A Modular Machine Learning Framework for Small-Molecule Drug Repurposing Based on Organ Permeability, Target Binding, and Biomarker Modulation

Abstract

With nearly 90% of drug candidates failing in clinical trials due to poor efficacy or toxicity, drug repurposing has emerged as a vital strategy to accelerate the delivery of life-saving treatments. However, most current drug repurposing approaches fail to account for the physiological barriers and downstream biological impacts that dictate therapeutic success. To bridge this gap, we present SCOUT (Screening Candidates via Organ Uptake and Target-binding), a modular machine learning-driven framework for drug repurposing by simultaneously modeling organ permeability, drug-target binding, and biomarker modulation. Unlike conventional repurposing efforts that rely on single-point predictions or disconnected steps, SCOUT integrates these factors into a unified predictive funnel, significantly reducing trial-and-error and prioritizing candidates with the highest probability of therapeutic relevance. As a proof of concept, we applied SCOUT to Alzheimers disease, where blood-brain barrier (BBB) penetration is an important hurdle. The framework first predicts the unbound brain-to-plasma partition coefficient, Kpuu, using SMILES-based embeddings and achieved robust performance (Accuracy: 0.90, Recall: 0.94), then integrates machine learning to predict binding affinity with BACE1, a key Alzheimers target that was used in this work as a proof of concept. By combining these modules, SCOUT reduced the candidate search space by 99.9% and identified hits with diverse mechanisms of action. Critically, SCOUT extends beyond hit identification by employing mechanistic modeling to simulate how these candidates might modulate specific disease biomarkers (e.g. Amyloid-beta (A{beta}) peptides). This hybrid approach ensures repurposed candidates are not only chemically viable but also physiologically active, providing a rational and resource-efficient pipeline for drug development.