Enhancing Predictive Toxicology with GPU-Enhanced Computational Biology

EasyChair Preprint 14168

Abstract

Predictive toxicology, a field essential for assessing the safety of chemical substances, has traditionally relied on in vitro and in vivo methods, which are often time-consuming and costly. The advent of computational biology and machine learning techniques has revolutionized this domain by providing efficient and accurate predictive models. However, the sheer volume and complexity of toxicological data necessitate advanced computational power. This study explores the integration of GPU (Graphics Processing Unit) acceleration in computational biology to enhance predictive toxicology. Leveraging the parallel processing capabilities of GPUs, we develop and evaluate machine learning models capable of rapidly analyzing large-scale toxicological datasets. Our findings demonstrate significant improvements in prediction accuracy and computational efficiency, enabling real-time analysis and decision-making. The application of GPU-accelerated techniques not only expedites the toxicological assessment process but also enhances the scalability and robustness of predictive models. This advancement holds promise for more effective risk assessment, reduced reliance on animal testing, and accelerated development of safer chemicals and pharmaceuticals.

Keyphrases: Graphics Processing Units (GPUs), Toxicology, computational biology

@booklet{EasyChair:14168,
  author    = {Abi Litty},
  title     = {Enhancing Predictive Toxicology with GPU-Enhanced Computational Biology},
  howpublished = {EasyChair Preprint 14168},
  year      = {EasyChair, 2024}}