< Back to previous page


Development and Application of Machine Learning and Statistical Methods for Chemical Drug Discovery

As a computational chemist, I will be responsible for researching the
viability of new drug ligands. With the aide of Declarative Languages and
Artificial Intelligence (DTAI) and the Center for Drug Discovery Design (CD3). I will primarily apply statistical models and methods, such as Partial Least Squares,  Principal Component Analysis and optimization methods like Support Vector Machines. The innovative methods I will derive and use for the purpose of the drug predictions, will be guided by knowledge of contemporary and groundbreaking scientific research being done by researchers in the area of HIV in academia (KULeuven, division of Rega Prof. Jan Balzarini and Prof. Annemie Vandamme) and in industry (example Tibotec).  I will additionally be using cutting edge and novel software to computationally derive compounds that can be used in realistic chemotherapeutic applications. I will use chemically relevant data sources from PubChem, PDB/PDBBind, Stanford's hivdb, along with many others, to assist in the drug discovery process. Along with occasionally having to build molecular models using a molecular modeling tools, This will be my goal as I work towards developing drug ligands using innovative methods.
Date:1 Oct 2008  →  30 Sep 2014
Keywords:Drug predictions
Disciplines:Other engineering and technology
Project type:PhD project