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libraries that are then tested against a variety of biological targets. This design strategy is suited for lead identification programs. Thus, two categories of computational tools should be developed and validated to meet the needs of the two different types of projects.

In a targeted screening project, computational library design involves the selection of a subset of chemical building blocks from an available pool of chemical structures. This subset of selected building blocks affords a limited virtual library with a high content of compounds similar to a lead molecule. Molecular similarity is quantified by using a chosen set of molecular descriptors and similarity metrics [64,65]. Building blocks can also be chosen such that the resulting virtual library could have a high percentage of compounds that are predicted to be active from a preconstructed QSAR model [66]. When the structure of the biological target is known, one can select building blocks such that the resulting library compounds are stereochemically complementary to the binding site structure of the underlying target [67,68]. Other approaches to targeted library design using different criteria have also been considered [69]. Similar approaches have long been used in targeted database mining that were based on the principle of either molecular similarity [70,71] or structure-based drug design [72]. In a broad screening project, computational library design or database mining involves the selection of a subset of compounds that are optimally diverse and representative of available classes of compounds, leading to a nonredundant chemical library or a set of nonredundant compounds for biological testing. Reported methods can be generally classified into several categories.

1.Cluster sampling methods, which first identify a set of compound clusters, followed by the selection of several compounds from each cluster [73].

2.Grid-based sampling, which places all the compounds into a low-dimensional descriptor space divided into many cells and then chooses a few compounds from each cell [74].

3.Direct sampling methods, which try to obtain a subset of optimally diverse compounds from an available pool by directly analyzing the diversity of the selected molecules [75,76].

Many reports have been published that address various aspects of diversity analysis in the context of chemical library design and database mining [77–84].

IV. SUMMARY AND CONCLUSIONS

We have reviewed several methods of computer-aided drug design that are formally divided into ligand-based and receptor-based methods. Ligand-based methods of analysis are used widely because they are not very computationally intensive and afford rapid generation of QSAR models from which the biological activity of newly designed compounds can be predicted. Most of the existing methods require the generation of a 3D pharmacophore hypothesis (i.e., unique 3D arrangements of important functional groups common to all or the majority of the receptor ligands). In many cases, when the receptor ligands are not very structurally diverse and include conformationally rigid compounds, a pharmacophore can be generated in a reasonably unbiased and unique way, using either automated (e.g., DISCO [85]) or semiautomated [3] pharmacophore prediction methods. The pharmacophore hypothesis can help medicinal chemists gain an insight into the key interactions between ligand and receptor when a 3D ligand–receptor complex structure has not been determined. It can be used as the search query for 3D database mining, and

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this approach has been demonstrated as very productive for lead compound discovery [86]. It can also be used for 3D QSAR analysis, grouping together the compounds that follow the same binding mode and indicating the possible 3D alignment rules [87]. However, this task of unique pharmacophore generation becomes less feasible for more structurally diverse and/or conformationally flexible compounds. In general, in the absence of detailed structural information about the receptor binding site, any pharmacophore inferred from only the ligand structure remains hypothetical.

Structure-based design methods present an appealing alternative to more traditional approaches to CADD. Molecular docking algorithms can generate fairly accurate orientations of known or designed receptor ligands in the active site (e.g., GRID [88], DOCK [16]). However, their application in practice is limited by the availability of macromolecular target structure. The protein and nucleic acid structural databases have been growing exponentially over the past decade, with the size of the Protein Data Bank (PDB) [89] currently exceeding 10,000 entries. Nevertheless, many of these entries are highly homologous proteins, and the structure of many pharmacologically important molecules such as transmembrane receptor proteins cannot be resolved experimentally at the present time. Furthermore, predictions of the binding affinity of receptor ligands are fast but rather inaccurate because the scoring functions used in docking calculations are not robust enough. Alternatively, these predictions can be fairly accurate but very computationally intensive when free energy simulation methods are used [21,90]. Therefore, because of these practical limitations, ligand-based design methods will probably remain the main arena of CADD efforts, supplemented, when possible, by structure-based approaches.

We have shown that successful molecular modeling and the design of new drugs can be achieved by integration of different computational chemistry and molecular modeling techniques. Owing to the rapid increase in computational power due to advances in both hardware and software, molecular modeling has become an important integral part of multidisciplinary efforts in the design and synthesis of new potent pharmaceuticals. Practical knowledge of these techniques and their limitations is a necessary component of modern research in drug discovery.

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