Geeta Deswal – författare

Integrate QSAR, molecular docking, AI, and nanotechnology into drug design Computational approaches now drive critical decisions at every stage of drug discovery and development. Computer-Aided Drug Design: Principles, Techniques, and Applications provides an in-depth treatment of CADD methodologies, from QSAR and molecular docking to pharmacophore mapping and virtual screening. Written by Ajmer Singh Grewal, a pharmaceutical chemistry researcher with nearly 13 years of experience, each chapter links theoretical foundations directly to real-world drug design applications. Computer-Aided Drug Design covers molecular and quantum mechanics, energy minimization, ADMET prediction, de novo drug design, and homology modeling. The book integrates artificial intelligence, machine learning, and nanotechnology into its treatment of contemporary drug discovery workflows. Case studies demonstrate practical use of computational tools, connecting technique-level detail to tangible outcomes in anticancer, anti-diabetic, anti-inflammatory, and Alzheimer s therapeutic research programs. Readers will also find: Detailed coverage of QSAR modeling, molecular docking protocols, and pharmacophore mapping with step-by-step computational methodologies for each technique Integration of artificial intelligence and machine learning approaches into structure-based and ligand-based drug design workflowsPractical guidance on ADMET prediction tools and their application to optimizing drug candidate selectivity and efficacyReal-world case studies linking computational modeling to therapeutic outcomes across multiple disease areas including oncology and neurodegenerationCoverage of de novo drug design, homology modeling, and virtual screening techniques with current computational tools and software platforms Designed for pharmaceutical scientists, computational chemists, bioinformatics and cheminformatics professionals, and advanced postgraduate students, this reference connects foundational CADD principles with current AI-driven and nanotechnology-enhanced approaches. It serves as a practical resource for researchers and drug developers seeking to apply computational methods across the drug discovery pipeline.

Integrate QSAR, molecular docking, AI, and nanotechnology into drug design Computational approaches now drive critical decisions at every stage of drug discovery and development. Computer-Aided Drug Design: Principles, Techniques, and Applications provides an in-depth treatment of CADD methodologies, from QSAR and molecular docking to pharmacophore mapping and virtual screening. Written by Ajmer Singh Grewal, a pharmaceutical chemistry researcher with nearly 13 years of experience, each chapter links theoretical foundations directly to real-world drug design applications. Computer-Aided Drug Design covers molecular and quantum mechanics, energy minimization, ADMET prediction, de novo drug design, and homology modeling. The book integrates artificial intelligence, machine learning, and nanotechnology into its treatment of contemporary drug discovery workflows. Case studies demonstrate practical use of computational tools, connecting technique-level detail to tangible outcomes in anticancer, anti-diabetic, anti-inflammatory, and Alzheimer s therapeutic research programs. Readers will also find: Detailed coverage of QSAR modeling, molecular docking protocols, and pharmacophore mapping with step-by-step computational methodologies for each technique Integration of artificial intelligence and machine learning approaches into structure-based and ligand-based drug design workflowsPractical guidance on ADMET prediction tools and their application to optimizing drug candidate selectivity and efficacyReal-world case studies linking computational modeling to therapeutic outcomes across multiple disease areas including oncology and neurodegenerationCoverage of de novo drug design, homology modeling, and virtual screening techniques with current computational tools and software platforms Designed for pharmaceutical scientists, computational chemists, bioinformatics and cheminformatics professionals, and advanced postgraduate students, this reference connects foundational CADD principles with current AI-driven and nanotechnology-enhanced approaches. It serves as a practical resource for researchers and drug developers seeking to apply computational methods across the drug discovery pipeline.

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.