Medicinal plants serve as a valuable source of secondary metabolites since time immemorial. Computational Research in 21st century is giving more attention to medicinal plants for new drug design as pharmacological screening of bioactive compound was time consuming and expensive. Computational methods such as Molecular Docking, Molecular Dynamic Simulation and Artificial intelligence are significant Insilico tools in medicinal plant research. Molecular docking approach exploits the mechanism of potential phytochemicals into the target active site to elucidate its interactions and biological therapeutic properties. MD simulation illuminates the dynamic behavior of biomolecules at atomic level with fine quality representation of biomolecules. Dramatical advancement in computer science is illustrating the biological mechanism via these tools in different diseases treatment. The advancement comprises speed, the system configuration, and other software upgradation to insights into the structural explanation and optimization of biomolecules. A probable shift from simulation to artificial intelligence has in fact accelerated the art of scientific study to a sky high. The most upgraded algorithm in artificial intelligence such as Artificial Neural Networks, Deep Neural Networks, Neuro-fuzzy Logic has provided a wide opportunity in easing the time required in classical experimental strategy. The notable progress in computer science technology has paved a pathway for understanding the pharmacological functions and creating a roadmap for drug design and development and other achievement in the field of medicinal plants research. This review focus on the development and overview in computational research moving from static molecular docking method to a range of dynamic simulation and an advanced artificial intelligence such as machine learning.

自古以来，药用植物是次生代谢物的宝贵来源。由于生物活性化合物的药理筛选既费时又费钱，21世纪的计算研究越来越关注用于新药设计的药用植物。计算方法如分子对接，分子动力学模拟和人工智能是显著Insilico药用植物研究的工具。分子对接方法利用潜在的植物化学物质进入目标活性位点的机制来阐明其相互作用和生物治疗特性。MD 模拟在原子水平上阐明了生物分子的动态行为，并具有生物分子的高质量表示。计算机科学的巨大进步正在通过这些工具说明不同疾病治疗中的生物学机制。进步包括速度、系统配置和其他软件升级，以深入了解生物分子的结构解释和优化。从模拟到人工智能的可能转变实际上将科学研究的艺术加速到了天上。人工神经网络等人工智能领域最升级的算法，深度神经网络、神经模糊逻辑为减轻经典实验策略所需的时间提供了广泛的机会。计算机科学技术的显着进步为了解药理功能、制定药物设计和开发路线图以及药用植物研究领域的其他成果铺平了道路。本综述重点关注计算研究的发展和概述，从静态分子对接方法到一系列动态模拟和高级人工智能（如机器学习）。计算机科学技术的显着进步为了解药理功能、制定药物设计和开发路线图以及药用植物研究领域的其他成果铺平了道路。本综述重点关注计算研究的发展和概述，从静态分子对接方法到一系列动态模拟和高级人工智能（如机器学习）。计算机科学技术的显着进步为了解药理功能、制定药物设计和开发路线图以及药用植物研究领域的其他成果铺平了道路。本综述重点关注计算研究的发展和概述，从静态分子对接方法到一系列动态模拟和高级人工智能（如机器学习）。