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GPU-accelerated Chemical Similarity Assessment for Large Scale Databases.

Marco Maggioni¹, Marco Domenico Santambrogio², Jie Liang¹

¹Department of Computer Science, University of Illinois at Chicago ; Department of Bioengineering, University of Illinois at Chicago.

Procedia Computer Science

|January 1, 2011

Summary

This study demonstrates that Graphics Processing Units (GPUs) significantly accelerate chemical similarity calculations. Leveraging many-core architectures offers a substantial speed-up for chemoinformatics tasks, enhancing database searching and virtual screening.

Keywords:

GPU Tanimoto coefficient chemical fingerprints chemical similarity chemoinformatics

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Area of Science:

Chemoinformatics
Computational Chemistry
High-Performance Computing

Background:

Chemical similarity assessment is fundamental in chemoinformatics for database searching, virtual screening, and compound clustering.
Large-scale chemical databases pose computational challenges for traditional chemoinformatics algorithms, necessitating faster similarity methods.

Purpose of the Study:

To analyze the benefits of many-core architectures, specifically Graphics Processing Units (GPUs), for calculating common chemical similarity coefficients.
To present a proof-of-concept for the application of GPU computing in chemoinformatics.

Main Methods:

Developed a general GPU algorithm for all-to-all chemical comparisons using binary fingerprints and floating-point descriptors.
Applied optimization techniques to reduce global memory access and enhance computational efficiency.
Tested the algorithm on diverse GPU hardware, from low-end to high-performance systems.

Main Results:

Achieved significant speed-ups on a laptop with a low-end GPU: 4-6x for fingerprints and 4-7x for descriptors compared to single-core implementations.
Observed substantial speed-ups on a desktop with a performant GPU: 195-206x for fingerprints and 100-328x for descriptors.
Demonstrated the effectiveness of GPU acceleration for large-scale chemical similarity computations.

Conclusions:

GPU architectures offer a powerful and efficient solution for accelerating chemoinformatics tasks, particularly chemical similarity calculations.
The proposed GPU algorithm and optimizations provide a viable approach to handle the computational demands of modern large-scale chemical databases.