Singapore team targets more adaptable drug discovery models

A Singapore research team proposed a framework at ICLR 2026 to help AI models make out-of-domain bioactivity predictions, a capability relevant to drug discovery systems evaluating compounds beyond familiar data settings.

The work focuses on making bioactivity models more adaptable when predictions move outside the domain where a model is expected to perform reliably. That goal addresses a key limitation for AI-driven drug discovery, where models often need to assess new biological or chemical contexts.