Drug Repurposing

Drug-Target Interaction Prediction uses AI to estimate the therapeutic potential of existing drugs against new disease targets, enabling accelerated identification of repurposing candidates, by integrating biomedical data to predict drug-target binding, across pharmaceutical R&D workflows.

Developing new drugs from scratch is prohibitively lengthy and costly, leaving many rare and unmet diseases without viable treatments; existing approved drugs that could address these conditions are not systematically evaluated for new indications.

The AI integrates diverse biomedical datasets (molecular structures, clinical outcomes, genomics, and literature) and predicts drug-target interaction probabilities, scoring candidate drug-disease pairs for repurposing feasibility.

Reduces drug discovery timelines and associated R&D costs by surfacing high-probability repurposing candidates earlier; measured as reduction in time-to-candidate identification and cost-per-candidate.

• •Curated biomedical databases (molecular structures, clinical outcomes, genomics) are accessible and licensed
• •A validated drug-target interaction dataset exists for model training and benchmarking