Deep Learning for Predictive Toxicology Assessment Early Detection of Adverse Drug Reactions.
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Abstract
Predicting adverse drug reactions (ADRs) early in the drug development process is crucial for ensuring drug safety and reducing costly late-stage failures. Traditional methods for toxicity assessment rely heavily on animal testing and empirical observations, which are often time-consuming, expensive, and ethically questionable. In recent years, deep learning techniques have emerged as powerful tools for predictive toxicology, offering the potential to accelerate the identification of potential ADRs while reducing reliance on animal models.This paper reviews the current state-of-the-art deep learning approaches for predictive toxicology assessment, focusing on their applications in the early detection of ADRs. [1],[2] We discuss various deep learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and graph neural networks (GNNs), and their utilization in analyzing diverse data types such as chemical structures, omics data, and adverse event reports.Furthermore, we examine the challenges and limitations associated with deep learning-based predictive toxicology, including data availability, model interpretability, and regulatory acceptance. We also explore ongoing efforts to address these challenges, such as the development of standardized datasets, explainable AI techniques, and collaborations between academia, industry, and regulatory agencies.Overall, this paper highlights the potential of deep learning for early detection of ADRs in drug development and underscores the need for continued research and collaboration to realize the full benefits of these techniques in ensuring drug safety and improving public health.