Computational Drug Discovery: Methods and Applications

Comprehensive resource explaining efficient and cost-effective computational technologies for drug optimizations in order to enable innovative drug exploration and design

Computational Drug Discovery: Methods and Applications (2V set) covers a wide range of cutting-edge computational technology or computational chemistry method that are transforming drug discovery. The book delves into recent advances, particularly focusing on artificial intelligence (AI) applications in protein structure prediction, AI-enabled virtual screening, and generative modeling. Additionally, it covers key technological advancements in computing impacting drug discovery, such as quantum computing, and cloud computing.

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 targeting 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 utilizing 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 relevant 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:

• Application of molecular dynamics simulations and related approaches in drug discovery
• The application of QM, hybrid approaches such as QM/MM, and fragment molecular orbital framework for understanding protein-ligand interactions
• Adoption of artificial intelligence in pre-clinical drug discovery, encompassing protein structure prediction, generative modeling for de novo design, and virtual screening.
• Techniques for navigating and visualizing the chemical space, along with the utilization of big data to drive drug discovery efforts.
• Methods for performing ultra-large-scale virtual screening for hit identification.
• Computational strategies for designing new therapeutic models, including PROTACs and molecular glues.
• In silico ADMET approaches for predicting a variety of pharmacokinetic and physicochemical endpoints.
• The role of computing technologies like quantum computing and cloud computing in accelerating drug discovery

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.