Computational advancements to facilitate therapeutic application of phytochemicals: Where do we stand?

The bioactivity of phytochemicals has been widely reported in the literature, however, the abundance of phytochemical resources and their potent activities require laborious screening methods for feasible applications. Owing to the lack of pharmacologically safe therapeutic options to tackle emerging infections and drug resistance, there is an increasing interest in the diverse therapeutic potential of bioactive phytochemicals. However, consolidated reports on the same are very limited. The present article provides an overview of exemplary studies from the last decade on the application of in silico resources that have guided the fast and efficient therapeutic application of phytochemicals. The application domain pertains to the functional aspects of phytochemicals, such as their antibacterial, antiviral, antiparasitic, antifungal, antioxidant, anti-inflammatory, and anticancer effects. Based on the reviewed computational approaches, a common and popularly adopted pipeline was illustrated for the therapeutic utility of phytochemicals. A list of databases is provided to help computational researchers identify phytocompounds for therapeutic research. The prospect of generating a high volume of therapeutic research data on phytochemicals can facilitate machine learning and artificial intelligence-based methods for future therapeutic predictions during healthcare emergencies and disease outbreaks. Establishment of a comprehensive computational drug discovery pipeline for phytocompounds. Integration of global phytochemical databases to accelerate therapeutic identification. The emerging role of AI/ML in enhancing natural product drug discovery. Case-based Evidence supporting translational potential from in silico to in vitro. Establishment of a comprehensive computational drug discovery pipeline for phytocompounds. Integration of global phytochemical databases to accelerate therapeutic identification. The emerging role of AI/ML in enhancing natural product drug discovery. Case-based Evidence supporting translational potential from in silico to in vitro.