Title : Atomic watchtower: a multi-modal machine learning framework for swift antimicrobial resistance prediction using whole genome sequencing and SMILES-based molecular mapping

Abstract:

The 21st century’s most urgent global health challenge is Antimicrobial resistance (AMR) as it is predicted that AMR will lead to 10 million deaths every year by 2050. However, the conventional diagnostic approaches take 48 to 72 hours to yield results leading to a dangerous information gap and causing the misuse of broad-spectrum antibiotics. Atomic Watchtower is a machine learning framework that integrates Whole Genome Sequencing (WGS) k-mer features with drug structure analysis via SMILES. In this way, we hope to close the gap between drug and genome molecular phenotyping. The resistance is viewed as a structural matching problem and a dual layer has been used. One layer uses a Random Forest Regressor to predict the Minimum Inhibitory Concentration (MIC) value. The second layer uses Deep Purpose library for the drug-target interaction prediction. The validated cohort of 491 Indian clinical isolates yielded a median AUROC of 0.90 as well as a ciprofloxacin resistance prediction accuracy of 0.99. The use of a privacy-preserving Federated Learning framework for cross-border intelligence with HIPAA compliance realizes novelty. According to our Streamlit dashboard, Atomic Watchtower can generate actionable resistance risk assessments in less than 4 hours, turning a guessing game into an actionable certainty.

Keywords: Antimicrobial Resistance (AMR), Multi-Modal Machine Learning, Whole Genome Sequencing (WGS), SMILES, Minimum Inhibitory Concentration (MIC), Federated Learning, SHAP Explainability, Clinical Decision Support System (CDSS).