ROCS-derived features for virtual screening

Graph Mining for Virtual Screening

In drug discovery, one key step is to predict chemical activities of drug candidates such as toxicity, mutagenicity and solubility and thereby reduce the number of chemical compounds screened by actual experimental means. Such computational prediction is called “virtual screening”. Drug candidates are small molecules which are represented as attributed graphs where atoms and bonds correspond to...

Novel technologies for virtual screening.

There are several methods for virtual screening of databases of small organic compounds to find tight binders to a given protein target. Recent reviews in Drug Discovery Today have concentrated on screening by docking and by pharmacophore searching. Here, we complement these reviews by focusing on virtual screening methods that are based on analyzing ligand similarity on a structural level. Spe...

An Enhancement of Bayesian Inference Network for Ligand-Based Virtual Screening using Features Selection

Problem statement: Similarity based Virtual Screening (VS) deals with a large amount of data containing irrelevant and/or redundant fragments or features. Recent use of Bayesian network as an alternative for existing tools for similarity based VS has received noticeable attention of the researchers in the field of chemoinformatics. Approach: To this end, different models of Bayesian network hav...

Binary ROCs and Their Implications for the Measurement of Memory

Pharmacophore Based Virtual Screening Approach to Identify Selective PDE4B Inhibitors

Phosphodiesterase 4 (PDE4) has been established as a promising target in asthma andchronic obstructive pulmonary disease. PDE4B subtype selective inhibitors are known toreduce the dose limiting adverse effect associated with non-selective PDE4B inhibitors. Thismakes the development of PDE4B subtype selective inhibitors a desirable research goal. Toachieve this goal, ligand based pharmacophore m...