Year: 2015
Type: dissertation
Abstract: Computational methods have a crucial role in modern drug discovery and design. Virtual screening (VS) is an efficient and cost-effective way to screen large molecular databases computationally in the search for novel drug candidate molecules. In VS, the commonly used computational methods can be categorized into ligand-based and protein structure-based methods. Ligand-based methods rely on the idea that similar compounds have similar properties, while protein structure-based methods utilize information about the target protein when ligand binding is predicted. This doctoral thesis continues the development of Panther, a promising novel negative image-based VS method. This VS method employs chemical and structural information derived from the ligand-binding area of the protein in the very rapid screening of molecules. In addition, several other computational methods are tested for their ability to be used as VS tools and to predict ligand binding. This comparison of the VS methods showed differences in their ability to identify active molecules and predict binding affinities, which helped to determine the relative advantages and limitations of these methods. However, based on these results, it is difficult to recommend one method over other methods.
Author: Sanna Niinivehmas
Language: English
Subfield: Drug Discovery
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W2423532033