Computational Drug Discovery: A Comprehensive Guide to Cutting-Edge Technologies and Methods

Authored by Vasanthanathan Poongavanam, Computational Drug Discovery: Methods and Applications is a 2-volume set that delves into the latest advancements in computational technologies and methods driving innovation in drug discovery.

A Comprehensive Resource for Professionals

This book covers a wide range of cutting-edge computational technologies and computational chemistry methods that are transforming drug discovery. From artificial intelligence (AI) and its application for protein structure prediction, AI-enabled virtual screening, and generative modeling for compound design, to key technological advancements in computing such as quantum and cloud computing, this comprehensive resource has it all.

Recent Trends in Computer-Aided Drug Design

Dedicated chapters address recent trends in the field, 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. The book also covers the application of molecular dynamics and other related methods, the use of QM and QM/MM methods in computational drug design, and techniques for navigating and visualizing the chemical space.

Big Data and Drug Discovery

The book highlights the importance of leveraging big data to drive drug discovery efforts, providing readers with an overview of the latest advances in computational drug discovery.

Key Topics Covered

• 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 harnessing 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 comprehensive guide 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, the pharmaceutical industry, and major drug discovery software providers, it offers an overview of the latest advances in computational drug discovery, making it a valuable resource for professionals engaged in drug discovery.