Free energy methods in drug discovery : current state and future directions
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Bibliographic Information
Free energy methods in drug discovery : current state and future directions
（ACS symposium series, 1397）
American Chemical Society, c2021
- : hardcover
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Includes bibliographical references and index
Description and Table of Contents
Description
Panoramic review of free energy methods
Continuous improvement in both hardware and software has resulted in the widespread use of rigorous free energy methodologies in the field of drug discovery. Given this, the practitioner, development, and commercial communities find themselves in ever closer collaboration working to apply these methodologies in the search for novel therapeutic agents. This work presents contributions from expert leaders across the free energy field and explores both the current and possible future states of
method development, application, and utilization. Researchers engaged in computational, physical, and pharmaceutical research will find this collection valuable.
Table of Contents
Preface
Preface
Chapter 1: Free Energy Methods in Drug Discovery-Introduction, Zoe Cournia, Christophe Chipot, Benoit Roux, Darrin M. York, and Woody Sherman
Chapter 2: Use of Free Energy Methods in the Drug Discovery Industry, Katharina Meier, Joseph P. Bluck, and Clara D. Christ
Chapter 3: Perspective on the SAMPL and D3R Blind Prediction Challenges for Physics-Based Free Energy Methods, Nicolas Tielker, Lukas Eberlein, Oliver Beckstein, Stefan Gussregen, Bogdan I. Iorga, Stefan M. Kast, and Shuai Liu
Chapter 4: On the Issues Impacting Reproducibility of Alchemical Free Energy Calculations, Miroslav Suruzhon, Marley L. Samways, and Jonathan W. Essex
Chapter 5: Prospective Application of Free Energy Methods in Drug Discovery Programs, Aysegul OEzen, Emanuele Perola, Natasja Brooijmans, and Joseph Kim
Chapter 6: Computational Approaches for Protein pKa Calculations, Dilek Coskun
Chapter 7: Robust, Efficient and Automated Methods for Accurate Prediction of Protein-Ligand Binding Affinities in AMBER Drug Discovery Boost, Tai-Sung Lee, Hsu-Chun Tsai, Abir Ganguly, Timothy J. Giese, and Darrin M. York
Chapter 8: Impacting Drug Discovery Projects with Large-Scale Enumerations, Machine Learning Strategies, and Free-Energy Predictions, Jennifer L. Knight, Karl Leswing, Pieter H. Bos, and Lingle Wang
Chapter 9: Optimizing Simulations Protocols for Relative Free Energy Calculations, Paul Labute and Maximilian Ebert
Chapter 10: Fast, Routine Free Energy of Binding Estimation Using Movable Type, Lance M. Westerhoff and Zheng Zheng
Chapter 11: Free Energy Methods in Drug Discovery: Who We Are, Where We Are, and Where We Are Going, Kira A. Armacost, Eric C. Gladstone, and David C. Thompson
Editors' Biographies
Author Index
Subject Index
by "Nielsen BookData"
