Current Topics in Medicinal Chemistry - Volume 14, Issue 16, 2014

Identifying the novel and potential starting lead compounds remains major challenge in drug discovery industry. In 90’s, high-throughput screening is a common practice in early stage of a project; screening of large number compounds to identify potential starting lead compounds which can modulate target of their interest. Although high-throughput screening has some remarkable successes in identifying potential drug leads Read More

Each year, a huge number of new cases accounts of TB with added problems due to multidrug resistant TB varieties. Globally, TB is one of the top causes of loss of life among people living with HIV who are more likely than others to get TB infection. Current TB treatment includes long term administration of cocktail of drugs; hence, the development of an alternative armamentarium against TB is the primary requirement. In Read More

Influenza is a seasonal and serious health threat, and the recent outbreak of H7N9 following the pandemic spread of H1N1 in 2009 has served to emphasize the importance of anti-influenza drug discovery. Zanamivir (Relenza™) and oseltamivir (Tamiflu®) are two antiviral drugs currently recommended by the CDC for treating influenza. Both are examples of the successful application of structure-based drug design strateg Read More

De novo drug design methods such as receptor or protein based pharmacophore modeling present a unique opportunity to generate novel ligands by employing the potential binding sites even when no explicit ligand information is known for a particular target. Recent developments in molecular modeling programs have enhanced the ability of early programs such as LUDI or Pocket that not only identify the key interacti Read More

Virtual screening (VS) techniques are well-established tools in the modern drug discovery process, mainly used for hit finding in drug discovery. The availability of knowledge of structural information, which includes an increasing number of 3D protein structures and the readiness of free databases of commercially available smallmolecules, provides a broad platform for VS. This review summarizes the current developme Read More

Machine learning (ML) computational methods for predicting compounds with pharmacological activity, specific pharmacodynamic and ADMET (absorption, distribution, metabolism, excretion and toxicity) properties are being increasingly applied in drug discovery and evaluation. Recently, machine learning techniques such as artificial neural networks, support vector machines and genetic programming have been explored Read More

Structure-based drug discovery (SBDD) is becoming an essential tool in assisting fast and cost-efficient lead discovery and optimization. The application of rational, structure-based drug design is proven to be more efficient than the traditional way of drug discovery since it aims to understand the molecular basis of a disease and utilizes the knowledge of the three-dimensional structure of the biological target in the Read More