AlloMIT: Boosting Allosteric Virtual Screening via Multi-Instance Parameter-Efficient Tuning

Allosteric modulation offers opportunities to alleviate target crowding and broaden the scope of drug discovery by targeting sites beyond canonical active sites. However, mainstream virtual screening methods largely focus on orthosteric screening, where a binding pocket is specified in advance. In contrast, allosteric sites may be unknown and require ligand-induced conformational changes, making allosteric screening more challenging. Moreover, the limited availability of allosteric activity data poses an additional challenge for training effective screening models. To this end, we introduce AlloMIT, a multi-instance parameter-efficient tuning strategy for allosteric hit discovery under limited activity data. AlloMIT integrates with pre-trained virtual screening alignment models in a plug-and-play manner, adapting them with only a small number of trainable parameters. Rather than relying on a single pre-defined binding pocket, AlloMIT represents each protein structure as a set of candidate pockets and learns target-ligand interactions in a weakly supervised, multi-instance manner, without requiring pocket-level annotations. To account for ligand-dependent pocket contributions, its Contextual Sparse Pocket Aggregation (CSPA) module considers potential pocket-ligand interactions within proteins and outputs the final pocket score via sparse aggregation. Additionally, we construct AlloVSBench to support parameter-efficient model adaptation and benchmark allosteric hit discovery performance across screening methods and parameter-efficient tuning methods. Ultimately, AlloMIT achieves the highest allosteric BEDROC score among all evaluated methods. Compared with adapted HypSeek, AlloMIT improves BEDROC from 17.09% to 23.25% and lifts EF₀.₅% from 20.79 to 41.70.