Vivek Asati – författare

Current Molecular Targets of Heterocyclic Compounds for Cancer Therapy discusses recently developed treatments based on molecular targets which are genetically altered in cancer cells and are essential for tumor development and survival. Considerable research effort has been devoted to the development of targeted drugs that inhibit the action of pathogenic kinases, and clinical studies performed so far have validated the positive effects of kinase inhibitors for cancer treatment. Each chapter discusses a molecular target, such as ALK2, ATR, CK, Src-Abl, EGFR, Fyn-Blk-Lyn, IGFs, and PAK1.The book's chapters are written by experts who actively work on the targets to help readers fully understand how they can be used. This is a valuable resource for cancer researchers, oncologists, graduate students and members of the biomedical field who are interested in the potential of novel cancer therapies based on molecular targets.

Discusses recently discovered molecular targets for cancer therapyBrings updated literature of heterocyclic compounds, an important construction motif for the development of new anticancer drugsEncompasses comprehensive compilation of recently introduced anticancer drugs in the market and their health outcomes and pharmacoeconomics

Current Molecular Targets of Heterocyclic Compounds for Cancer Therapy discusses recently developed treatments based on molecular targets which are genetically altered in cancer cells and are essential for tumor development and survival. Considerable research effort has been devoted to the development of targeted drugs that inhibit the action of pathogenic kinases, and clinical studies performed so far have validated the positive effects of kinase inhibitors for cancer treatment. Each chapter discusses a molecular target, such as ALK2, ATR, CK, Src-Abl, EGFR, Fyn-Blk-Lyn, IGFs, and PAK1.The book''s chapters are written by experts who actively work on the targets to help readers fully understand how they can be used. This is a valuable resource for cancer researchers, oncologists, graduate students and members of the biomedical field who are interested in the potential of novel cancer therapies based on molecular targets.- Discusses recently discovered molecular targets for cancer therapy- Brings updated literature of heterocyclic compounds, an important construction motif for the development of new anticancer drugs- Encompasses comprehensive compilation of recently introduced anticancer drugs in the market and their health outcomes and pharmacoeconomics

Integrating Artificial Intelligence with Virtual Screening: A New Era in Drug Discovery addresses the evolving landscape of drug discovery by combining traditional virtual screening with cutting-edge artificial intelligence technologies. This volume offers an interdisciplinary guide to AI-driven ligand and structure-based screening methods, including machine learning, deep learning, and generative models, thus enabling faster and more accurate identification of drug candidates. It covers mathematical models, computational workflows, and optimization strategies such as Pareto-based and physics-based approaches.Sections also discuss challenges in navigating ultra-large chemical libraries and present practical case studies that demonstrate real-world applications. Edited by leading researchers, the book provides authoritative insights suited for graduate students, researchers, and industry professionals. Users will be empowered by the latest tools and knowledge to innovate drug discovery pipelines and support the development of precision medicines and foster advancements in pharmaceutical sciences.Presents comprehensive AI-driven virtual screening methods that enhance efficiency in drug discoveryExplains the integration of machine learning, deep learning, and physics-based optimization techniquesCovers ligand-based and structure-based virtual screening with practical case studiesHighlights advancements in generative AI and active learning for molecular designAddresses challenges and solutions in screening ultra-large chemical libraries