High-Throughput Protein-Ligand Docking Using GPU-Accelerated Machine Learning

EasyChair Preprint no. 13849

Abstract

The rapid advancement of high-throughput protein-ligand docking has revolutionized drug discovery and design, significantly enhancing the efficiency and accuracy of identifying potential therapeutic compounds. However, traditional computational methods often struggle with the sheer volume and complexity of the data involved. This paper explores the transformative potential of GPU-accelerated machine learning in protein-ligand docking, presenting a novel approach that leverages the immense parallel processing power of modern GPUs. By integrating advanced deep learning algorithms with high-throughput docking simulations, our method achieves unprecedented speed and precision in predicting binding affinities and identifying promising drug candidates. We demonstrate the efficacy of our approach through extensive benchmarking against conventional techniques, highlighting substantial improvements in computational efficiency and predictive accuracy. Our findings underscore the critical role of GPU-accelerated machine learning in streamlining the drug discovery pipeline, paving the way for faster and more cost-effective development of new pharmaceuticals

Keyphrases: Accelerated sequence analysis, Bioinformatic algorithms, Computational genomics, Computational Proteomics, Deep learning in bioinformatics, Genomic data processing, GPU-accelerated machine learning, GPU-based bioinformatics, High Performance Computing, Machine learning in computational biology

@Booklet{EasyChair:13849,
  author = {Abey Litty},
  title = {High-Throughput Protein-Ligand Docking Using GPU-Accelerated Machine Learning},
  howpublished = {EasyChair Preprint no. 13849},

  year = {EasyChair, 2024}}