Beyond accuracy: comprehensive alignment for AI-driven molecular design

Artificial intelligence (AI) is rapidly transforming biomolecular and material design, enabling advances in protein engineering, drug discovery, and material innovation. Yet, growing evidence shows that AI-generated candidates often violate physical laws, diverge from scientific objectives, or neglect safety and regulatory principles—revealing a fundamental misalignment between computational outputs and real-world requirements. Here, we propose comprehensive alignment, a framework that links AI systems not only to statistical data distributions but also to natural laws, scientific goals, and responsible research principles. Drawing examples across proteins, drugs, and materials, we illustrate how neglecting any alignment layer can yield unstable folds, unsynthesizable molecules, or unsafe outcomes. We then outline strategies to bridge these gaps through enriched datasets, hybrid physics-informed architectures, multi-objective evaluation, and closed-loop feedback. Together, these changes recast AI from a one-way generator into a trustworthy copilot—capable of accelerating discovery while remaining anchored in the principles that govern scientific validity and societal responsibility.