Constraint-Aware Corrective Memory for Language-Based Drug Discovery Agents

CACM addresses a critical 'control gap' in AI-led drug discovery: LLMs are good at step-by-step reasoning but struggle with global set-level constraints (e.g., 'give me 10 molecules that are diverse but all bind to X'). The project is currently a reference implementation for a research paper with very low engagement (0 stars, 3 forks), which places it in the 'prototype' category. While the domain expertise required to build these constraints is a minor moat, the technique itself—corrective memory—is a common agentic pattern. It competes with broader scientific agent projects like ChemCrow or Coscientist. The primary risk is that frontier labs (OpenAI, Google DeepMind) are developing 'science-native' models (e.g., AlphaFold 3 integrated with LLMs) that might handle these constraints natively through better reasoning or specialized RLHF, potentially making external 'corrective' wrappers redundant within 18-24 months. However, the specificity to drug discovery protocols provides some temporary insulation from general-purpose platform updates.