Ajmer Singh Grewal – författare

Role of Green Chemistry in Ecosystem Restoration to Achieve Environmental Sustainability deals with current challenges of environmental problems along with the approaches of environmental sustainability in alliance with green chemistry. The book shows how to lessen the impact on the environment by maintaining a balance between society, the environment, and the economy, all of which are regarded as fundamental pillars of sustainability. Furthermore, policymakers and scholars will gain insights into how to develop and explore innovative techniques for achieving sustainable development goals. This book is unique in the field of environmental sustainability, as it is based on green chemistry concepts. Addresses root causes of prominent environmental problems, including environmental management, water sustainability and agricultural sustainability Discusses recent knowledge about the concepts of environmental sustainability Highlights various approaches of green chemistry to achieve sustainable development goals

The book provides a comprehensive overview of computer-aided drug design (CADD) and its various methodologies. It starts with an introduction to CADD, covering its principles and tools, and then delves into specific techniques like QSAR, molecular and quantum mechanics, and energy minimization. The book also explores practical applications such as molecular docking, ADMET prediction, de novo drug design, homology modeling, pharmacophore mapping, and virtual screening. Each chapter highlights the theoretical foundations, computational methods, and real-world applications, emphasizing the role of CADD in modern pharmaceutical research and drug development.

This book offers a comprehensive exploration of how artificial intelligence (AI) is transforming the development of therapeutics for some of the world’s most overlooked infectious diseases, including tuberculosis, malaria, leishmaniasis, and Chagas disease. This book presents state-of-the-art advancements in AI-driven target identification, drug repurposing, and de novo drug design.It delves into deep learning techniques such as CNNs, RNNs, VAEs, and GANs, for predicting drug-pathogen interactions, enhancing molecular docking, and integrating multi-omics data for biomarker discovery. Practical methodologies are outlined for leveraging AI in ADMET prediction and drug design, with accessible frameworks to support application in real-world research.The book features case studies that demonstrate how AI addresses key challenges such as drug resistance, toxicity, and vaccine development, while also covering ethical and regulatory considerations critical to ensuring equitable access in low-resource settings.Designed for scientists in drug discovery, computational biology, and pharmaceutical research, this book serves as an essential guide for applying AI technologies in neglected disease research. By bridging cutting-edge computational methods with practical drug development challenges, it offers a timely and valuable resource for accelerating innovation in global health.