title: Computational analysis and In-silico predictive modeling for inhibitors of PhoP regulon in S. typhi on high-throughput screening bioassay dataset.
abstract: The emergence of multidrug resistant salmonella enterica serotype typhi in pandemic proportions throughout the world and therefore there is a necessity to speed up the discovery of novel molecules having different modes of action and also less influenced to the resistance formation that would be used as drug for the treatment of salmonellosis particularly typhoid fever. The PhoP regulon is well studied and have now been shown to be critical regulator of number of gene expression whose required for intracellular survival of S.enterica and pathophysiology of disease like typhoid. The evident roles of two component PhoP/PhoQ-regulated products in salmonella virulence have motivated attempts to target them as therapeutically. Although the discovery process of biologically active compounds for the treatment of typhoid rely on hit finding procedure using high throughput screening technology alone is very expensive, as well as time consuming when performed on large scales. With the recent advancement in combinatorial chemistry and contemporary technique for compounds synthesis, there are more and more compounds become available that gives ample growth of diverse compound library, but the time and endeavor required to screen these unfocused massive and diverse library has been slightly reduced in the past years. Hence there is demand to improve the high quality hits and success rate for high-throughput screening (HTS) that required focused and biased compound library towards the particular target. Therefore we still need an advantageous and expedient method to prioritize the molecules that will be utilized for biological screens which save time and also inexpensive. In this concept In-silico methods like Machine Learning are widely applicable technique used to build computational model for high-throughput virtual screens to prioritize molecules for advance study. Furthermore in computational analysis we extended our study to identify the common enriched structural entities among the biologically active compound towards find out the privileged scaffold.
authors.affiliation: Department of Computer Science, Hamdard University, New Delhi, India, harleen_k1@rediffmail.com.
authors.forename: Harleen
authors.initials: H
authors.lastname: Kaur
authors.forename: Mohd
authors.initials: M
authors.lastname: Ahmad
authors.forename: Vinod
authors.initials: V
authors.lastname: Scaria