C-Prep ML: A Curriculum-Inspired Anti-Hallucination Framework for Machine Learning in Drug Discovery

Hallucinations — misleading outputs generated by machine learning models — are a growing challenge in biomedical AI, where inflated benchmark performance often collapses under real-world conditions. This preprint introduces C-Prep ML (Civil-Prep Inspired Machine Learning), a conceptual framework designed to mitigate hallucinations in drug discovery pipelines. The framework is structured into three stages: Prelims (Grounding): Incorporating retrieval-augmented generation, abstention mechanisms, and physics-informed loss to ensure biologically plausible predictions. Mains (Reasoning Discipline): Enforcing structured reasoning via program-of-thought prompting, verifier–critic cross-checking, and constrained decoding. Interview (Stress Testing): Enhancing robustness through adversarial evaluation, Bayesian uncertainty calibration, and grounded performance metrics. Applied conceptually to oncology pipelines (e.g., CML TKI discovery), C-Prep ML emphasizes quality over scale, showing how small but curated datasets combined with reasoning-focused training can improve translational reliability. The approach highlights that hallucination in medicine = patient risk, underscoring the urgency of robust, conceptually grounded AI in healthcare. This work serves as a perspective and hypothesis paper, offering a unifying framework to inspire further experimentation, validation, and community discussion. Keywords:Machine Learning, Drug Discovery, Biomedical AI, Hallucination Mitigation, Oncology, Robustness, Curriculum-Inspired Framework