T raditionally, the design o f novel drugs has essentially been a trial-and-error process despite the tremendous efforts devoted to it by pharmaceutical and academic research groups. It is estimated that only one in 3,000 compounds investigated in preclinical discovery research ever emerges as a clinical lead, and that about one in 10 drug candidates in

development ever gets through the costly process o f clinical trials. For each drug, the invest-

ment may be on the order o f $600 million over 13 years from its first synthesis to FDA ap-

proval. In 2000, U.S. pharmaceutical companies spent more than $22 billion in research and development, which, after inflation adjustment, represents a four-fold increase from the corresponding figure some 20 years ago. In an attempt to counter these rapidly increasing costs associated with the discovery of new medicines, revolutionary advances in basic science and technology are reshaping the manner in which pharmaceutical research is conducted. For example, the use o f D N A microarrays facilitates the identification o f novel disease genes and also

opens up other interesting opportunities in disease diagnosis, pharmacogenomics and toxicological research (toxicogenomics). The development o f combinatorial chemistry and parallel

synthesis methods has increased both the quantity and chemical diversity o f potential leads against new targets. Our ability to discover useful leads has been greatly enhanced through astonishing advances in high-throughput screening (FITS) technologies. Through miniaturiza-

tion and robotics, we now have the capacity to screen millions o f compounds against therapeutic targets in very short period of time. Central to this new drug discovery paradigm is the rapid explosion o f computational techniques that allow us to analyze vast amount o f data, prioritize

H TS hits and guide lead optimization. The advances and applications o f computational methods in drug design are beginning to have a significant impact on the prosperity o f the pharma-

ceutical industry. Modern approaches to computer-aided molecular design fall into two general categories.