Of the 400,000 compounds tested, 81 showed inhibition, producing a hit rate of only 0.021%. Simultaneously, this group performed a traditional HTS against the same target. Their virtual screen yielded 365 compounds, 127 of which showed effective inhibition, a hit rate of nearly 35%. The fact that both of these tools can be used in parallel provides an additional benefit for CADD in a drug discovery project.įor example, researchers at Pharmacia (now part of Pfizer) used CADD tools to screen for inhibitors of tyrosine phosphatase-1B, an enzyme implicated in diabetes. Because CADD requires significantly less preparation time, experimenters can perform CADD studies while the traditional HTS assay is being prepared. Additionally, traditional HTS assays often require extensive development and validation before they can be used.
PLS CADD METHOD 3 VS METHOD 4 FULL
This reduces the cost and workload of a full HTS screen without compromising lead discovery. Many compounds predicted to be inactive can be skipped, and those predicted to be active can be prioritized. 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. This low hit rate has limited the usage of HTS to research programs capable of screening large compound libraries. However, although traditional HTS often results in multiple hit compounds, some of which are capable of being modified into a lead and later a novel therapeutic, the hit rate for HTS is often extremely low.
The method has the advantage of requiring minimal compound design or prior knowledge, and technologies required to screen large libraries have become more efficient.
This brute force approach relies on automation to screen high numbers of molecules in search of those that elicit the desired biologic response. Although 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. Some have credited this as being the start of intense interest in the potential for computer-aided drug design (CADD). On October 5, 1981, Fortune magazine published a cover article entitled the “Next Industrial Revolution: Designing Drugs by Computer at Merck” ( Van Drie, 2007). Finally, computational methods for toxicity prediction and optimization for favorable physiologic properties are discussed with successful examples from literature. In addition, important tools such as target/ligand data bases, homology modeling, ligand fingerprint methods, etc., necessary for successful implementation of various computer-aided drug discovery/design methods in a drug discovery campaign are discussed. We review widely used ligand-based methods such as ligand-based pharmacophores, molecular descriptors, and quantitative structure-activity relationships. Ligand-based methods use only ligand information for predicting activity depending on its similarity/dissimilarity to previously known active ligands. The article discusses theory behind the most important methods and recent successful applications.
Structure-based approaches include ligand docking, pharmacophore, and ligand design methods. Structure-based methods are in principle analogous to high-throughput screening in that both target and ligand structure information is imperative. These methods are broadly classified as either structure-based or ligand-based methods. Computer-aided drug discovery/design methods have played a major role in the development of therapeutically important small molecules for over three decades.