Deciphering the Deep Learning and Machine Learning Tactics in Advancement of Neuroprotection by Phytochemicals

An unhealthy lifestyle, adverse living conditions, and health ailments have gradually affected people’s lives worldwide, leading to an exponential rise in chronic diseases. Additionally, alarming medical expenses, drug toxicity, and poor curability further summarize healthcare’s critical condition globally. Many phytoconstituents are being explored and studied for their key bioactivities, such as neuroprotective, anti-apoptotic, and anti-inflammatory effects in various neurological conditions. They constitute potent phytochemicals, such as terpenes, flavonoids, polyphenols, and carotenoids. Thus, numerous natural plant-derived pharmacophores and derivatives are under the discovery pipeline, unraveling their crucial mechanism of action and therapeutic effects with less lethality. Machine learning and deep learning methodologies have been utilized in multiple drug discovery processes such as synthesis, virtual screening, toxicity prediction, and ligand-based screening for design and development of novel entities for neuro diseases. Here, we aim to emphasize the importance of this tool for the identification of plant-based lead molecules against neuroprotection especially in Alzheimer’s disease. Hence, numerous natural products are being explored to address concerns with conventional therapies. In this chapter, we intend to comprehend advancements in phytochemical discovery and their mechanism of action. In addition, we also provide insights into the utilization of deep learning and machine learning tactics in advancements of neuroprotective roles of phytoconstituents.