Structure-Based Computational Design of Plant-Derived Therapeutic Agents

Abstract

Although the new target molecules have been identified from H. sapiens, the bioactive molecules obtained from plants are still the precious source for discovery of new therapeutic agents because of their structural diversity and vast pharmacological activities. In recent years, the study of natural products has been revolutionized by the possibility of using molecular docking, molecular dynamics simulations, pharmacophore modeling, virtual screening, quantitative structure–activity relationship (QSAR) analysis, binding free-energy calculations, and artificial intelligence (AI) approaches to advance the process of drug discovery. These computational methodologies have been useful in the development of efficient drug discovery tools for identification of drug targets, lead optimization, prediction of ADMET properties and rational design of drug therapeutics based on phytochemicals and reducing the cost and time of experimental drug discovery. Furthermore, AI, machine learning and multi-omics integration has enhanced the predictive capabilities and has enabled precision medicine applications in cancer, neurodegenerative diseases, cardiovascular, metabolic, infectious, and inflammatory diseases. While there are hurdles to address, such as the need for higher-quality structural data, a better understanding of protein flexibility, and the ability to validate experimental results, the potential for personalized phytopharmaceutical development continues to grow thanks to ongoing developments in the fields of computational biology and explainable AI. The principles, methodologies, applications, challenges and future prospects of structure-based computational approaches to discovery and optimization of plant-based therapeutic agents are highlighted in this chapter.