Physical Sciences and Mathematics Commons^™

How Low Can You Go? Feature Selection For Drug Discovery, Derek Jones, Sally R. Ellingson, W. A. De Jong

Commonwealth Computational Summit

The cost of bringing a drug to market depends on how quickly a candidate drug can be “discovered” and evaluated to ensure safety and effectiveness. In this work we develop a method for predicting whether a given drug and protein compound will “bind.” Our aim is to select a set of features to predict drug-protein interactions.

This study focuses on kinases. Kinase inhibitors are the largest class of new cancer therapies. Selective inhibition is difficult due to high sequence similarity, leading to off-target interactions and side-effects. Pictured here human c-SRC.

Structure-Based Drug Discovery: Computational Virtual Screening, Robert C. Monsen, Lynn Deleeuw, Jon Maguire, William L. Dean, Robert D. Gray, Jonathan B. Chaires, John O. Trent

Commonwealth Computational Summit

No abstract provided.

Parallelization Of Molecular Docking Algorithms Using Cuda For Use In Drug Discovery, Brandon Stewart, Jonathan Fine, Gaurav Chopra Phd

The Summer Undergraduate Research Fellowship (SURF) Symposium

Traditional drug discovery methodology uses a multitude of software packages to design and evaluate new drug-like compounds. While software packages implement a wide variety of methods, the serial (i.e. single core) implementation for many of these algorithms, prohibit large scale docking, such as proteome-wide docking (i.e. thousands of compounds with thousands of proteins). Several docking algorithms can be parallelized, significantly reducing the runtime of the calculations, thus enabling large-scale docking. Implementing algorithms that take advantage of the distributed nature of graphical processing units (GPUs) via the Compute Unified Device Architecture (CUDA) enables us to efficiently implement massively parallel algorithms. Two …