Mr. Amaan Arif

Amity University Uttar Pradesh
India

Abstract Title: AI-Powered Toxicity Profiling of Veterinary Drugs and Feed Additives: A Predictive Framework for Livestock Safety

Biography:

Amaan Arif is a passionate and multidisciplinary researcher with a strong academic foundation in Biotechnology, complemented by hands-on experience in bioinformatics, toxicology, artificial intelligence, and machine learning. His research interests lie at the intersection of life sciences and computational technology, with a focus on using AI-driven methods to solve complex biological problems, such as drug toxicity prediction, disease biomarker identification, genome analysis, and neurodevelopmental disorders. He has contributed to various projects involving transcriptomics, metabolomics, and microbial analysis, and is particularly interested in the application of deep learning for genome prediction, mental health research, and toxicological risk assessment. Amaan is also actively exploring novel approaches in antimicrobial resistance, cardiotoxicity, and structural biology, aiming to integrate data-driven methods into public health and pharmaceutical research.

Research Interest:

Background: Veterinary pharmacology increasingly relies on chemical agents and feed additives to enhance productivity and disease resistance in livestock. However, unintended toxic effects—ranging from hepatic and renal damage to neurological impairments—pose a significant threat to animal health, food safety, and regulatory compliance. Traditional toxicity assessments are time-consuming, ethically constrained, and often lack species-specific predictability. There is an urgent need for intelligent, scalable, and rapid screening methods to assess compound safety. Objective: This study aims to develop an AI-driven toxicity prediction framework that evaluates the toxic potential of veterinary drugs and feed additives, ensuring proactive risk management in livestock systems. Methods: A curated dataset of 1,200 compounds with known veterinary toxicological profiles was compiled from PubChem, TOXNET, and species-specific toxicology databases. Molecular descriptors, ADMET features, and physicochemical properties were extracted using RDKit. Machine learning models—including Random Forest, Support Vector Machines, and Deep Neural Networks—were trained to predict multi-organ toxicity endpoints (hepatotoxicity, nephrotoxicity, neurotoxicity) across species such as bovine, ovine, and avian livestock. Model performance was validated using ROC-AUC, precision-recall curves, and 10-fold cross-validation. Results: The ensemble deep learning model achieved an overall prediction accuracy of 91.3%, with high sensitivity and specificity across toxicity classes. Toxicity risk heatmaps and compound clustering revealed species-specific sensitivities, allowing informed compound selection and formulation design. Conclusion: This AI-powered toxicity profiling tool provides a rapid, non-invasive, and species-aware risk assessment platform for veterinary applications. Its adoption could reduce reliance on animal testing, accelerate regulatory decision-making, and improve animal welfare standards.