Computational Drug Discovery : Methods And Applications , Volumes 1 & 2 (Original PDF From Publisher)

Computational Drug Discovery : Methods And Applications , Volumes 1 & 2 (Original PDF From Publisher)

By Vasanthanathan Poongavanam (Author) 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: 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 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.