Today’s medicinal chemists largely rely on expensive high throughput screening to discover new drugs, but with the proliferation of advanced computing, a new paradigm is emerging whereby the efficiency of finding new drug leads can be increased by simulation and modelling
Chandrajit Bajaj, professor of computer science at The University of Texas in Austin, has been integrally involved in computational drug discovery for more than 20 years. As director of the Computational Visualization Center at the university’s Institute for Computational Engineering and Sciences (ICES), Bajaj has systematically attacked each step of the computational drug discovery process and recently made dramatic improvements to the algorithms involved in finding new candidate compounds to treat diseases such as HIV and Dengue Fever.
The process – a combination of modelling, simulation, analysis and visualisation – is accomplished by Bajaj through the expert application of biophysical algorithms and the use of the high performance, parallel processing supercomputers at the Texas Advanced Computing Center (TACC).
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