Mathematical and Computational Techniques for Drug Discovery: Promises and Developments.

Area of Science:

• Computational chemistry and bioinformatics
• Mathematical modeling in drug discovery
• Systems biology and virology

Background:

• Drug discovery for viral infections faces challenges in identifying effective treatments.
• Understanding complex molecular interactions is crucial for developing new therapeutics.
• Existing methods may not fully capture the intricate dynamics of protein-target binding.

Purpose of the Study:

• To review mathematical and computational techniques for drug discovery, particularly for viral infections.
• To explore the application of group theory, topology, and combinatorics in molecular interactions.
• To highlight the utility of computational simulations in identifying novel drug candidates and binding sites.

Main Methods:

• Review of topological, combinatorial, graph, and knot theory applications.
• Analysis of group theoretical techniques for phylogeny and protein dynamics.
• Integration of quantum chemical computations (QM/MM ONIOM) and molecular dynamics simulations.
• Examination of specific case studies involving Hepatitis C Virus, Dengue Virus, and HIV-1.

Main Results:

• Mathematical and computational approaches provide powerful tools for characterizing molecular interactions.
• Group theory and topological methods aid in understanding protein dynamics and DNA permutations.
• Computational simulations offer insights into protein-drug interactions and receptor binding.
• Studies reveal the existence of multiple binding sites, suggesting allosteric approaches.

Conclusions:

• Combinatorial and computational techniques, combined with experimental data, accelerate drug discovery.
• These methods underscore the importance of considering multiple or allosteric binding sites for effective drug design.
• The reviewed techniques offer a promising framework for developing novel therapeutics against viral diseases.