AI for Drug Discovery: Bridging the Translation Gap

A widening chasm has emerged between in-silico optimization and real-world therapeutic validation. Current deep learning models routinely claim state-of-the-art performance on static, curated academic benchmarks; however, these metrics are frequently artifacts of shortcut learning and selection biases inherent to public datasets. When deployed in production-grade drug discovery environments - characterized by severe data scarcity, unaligned modalities, and non-stationary biological drift—these models exhibit severe fragility. This workshop moves the community beyond superficial leaderboard chasing. We explicitly solicit papers that algorithmically diagnose benchmark vulnerabilities, develop provably robust and sample-efficient architectures, and design interactive AI Co-Scientists capable of safely steering generative models through data-starved biological regimes.