Artificial intelligence has significantly advanced early-stage drug discovery, enabling large-scale prediction, simulation, and molecular generation. Despite these improvements, clinical success rates have not increased proportionally. This work presents a structural explanation for this persistent gap. Modern AI systems are optimized for smooth, continuous problem spaces, while biological systems operate through thresholds, context-dependence, and irreversible transitions. As a result, many drug candidates appear promising during computational and preclinical phases but fail when exposed to the discontinuities inherent in living systems. Framing drug discovery as a problem of threshold navigation rather than pure optimization, this paper outlines practical implications for AI-driven pharmaceutical research. It proposes the use of large-scale computational infrastructure not only for candidate generation, but for mapping collapse boundaries, identifying fragility across contexts, and improving decision-making under irreversible conditions. This paper serves as an executive-level summary of the framework developed in Almost Working: Why Artificial Intelligence Fails at the Threshold of Drug Discovery (2026), providing a concise operational perspective for researchers, investors, and pharmaceutical leaders.
Larry Lim Kheng Cheong (Tue,) studied this question.