Optimizing Autodock with CUDA

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AutoDock is a molecular docking program which fits and scores virtual ligands into protein targets to screen for potential new drugs. Each drug to protein evaluation is a time consuming process for which there are many speed to accuracy trade-offs, especially when screening against millions of compounds. Clearly any speed increases of drug docking can lead to greater numbers of compounds screened, or allow the each drug evaluation to be carried out at a higher level of confidence and accuracy. Porting AutoDock to utilise GPU architecture could potentially increase its performance, thus making a significant contribution to the continuing search for new, biologically active drugs.

Pharmaceutical drugs are typically small molecules of less that 300 molecular weight that bind to specific protein targets to induce physiological effects, such as aspirin inhibiting the cyclooxygenase enzyme to provide pain relief. The physical screening of such drugs is an extremely costly and time-consuming exercise so there has been great interest for an effective in silico approach to help search for lead compounds for further drug development. Virtual docking of drugs into a protein target is a complex shape-fitting problem which is compounded by electrostatic and solvation effects, as well as that both the ligand and target can change conformation. Download free Optimizing Autodock with CUDA.pdf here

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