Accurate protein structure prediction is increasingly central to AI driven drug discovery, yet variability in structural data quality continues to limit model reliability. This poster explores the challenges of integrating predicted protein structures into AI pipelines, with emphasis on data provenance, confidence scoring, and consistency across prediction methods. Key discussion areas include assessing structural uncertainty, managing discrepancies between experimental and predicted models, and understanding the downstream impact on target assessment and molecular design. The session will highlight best practices for incorporating structure prediction outputs into discovery workflows while maintaining data integrity, reproducibility, and confidence in AI driven decision making.