Search for a command to run...
Recent advances in AI-driven drug discovery have led to widespread narratives suggesting that a single model or platform can generate viable therapeutic candidates and, when combined with automated laboratory systems, rapidly progress to clinical development. These narratives often imply that AI-driven design coupled with robotic execution can substantially compress the path to Phase I trials and accelerate the treatment of complex diseases within a few years. However, practical implementation reveals a significant gap between model-level performance and end-to-end drug development success. This work presents a systems-level analysis of current AI-driven pipelines and introduces a multi-model, feasibility-aware, closed-loop framework designed to address key structural limitations. We identify three primary constraints: (1) fragmentation across specialized tools requiring multiple independent models prior to synthesis, (2) misalignment between benchmark performance and biological outcomes, and (3) limited compatibility between novel compound generation and standardized experimental workflows. We further demonstrate that downstream automation platforms, including robotic laboratory systems, accelerate specific classes of high-throughput experimental workflows but do not eliminate upstream fragmentation or the need for bespoke synthesis strategies. As molecular novelty increases, compatibility with standardized automation decreases, reinforcing the need for adaptive, human-guided experimental design. We argue that drug discovery is fundamentally a decision-making system under uncertainty, rather than a single-model prediction problem. The proposed framework integrates multi-model orchestration, early-stage feasibility filtering, tiered candidate prioritization, and closed-loop experimental feedback to improve translation from computational predictions to real-world outcomes. As of March 2026, even leading integrated platforms such as Isomorphic Labs have not released public wet-lab validation data despite strong computational benchmarks, underscoring the persistent translation gap. Bridging this gap requires not only improved models, but coordinated systems that integrate prediction, constraint, and experimental validation into a unified workflow. This distinction between perceived and actual workflows is illustrated in Figure 1, and the underlying fragmentation across current platforms is detailed in Figure 2.