Revisiting Target-Aware de novo Molecular Generation with TarPass: Between Rational Design and Texas Sharpshooter

Target-aware molecular generation models have emerged as promising tools for structure-based drug discovery, yet it remains unclear whether they genuinely exploit target information or merely resemble the Texas Sharpshooter fallacy by retrospectively rationalizing outputs. To address this, we introduce TarPass, a benchmark comprising 18 well-characterized targets and reference actives, enabling systematic evaluation across three paradigms: 3D in-situ, non-3D, and optimization-based methods. We assessed 15 representative models from the perspectives of protein-ligand interactions (PLIs), molecular plausibility, and drug-likeness. Our results show that only a few models surpass random baselines in recovering PLIs, while pretrained approaches yield more chemically plausible and drug-like molecules with fewer structural alerts. Optimization-based methods effectively redirect outputs toward favorable regions of chemical space, but often at the expense of other properties. In terms of target specificity, existing models can distinguish at the protein-family level but struggle to recognize more fine-grained structural differences. Integrating these insights, we designed a multi-tier virtual screening workflow combining hard filters with refined selection, which can cluster of promising candidates for experimental validation. Overall, TarPass reveals that current models were still far from rational design tools, highlighting both their potential to explore novel chemical space and the need for future improvements in capturing the principles of protein-ligand interactions.