@inbook{e071f5dd76244030ad721d773bebba54,
title = "SBDD and Its Challenges",
abstract = "Proteins are the important biological macromolecules that are targeted by most of the existing drugs. SBDD play a critical role in design of drug-like, novel, potent, and safe modulators. It is a joint effort from structural biologists and computational scientists, which considers various limitations of the techniques and suitably guides drug designers. Identifying a novel, potent, and safe drug-like molecule is a long challenging path, and throughout this discovery journey, SBDD provides crucial guiding light at different stages. SBDD involves the use of structural data of target proteins to identify suitable ligand candidates that might bind the protein of interest and modulate its functions, resulting in therapeutic benefit. In this chapter, we provide an overview of computational SBDD workflow, and the various challenges associated with it. We also discuss strategies that could be adopted to tackle the challenges by making the best use of available information.",
keywords = "Binding affinity prediction, Ligand screening, Protein flexibility, Structure selection, Structure-based drug discovery (SBDD)",
author = "Sohini Chakraborti and S. Sachchidanand",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.",
year = "2023",
doi = "10.1007/978-3-031-33871-7_1",
language = "English",
isbn = "9783031338700",
series = "Challenges and Advances in Computational Chemistry and Physics",
publisher = "Springer ",
pages = "1--24",
editor = "Kar, {Supratik } and Leszczynski, {Jerzy }",
booktitle = "Current Trends in Computational Modeling for Drug Discovery",
edition = "1",
}