International Seminar

ACORUS CALAMUS L. ON TYPE 2 DIABETES MELLITUS MEDICATION

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Insilico screening chemical compounds alpha-glucosidase inhibitor from cordia myxa L.

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20th WCASET, Kuala Lumpur|Malaysia

NPASS Database

Modern drug discovery and today’s pharmacopeia is largely benefited from nature. More than 50% of approved drugs are natural products or natural product derivatives. It is estimated that there are about a million natural products have been isolated and many of them have been subjected to experimental assays to evaluate quantitative biological activities. However, there is a lack of an integrated datahouse to assemble these valuable data from individual literatures and provide

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HitPick

HitPick is a web server that facilitates the analysis of chemical screenings by identifying hits and predicting their molecular targets. The target prediction functionality can also be used in a stand-alone fashion.

For hit identification, the widely used B-score method (1) is applied.

For target prediction, HitPick applies a new approach that combines two 2D molecular similarity based methods, namely, simple 1-Nearest-Neighbour (1NN) similarity searching (2) and a machine learning method based on Laplacian-modified naive Bayesian models (3).

Ultrafast Chemistry

Screening Goes In Silico

In the last decade, a growing number of drug discovery researchers have replaced robots and reagents in their high-throughput screens with computer modeling, relying on software to identify compounds that will bind to a protein target of interest.

Researchers often combine virtual screening with other computational tools that make predictions about the activity of individual compounds, such as how they will interact with proteins. Together, these tools help narrow down large libraries of compounds into a subset to test experimentally. The biggest compound libraries boast several million molecules, an unrealistic load for the best-equipped lab to screen the old-fashioned way. Experimentally testing more modest libraries of thousands of molecules would still strain the resources of academic researchers, who are increasingly tackling drug discovery. “As an academic lab, I can’t afford to buy thousands of compounds to do a high-throughput screen, but I could afford to buy 10 or 20,” says Werner Geldenhuys, an associate professor of pharmaceutical sciences at Northeast Ohio Medical University.

Computational tools have their own challenges, however. Depending on the type of predictions the program makes and the size of your library, these screens could take hours to days to run. Some programs require users to perform basic coding. And of course, virtual hits have to be validated in the lab for their ability to actually bind to the target and modulate its activity.

Virtual Ligand Screening – part two

Virtual Ligand Screening – part one