Nikolas: Autonomous Discovery of Novel EGFR Kinase Inhibitors via the PRISM Meta-Cognitive Architecture

Discovering targeted small-molecule therapeutics requires navigating a chemical space exceeding 10 60 drug-like compounds-a process traditionally demanding years of iterative synthesis and screening. We present Nikolas, a semi-autonomous AI agent powered by PRISM (Persistent Recursive Intelligence with Structured Metacognition)-a domainagnostic 5-layer meta-cognitive architecture that enables emergent chemical intuition through hierarchical memory and recursive self-reflection. Previously validated on inorganic CO 2 reduction catalysis, PRISM is applied here to de novo EGFR kinase inhibitor design for non-small cell lung cancer. The pipeline integrates GPT-5.2 for generative molecular reasoning, RDKit for multi-objective drug-likeness validation, and AutoDock Vina for physics-based docking against PDB: 1M17. In 20 autonomous iterations, Nikolas generated 13 valid drug-like molecules, 12 achieving "Excellent" binding affinities (∆G < −9.0 kcal/mol). The champion (∆G = −9.67 kcal/mol, Drug Score 95/100) surpasses Erlotinib (∆G ≈ −9.5 kcal/mol) with confirmed Met793 hydrogen bonding. Cross-referencing against PubChem (115M+ compounds) and ChEMBL (2.4M+ bioactive molecules) confirmed 4 of 5 top candidates are Novel Chemical Entities. Tanimoto analysis (T c = 0.183-0.474 vs. approved TKIs) confirms structurally distinct scaffolds. All candidates exhibit ideal ADMET profiles: zero Lipinski violations, high GI absorption, and SA scores of 2.2-2.5.