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.
Vasanthanathan Poongavanam is a senior scientist in the Department of Chemistry-BMC, Uppsala University, Sweden. Before starting at Uppsala University in 2016, he was a postdoctoral fellow at the University of Vienna, Austria, and at the University of Southern Denmark. He obtained his Ph.D. degree in Computational Medicinal Chemistry as a Drug Research Academy (DRA) Fellow at the University of Copenhagen, Denmark, on computational modeling of cytochrome P450. He has published more than 65 scientific articles, including reviews and book chapters. His scientific interests focus on in silico ADMET modeling including cell permeability and solubility, and he has worked extensively on understanding the molecular properties that govern the pharmacokinetic profile of molecules bRo5 property space, including macrocycles and PROTACs.
Vijayan Ramaswamy (R.S.K. Vijayan) is a senior research scientist affiliated with the Structural Chemistry division at the Institute for Applied Cancer Science, University of Texas MD Anderson Cancer, TX, USA. In 2016, he joined MD Anderson Cancer after a brief tenure as a scientist, at PMC Advanced Technologies, New Jersey, USA. He undertook postdoctoral training at Rutgers University in New Jersey, USA, and Temple University in Pennsylvania, USA. He received his Ph.D. in Pharmacy as a CSIR senior research fellow from the Indian Institute of Chemical Biology, Kolkata, India. He is a named co-inventor on 7 issued US patents, including an ATR kinase inhibitor that has advanced to Phase 2 clinical trials. He has published more than 20 scientific articles and authored one book chapter. His research focuses on applying computational chemistry methods to drive small molecule drug discovery programs, particularly for oncology and neurodegenerative diseases.
