HitScreen: A Sequence-Based Drug Virtual Screening Approach Using Data Augmentation and Protein Language Models

Sequence-based drug-target interaction (DTI) prediction is an effective approach for identifying potential drug candidates without relying on three-dimensional protein structures. However, current sequence-based methods often suffer from limited generalization to novel targets and fail to capture essential spatial interaction features. As a result, they exhibit a significant performance gap compared with structure-based methods. To bridge this gap, we propose HitScreen, a robust deep learning framework specifically designed for sequence-based DTI prediction, applied to virtual screening scenarios. We introduce a conditional label inversion strategy to address class imbalance, annotation biases, and ligand biases in the data sets. HitScreen integrates multiple pretrained protein language models (Ankh, ESM-2, ProtT5) alongside the molecular pretrained model Uni-Mol to encode spatial information. Additionally, HitScreen utilizes a cross-attention mechanism to capture local intermolecular interactions between drug molecules and protein sequences. Rigorous benchmarking on independent data sets (DEKOIS2.0 and DUD-E) demonstrates that HitScreen achieves performance comparable to or surpassing state-of-the-art structure-based methods, while relying solely on protein sequence information. Comprehensive interpretability analyses further validate how the model accurately identifies biologically relevant molecular interactions, providing valuable insights into rational drug design. In summary, these findings demonstrate HitScreen as a robust, interpretable, and broadly applicable framework for DTI prediction with applications in sequence-based drug virtual screening.