Moringa oleifera is a traditional Indian herb belonging to the Moringaceae family, it is commonly known as the horse‐radish tree, drumstick, or sahajna. In developing countries, Moringa is used as feed for both humans and animals due to its well‐known antioxidant, anti‐inflammatory, and anti‐apoptotic properties owing to its several phytoconstituents including β‐carotene, quercetin, kaempferol, ascorbic acid, flavonoids, phenolic acid, rhamnose, glycosylates, glucomoringin, and isothiocyanates. These constituents help to maintain the brain antioxidant enzyme levels, mitochondrial functions, and neurogenesis, showing neuroprotective effects in several neurodegenerative disorders including Parkinson's Disease, Alzheimer's Disease, Huntington's Disease, and Amyotrophic lateral sclerosis. This review discusses various phytoconstituent of moringa and their therapeutic potential in various neurological disorders. Additionally, we also concise the safety and toxicity profile, of different molecular pathways involved in the neuroprotective effect of M. oleifera including M. oleifera nanoparticles for better therapeutic value. Practical applications Several clinical and preclinical studies on Moringa oleifera have been conducted, and the outcomes indicate moringa could be used in the treatment of brain disorders. As a result, we conclude that moringa and its nanoformulations could be employed to treat neurological problems. In the future, M. oleifera phytoconstituents could be evaluated against specific signaling pathways, which could aid researchers in discovering their mechanism of action. Furthermore, the use of moringa as a nutraceutical owing to its myriad pharmacological potential will go a long way in boosting the economy of countries that grow moringa on a large scale.

Artificial intelligence (AI) is a machine science that can mimic human behaviour like intelligent analysis of data. AI functions with specialized algorithms and integrates with deep and machine learning. Living in the digital world can generate a huge amount of medical data every day. Therefore, we need an automated and reliable evaluation tool that can make decisions more accurately and faster. Machine learning has the potential to learn, understand and analyse the data used in healthcare systems. In the last few years, AI is known to be employed in various fields in pharmaceutical science especially in pharmacological research. It helps in the analysis of preclinical (laboratory animals) and clinical (in human) trial data. AI also plays important role in various processes such as drug discovery/manufacturing, diagnosis of big data for disease identification, personalized treatment, clinical trial research, radiotherapy, surgical robotics, smart electronic health records, and epidemic outbreak prediction. Moreover, AI has been used in the evaluation of biomarkers and diseases. In this review, we explain various models and general processes of machine learning and their role in pharmacological science. Therefore, AI with deep learning and machine learning could be relevant in pharmacological research.

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