Drug discovery by integration of pharmacophore modeling, virtual screening and biological evaluation by means of bioinformatics resources

Drug discovery is a step-by-step process very impor tant in biopharmaceutical field. We are interested in identifying new investigational drug-likes as potential inhibitors of determinate biologicaltherapeutic targets, trying to decrease the side effects and to safeguar d the human health [1-2]. However, it is a long and very expensive process [3]. Therefore, we are using a ne w computational strategy, based on Pharmacophore modeling, to select bioactive substances (natural or synthe tic), through the integration of bioinformatics onl ine tools and local resource and platforms, in order to include into the strategy also knowledge from high throughput studies, for new potential lead compounds generation-optimization, trying to accelerate the early phase of the drug development process [4]. The protocol of this new computational strategy is characterized by a multi-step design focused on: 1) screening in RCSB-PDB for a crystal structure of a specific biological target, suitable for the following steps; 2) pharmacophore modeling and virtual computational screening, by using public domain databases of bioactive compounds, as the ZINC 12 database [5], in order to find a promising molecule that could become a new potential medicine. 3) molecular and biological evaluation, to check the compounds selected by virtual screening, for their biological properties through public databases, as PubChem Compound, SciFinder, a nd Chemicalize to trace their origin and underline the ir most important physical-chemical features, PathP red (an enzyme-catalyzed metabolic pathway predictor server ) to highlight and identify their biosynthetic-meta bolic pathways and investigating the biotransformation of best candidates, analyzing their metabolites and t heir potential biological activity. Moreover, ADMET/toxi city predictor server applying the Lipinski-Veber f ilter are used to calculate the bioavailability the ADMET /toxicity properties. After this check, only molecules with good bioavail ab ity, good predicted activity and good ADMET properties are considered as hits compounds or rug-likes to direct the design of next experimental assays [ 6]. Finally, the lead compounds selected are analyzed t hrough molecular dynamics simulations. 4) simulations of molecular dynamics on the best lead compounds, to investigate atomic details of protein-compound m olecular interactions in different conditions (different org anic solutions, organisms and systems).