Application and Development of Computational Methods for Structure-based Drug Discovery
Doktorsavhandling, 2014

Researchers involved in drug discovery need tools to support their decisions regarding which of many available chemical entities could be developed into potent drug candidates. The qualitative and quantitative results from computational docking methods constitute the basis for structure-based rational design of inhibitors in the early stages of drug discovery. Research contributions related to application and development of such computational methods are presented here. Initially, existing computational methods were used to obtain insights into the functionality of human enzyme Arylsulfatase A and the adhesion of Norovirus VA387 to human ABO blood group saccharides. The computational results were validated either with existing experimental data or with data from new experiments, and have significant importance for rational design of inhibitors for Arylsulfatase A and Norovirus VA387. These studies motivated the development and evaluation of new computational methods for estimating binding energies, and their thermodynamic components, of protein-ligand complexes. Support vector machine based scoring functions were developed to estimate the binding energies of the protein-ligand interactions including their enthalpy and entropy components with significant accuracy. The estimations by the reported scoring functions are seen to outperform the existing scoring functions in benchmarks with protein-ligand complexes from the PDBbind database. Methods based on expanded ensemble molecular dynamics simulations were explored for the first time for estimating the binding energies and their thermodynamic components. Binding energies were estimated for interactions of plant lectin hevein with its carbohydrate ligands, giving results that are in good agreement with the existing experimental binding data. These methodological developments related to estimating binding energies and their thermodynamic components should be valuable for profiling the binding characteristics of lead compounds that could be developed to potential drug candidates.

Opponent: Prof. Peter J Reilly, Iowa State University, USA


Ashok Krishna Chaitanya Koppisetty

Chalmers, Data- och informationsteknik, Datavetenskap


C3SE (Chalmers Centre for Computational Science and Engineering)


Annan kemi

Bioinformatik (beräkningsbiologi)


Livsvetenskaper och teknik (2010-2018)



Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie


Opponent: Prof. Peter J Reilly, Iowa State University, USA

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