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                  4D-QSAR IN DRUG DESIGN

Computational methods play a crucial role in modern medicinal chemistry, presenting a unique potential for transforming the early phases of drug research, particularly in terms of time and cost savings. Most of the techniques used in structure-based drug design have experienced significant improvements in the past few years, resulting in a remarkable enhancement of the speed and the efficacy of this approach. The successful application of 4D-QSAR models to generate 3D pharmacophores of ligand-receptor data sets, to analyze and design of virtual libraries, showing that it can be a powerful tool in the early stages of drug discovery process. 4D-QSAR analysis can also be applied to non-medicinal chemistry and biological problems. One such example in materials science is to predict how chelators will bind metal ions both in solution and on surfaces. The practical applications are to design chelators that selectively remove specific ions from solutions and surfaces. Real world examples are keeping the walls of the tanks of hot water heater clean, swimming pool liners clean and making 'hard' water 'softer' by removing divalent ions like Ca++.
4D-QSAR:
As an evolution of Molecular Shape Analysis (MSA)], 4D-QSAR formalism, which includes the conformational flexibility and the freedom of alignment by ensemble averaging in the conventional 3dimensional descriptors found in old 3D-QSAR methods. Thus the 4th Dimension. of the method ensemble sampling the spatial features of the members of a training set.
The grid cell occupancy descriptors are generated for a number of different atom types, called interaction pharmacophore elements, IPEs. These IPEs, defined as “any type”, “nonpolar”, “polar-positive charge”, “polar-negative charge”, “hydrogen bond acceptor”, “hydrogen bond donor”, and “aromatic”, correspond to the interactions that may occur in the active site, and are related to the pharmacophore groups. IPEs are related to the descriptors nature in 4D-QSAR analysis. The sampling process allows the construction of optimized dynamic spatial QSAR models in the form of 3D pharmacophores, which are dependent on conformation, alignment, and pharmacophore grouping.
Quantitative structure-activity relationships (QSAR) play a vital role in modern drug design, since they represent a much cheaper and rapid alternative to the medium throughput in vitro and low throughput in vivo assays which are generally restricted to later in the discovery cascade. One would say that nowadays no drug is developed without previous QSAR analyses.

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