STRATEGIES IN COMPUTER-AIDED DRUG DESIGNING

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Target Identification is a field that integrates different levels of information in drug – protein and protein – disease networks and involves an intricate network of databases and correlations across genomics , proteomics, transcriptomics, metabolomics, microbiome and pharmacogenomics.
There are two approaches that can be used for target identification: the first is at the beginning (target-based or reverse chemical genetics) and the second is at the end (phenotype-based or forward chemical genetics) Biological screening.
Target selection can be done in three ways: biochemical, genetic interaction, and computational inference.
Biochemical affinity purification directly affects the target of small molecules through biochemical approaches.
However, lead optimization is good at the availability of target structure information with the help of enzymes assays and other biochemical tests.
Various DNA and RNA-based methods allow target identification via genetic or genomic methods for the functional analysis of protein targets in the controlled system.
Numerous computational inference methods have also been reported for the predictions of targets of identified inhibitors using structural-based and (ligand-based) profiling methods.

In the early stages of the drug development process, researchers faced challenges due to little or no information on the Structure – Activity Relationship (SAR) information.
The development of HTR techniques has now made it easier for chemists to develop the assay and screening of lead compounds.
The selection of lead compounds is based on a set of compounds that have diversity in their physiochemical properties and are easier to select rather than randomly selected.
The aim of these analyses is to select and test less compounds while obtaining as much information as possible about the data set.
The selection of compounds to be tested is of primary importance as it has a strong impact on the number of compounds tested for research efficiency and also on the cost implications.
Recent research on refinements and lead findings has been conducted on the rational design of drugs for the structural diversity of databases.
Three-dimensional diversity enhancement databases were compared with hierarchical clustering and maximum dissimilarity methods.
Rational selection and random approaches were compared for the performance of two-dimensional fingerprints as a validated molecular descriptor.
This comparison leads to a choice of pharmacophores. The Pharmacophore model is a spatial arrangement of atoms or functional groups that explains the linkage of structurally diverse ligands to a common receptor site.

A bioassay or bioassay is defined as a test procedure that is used to estimate the concentration of a pharmaceutically active substance in a formulated product or bulk material.
Of the different physical or chemical methods available, a bioassay provides exhaustive information on the biological activity of a substance.
CADD-based drug target identification can be divided into drug-based similarity inference (DBSI), target-based similarity inference (TBSI) and network-based inference (NBI). The NBI method is considered to be the best method among them.
These in vitro assays have shown that the five old drugs, namely montelukast, diclofenac, simvastatin, ketoconazole and itraconazole, exhibit polypharmacological properties on oestrogen receptors or dipeptidyl peptidase-IV.
Target prediction may also be performed by receptor-based methods, such as reverse docking, which have also been used in drug-target (DT) binding affinity predictions. The availability of a 3D structure is the limitation of the target prediction.

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