Phase Cancellation Networks: A Physics-Informed AI Architecture for Hallucination-Free De Novo Drug Design

Generative AI models often suffer from hallucinations, proposing molecular structures that are chemically plausible but physically invalid. This study introduces "Project Trinity," a novel architecture that integrates Complex-Valued Neural Networks (CVNN) with a Hallucination Noise Cancellation (HNC) filter. By treating molecular interactions as wave functions, we define "false" information as phase-mismatched signals and eliminate them via destructive interference. Applying this architecture to Alzheimer's Beta-amyloid fibrils, we screened 5 million candidates and identified a single novel compound, AP-2601. In-silico validation confirms that AP-2601 possesses optimal Blood-Brain Barrier (BBB) permeability and successfully disrupts the amyloid beta-sheet structure. This work demonstrates a paradigm shift from probabilistic generation to physical verification in AI-driven drug discovery.