central nervous system (CNS) has lagged behind industry standards for too long. Now with the intervention of AIOps, research and discoveries are more accurate and progress at a faster rate. For instance, in Schizophrenia, there can be several permutations and combinations in the underlying disease itself; naturally, effective treatment suffers. Coupled with performance testing of the model will give full confidence to execute the models in parallel for desired output within the expected time. Modern biomedical data, bolstered by significant contributions from AI and ML, offer promising prospects for addressing CNS disorders in effective ways. In this study, we have highlighted the best possible AI- and ML-assisted technological approaches to uncover the mysteries that CNS has posed. There is no straightforward strategy that can be employed as with other body disorders, as the intensity of the underlying disease can vary from patient to patient. This study elaborates on many techniques that have been integrated with AIOps, Performance Testing, and the associated AI, ML, and Deep Learning (DL) applications.