3 D Enantioselective Descriptors for Ligand - Based Computer - Aided Drug Design

4. ROC and PPV results for the feature forward analysis with the control set of features compared with the control set combined with EMAS features .. Introduction On October 5, 1981, Fortune magazine published a cover article entitled the " Next Industrial Revolution: Designing Drugs by Computer at Merck " [1]. Some have credited this as being the start of intense interest in the potential for Computer Aided Drug Design (CADD). While progress was being made in CADD, the potential for high-throughput screening (HTS) had begun to take precedence as a means for finding novel therapeutics. This brute force approach relies on automation to screen high numbers of molecules in search of those which elicit the desired biological response. This method requires little compound design or prior knowledge and the efficiency of technologies required to screen large libraries continues to increase. However, while traditional HTS is often successful in the discovery of multiple lead compounds, the hit rate for this method is extremely low. This low hit rate has limited the usage of HTS to research programs capable of screening very large compound libraries. In the past decade, CADD has reemerged as a way to significantly decrease the number of compounds necessary to screen while retaining the same level of lead compound discovery. CADD techniques allow compounds predicted as inactive to be skipped and those predicted as active to be prioritized. This reduces the cost and workload of a full HTS screen without sacrificing lead discovery. For example, researchers at Pharmacia (now part of Pfizer) used CADD tools to screen for inhibitors of tyrosine phosphatase-1B, an enzyme implicated in diabetes. Their CADD-based virtual screen yielded 365 compounds, 127 of which showed effective inhibition, a hit rate of nearly 35%. Simultaneously, this group performed a traditional HTS against the same target. Of the 400,000 2 compounds tested, 81 showed inhibition, producing a hit rate of only .021%. This comparative case effectively displays the power of CADD for reducing the number of compounds necessary to test for hit discover [2]. CADD has already been used in the discovery of compounds which have passed clinical trials and become novel therapeutics in use for the treatment of a variety of diseases. Some of the earliest examples of approved drugs that owe their discovery in large part to the tools of CADD include the carbonic anhydrase inhibitor dorzolamide, approved in 1995 [3], the ACE inhibitor …