Normally leads were discovered from natural products, biochemistry or exploring analogs of know substrates or ligands. Nowadays, computational methods are beginning to play a major role.

Only in the last few years: structure available during the drug discovery process. Membrane-bound proteins specially difficult to find.

Computer-aided drug design:

• Analog-based: uses pharmacophores (an explicit geometric hypothesis of the critical features of a ligand) and QSAR (quantitative structure-activity relationships), based solely on their chemical structure: the linear free energy principle.
• Structure-based: starting from the 3D-structure of the target (by X-ray crystallography, NMR spectrocopy, computer homology methods or ab initio methods), the binding site is located by comparison or homology. Conformational analysis: lowest energy conformations when free in solution and when bound to the receptor.

Two main approaches:

1. Docking: characterization of the ligand, sampling: positioning (configurational) and conformational states [FFT], and scoring: energetic evaluation of each discrete geometry.
2. De novo: construction of molecules that have not been synthesized previously. Three methods: a) fragment placement: focus on a small number of well-placed fragments, b) connection methods: the linker provides a compatible geometry for connecting the critical fragments, c) sequential growth: a step-by-step (starting with a seed) construction of a hypothetical ligand within a binding pocket.

Virtual screening: build a library to priorize experimental efforts.

Structural Bioinformatics on Drug DiscoveryTarget Assessment:

Target druggability: the energetically optimal protein would be spherical, with all its hydrophobic residues pointing inward. A quantitative approach is the rule of five (Lipinski):A compound is likely to show poor absorption or permeation if:

• It has more than five hydrogen bond donors

• The molecular weight is over 500

• The Clog P (calculated octanol/water partition coefficient) is over five

• The sum of nitrogens and oxygens is over 10

• Weak inhibition (<100nM) is observed

  Another physicochemical complementary properties: surface area and volume of the pocket, hydrophobicity and hydrophilic character, curvature and shape of the pocket.

  • Target Triage: computer-aided target selection (CATS), based on the importance of a gene for the organism, the occurrence of the gene in multiple target species, specificity or inhibition by reference to sequence similarity, and easiness of assay. The group of residues lining a ligand-binding site are of moreimportance than long-range interaction and conformational changes.

  • Target Validation: knock-out of the gene of interest or RNA antisense technology to inactivate the gene.Lead Identification: structural bioinformatics can be used for function and ligand prediction. Using structural similarity to find chemical leads (usually, when the proteins share less than 30% sequence identity the active sites are nonidentical).

  • Virtual screening: to derive a pharmacophore describing the functionally important sites in a ligand-binding site, and docking and scoring. Creating a chemical library.

  • Lead Optimization: repeated cycles of determining the structure of the target in complex with a number of lead compounds and their analogs. Structural bioinformatics should be used to design suitable constructs of the outset of the project. Alignment and secondary structure prediction on multiple alignments. Homology modeling and the use of a surrogate protein (an orthologous protein from another species or a similar member of the same gene family).

  • ADMET Modeling: additional parameters that can affect the biopharmaceutical and safety properties of the drug: in vivo absorption, distribution, metabolism, excretion, and toxicology. Sequence-structure relationship and protein homology modeling can be used in ADMET modeling.

The bioinformatics industry is among one of the largest sector within super-computing and stands to benefit appreciably from acceleration of FPGA-based applications. Bioinformatics involves annotation, recording, analysis, storage, and searching/extracting of molecular information such as protein sequences, gene sequences, cell activity and variation in genetic structures.

Bioinformatics is mainly used in area of life sciences like molecular medicine, drug discovery, microbial genome applications, comparative studies, agriculture and others. The contribution of Bioinformatics to these areas of life sciences is evident by its application in the research fields e.g. proteomics, genomics.

In the near future the contribution of genomics in the field of drug discovery and development is predicted to be one of the highest. Over the long haul, increasing number of new drugs designed would be genomic-related. However, after human genome mapping the use of proteomics is likely to rise at a higher rate than that of genomics, as it is proteins, not genes, which the researchers would use in the coming years for developing new drugs.

The growth of the Bioinformatics market is stable as 20% of the use is based upon proteomics and genomics. Many Research and development centers across the world are looking for a base to transfer the data, information and knowledge to facilitate an informatics-based decision support system.

However, biotechnology and pharmaceutical companies will continue to look at the tools for understanding the association between biological data types. As bioinformatics is broadly more useful to discovery and development, the end users will adopt solutions that streamline tracking, access, sharing of data and interpretation of different data types with least impact on resources. In consequence, hundreds of suppliers are embarking upon the bioinformatics bandwagon.

For further information about bioinformatics market please read the report “Bioinformatics Market Update (2006)” published by RNCOS at http://www.rncos.com/Report/COM31.htm