Vasanthanathan Poongavanam – författare

This publication is based on peer-reviewed manuscripts from the 2019 Conference on Drug Design & Discovery Technologies (CDDT) held at Ramaiah University of Applied Sciences, India. Providing a wide range of up to date topics on the latest advancements in drug design and discovery technologies, this book ensures the reader receives a good understanding of the scope of the field. Aimed at scientists, students, regulators, academics and consultants throughout the world, this book is an ideal resource for anyone interested in the state of the art in drug design and discovery.

Computational Drug Discovery

A comprehensive resource that explains a wide array of computational technologies and methods driving innovation in drug discovery

Computational Drug Discovery: Methods and Applications (2 volume set) covers a wide range of cutting-edge computational technologies and computational chemistry methods that are transforming drug discovery. The book delves into recent advances, particularly focusing on artificial intelligence (AI) and its application for protein structure prediction, AI-enabled virtual screening, and generative modeling for compound design. Additionally, it covers key technological advancements in computing such as quantum and cloud computing that are driving innovations in drug discovery.

Furthermore, dedicated chapters that addresses the recent trends in the field of computer aided drug design, including ultra-large-scale virtual screening for hit identification, computational strategies for designing new therapeutic modalities like PROTACs and covalent inhibitors that target residues beyond cysteine are also presented.

To offer the most up-to-date information on computational methods utilized in Computational Drug Discovery, it covers chapters highlighting the use of molecular dynamics and other related methods, application of QM and QM/MM methods in computational drug design, and techniques for navigating and visualizing the chemical space, as well as leveraging big data to drive drug discovery efforts.

The book is thoughtfully organized into eight thematic sections, each focusing on a specific computational method or technology applied to drug discovery. Authored by renowned experts from academia, pharmaceutical industry, and major drug discovery software providers, it offers an overview of the latest advances in computational drug discovery.

Key topics covered in the book include:

This book will provide readers an overview of the latest advancements in Computational Drug Discovery and serve as a valuable resource for professionals engaged in drug discovery.

Computational Drug Discovery

A comprehensive resource that explains a wide array of computational technologies and methods driving innovation in drug discovery

Computational Drug Discovery: Methods and Applications (2 volume set) covers a wide range of cutting-edge computational technologies and computational chemistry methods that are transforming drug discovery. The book delves into recent advances, particularly focusing on artificial intelligence (AI) and its application for protein structure prediction, AI-enabled virtual screening, and generative modeling for compound design. Additionally, it covers key technological advancements in computing such as quantum and cloud computing that are driving innovations in drug discovery.

Furthermore, dedicated chapters that addresses the recent trends in the field of computer aided drug design, including ultra-large-scale virtual screening for hit identification, computational strategies for designing new therapeutic modalities like PROTACs and covalent inhibitors that target residues beyond cysteine are also presented.

To offer the most up-to-date information on computational methods utilized in Computational Drug Discovery, it covers chapters highlighting the use of molecular dynamics and other related methods, application of QM and QM/MM methods in computational drug design, and techniques for navigating and visualizing the chemical space, as well as leveraging big data to drive drug discovery efforts.

The book is thoughtfully organized into eight thematic sections, each focusing on a specific computational method or technology applied to drug discovery. Authored by renowned experts from academia, pharmaceutical industry, and major drug discovery software providers, it offers an overview of the latest advances in computational drug discovery.

Key topics covered in the book include:

This book will provide readers an overview of the latest advancements in Computational Drug Discovery and serve as a valuable resource for professionals engaged in drug discovery.

This book summarizes state-of-the-art antiviral drug design and discovery approaches starting from natural products to de novo design, and provides a timely update on recently approved antiviral drugs and compounds in advanced clinical development. Special attention is paid to viral infections with a high impact on the world population or highly relevant from the public health perspective (HIV, hepatitis C, influenza virus, etc.).  In these chapters, limitations associated with adverse effects and emergence of drug resistance are discussed in detail. In addition to classical antiviral strategies, chapters will be dedicated to discuss the non-classical drug development strategies to block viral infection, for instance, allosteric inhibitors, covalent antiviral agents, or antiviral compounds targeting protein–protein interactions. Finally, current prospects for producing broad-spectrum antiviral inhibitors will be also addressed.

The book is distinctive in providing the most recent update in the rapidly evolving field of antiviral therapeutics. Authoritative reviews are written by international scientists well known for their contributions in their topics of research, which makes this book suitable for researchers not only within the antiviral research community but also attractive to a broad audience in the drug discovery field.

This book covers molecular structures and biochemical mechanisms mediating the antiviral effects, while discussing various ligand design strategies, which include traditional medicinal chemistry, computational chemistry, and chemical biology approaches. The book provides a comprehensive review of antiviral drug discovery and development approaches, particularly focusing on current innovations and future trends.