Method for accelerating AutoDock Vina based on GPU

Abstract

The invention discloses a method and implementation for accelerating AutoDock Vina based on a GPU (Graphics Processing Unit). According to the method, the parallel scale of butt-joint threads in a Monte Carlo iterative local search method is expanded by increasing the number of initial random ligand conformations, and the number of search steps in the Monte Carlo iterative local search method is reduced; a heterogeneous parallel structure of a CPU (Central Processing Unit) and a GPU (Graphics Processing Unit) in AutoDock Vina is realized by utilizing OpenCL, so that higher-level parallel and acceleration capabilities are achieved. Modern drug screening needs to face massive compound data, and the current molecular docking technology cannot meet the actual demand. By adopting the method disclosed by the invention, the molecular docking and virtual screening speed can be greatly accelerated while the docking precision is not lost, and the actual demand of molecular docking of a large compound database is met.

Description

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AI Technical Summary

Benefits of technology

This technology allows scientists to quickly find new drugs by searching through databases containing thousands of different chemical structures called combinatorial libraries (also known as chemistry collections). By doing this they have fewer steps needed compared to traditional ways like testing them manually. They also allow users to easily access these library resources without having to invest more money than necessary due to their expensive hardware components. Overall, this innovation makes medicine quicker and easier to develop at scale.

Problems solved by technology

This patents discusses different methods or techniques related to improving the efficiency and accuracy of searching unknown organic compounds during pharmaceutical discovery processes. These improvements aim to increase the time required by scientists to find specific ones while minimising their impacts upon future clinically relevant treatments. Current computational platforms have limitations due to memory requirements and processing capabilities, making them slow down when trying out novel leads. To address these issues, there exists various computerized algorithms called autoDeck Viruses® that allow faster analysis without sacrificial effort.

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