The computational biology contributions focus on the development of optimized algorithmic approaches that utilize high performance computing (HPC) resources in order to analyze high dimensional medical data. Specifically, a system has been developed that through implementing new and utilizing existing web service accessible cloud computing HPC systems is capable of performing epistasis (interaction) testing in high dimensional genome wide association scans (GWAS). This research was performed in collaboration with one of the pharmaceutical industry leaders that enabled the use of state of the art HPC systems and also provided access to some of the largest GWAS datasets available. An analytical pipeline that performs both traditional and exploratory analytical techniques was also implemented for GWAS data. Finally a lot of work has been performed on analyzing GWAS data in combination with many other types of biomarkers, such as protein expression levels in blood to discover associations of genetic markers to biomarkers.
The chemoinformatics team focuses on specific problems found in the small molecule discovery process, primarily in drug discovery. Current research includes small molecule design (de novo design), QSAR modeling (as well as inverse-QSAR), and Virtual Screening. A new initiative focuses on the application of advanced chemoinformatics methods for the development of chemoprevention agents in close cooperation with experts in EU (Germany and Cyprus) and the US. In recent years the team has taken a leading role in the introduction of multi-objective optimization methods in chemoinformatics and has proposed such algorithms for de novo drug design among others.