AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
Overall, this integrated strategy combining predictive modeling with FAERS-based signal comparison provides a useful framework for drug safety evaluation and mechanistic investigation of reproductive toxicity.
This review discusses recent progress in ML, DL, and AI-based strategies for toxicity prediction, assesses their utility across major toxicity endpoints, and highlights the challenges and future research opportunities in predictive toxicology.
A high-accuracy QSAR model is developed to accurately predict TR-related endocrine activity, thereby supporting the reduction of animal testing and regulatory decision-making and addresses key regulatory concerns through rigorous data ultra-curation and applicability domain assessment.
Over the past three decades, artificial intelligence (AI) and machine learning (ML) have revolutionized computational toxicology, providing powerful tools for predicting chemical toxicity and supporting safer assessments for human health and the environment. This review offers a critical 30-year synthesis (1995-2025) t...
The main advantage of the ToxCompl approach is that it drastically extends the toxicogenomic landscape into many data-poor tissues in the absence of acquiring additional experimental data, thereby allowing researchers to formulate mechanistic hypotheses about effects in tissues that have been underrepresented in the li...
This work shows that explainable machine learning offers accurate and interpretable molecular toxicity predictions to enable compound prioritization and computational chemical-safety assessment.
The machine learning-derived 12-gene signature, interpreted as CRC-associated genes overlapping with predicted BPA targets and supported by in silico molecular docking, offers valuable insights into the molecular basis of BPA-associated colorectal carcinogenesis and presents candidate targets for subsequent experimenta...
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