Abstract
The rapid proliferation of AI/ML models in drug discovery heralds an era of extraordinary progress but also raises urgent questions about whether the true predictive performance is as good as advertised. On-target prediction models often benefit from high-resolution structural or atomistic representations that capture the subtleties of binding affinity and pose. In contrast, off-target and ADMET liabilities have typically relied on more implicit representations of molecular interactions. Retrospective benchmarks often provide a misleading picture of how successful these diverse representations are at predicting properties, and the community lacks standardized, prospective comparisons. Blind challenges, such as the OpenADMET × ASAP × PolarisHub Challenge featured in this issue, are crucial for realistically evaluating progress, encouraging iterations, and directing collective efforts toward major accuracy barriers. With ongoing investment in large-scale, open data creation, and community-led challenges, predictive modeling is poised to rapidly transform drug discovery by enabling accurate, multiparameter optimization.