Model-driven Collaborative Information Enhances Drug Activity Prediction in Low-resource Scenarios

Drug development is hindered by the high cost and time consumption of identifying bioactive molecules, particularly in zero-shot and few-shot scenarios where experimental data is scarce. Existing methods often fail to generalize to novel proteins or assays, limiting their applicability in real-world drug discovery. Furthermore, these approaches struggle with the simultaneous processing of heterogeneous data types, including assay descriptions, molecular structures, and protein sequences. To address these limitations, we propose Assay Similarity-based Activity Prediction (ASAP), a novel framework that leverages a model-driven assay similarity and ranking loss to predict bioactivity. By learning the collaborative information between assays and mitigating batch effects, ASAP enables robust predictions for low-resource scenarios, such as novel assays with only text or protein sequence information. Extensive experiments demonstrate the superiority of ASAP in both zero-shot and few-shot settings. Our proposed approach overcomes the limitations of traditional pair-input models and provides a scalable, generalizable solution for accelerating drug discovery.