SMCseeker: An attentive virtual screening model for antiviral discovery.

ABSTRACT: Influenza virus, with high morbidity and mortality rates, is a global health threat. Traditional antiviral screenings are costly, whereas machine learning could enhance the effectiveness of antiviral drug discovery. Leveraging a large-scale, in-house antiviral dataset against H1N1, we developed a small molecule compound seeker (SMCseeker) framework for identifying highly active anti-H1N1 agents. Data augmentation and a multi-head attention mechanism were utilized to address the extreme data imbalance and enhance the generalization ability of the model. 18,093 structure-activity signatures after cleaning from 52,800 compounds were selected for training, with another 3,876 validation and 3,879 unseen data points to verify the model's generalization ability. H1N1-SMCseeker demonstrates stable

SUBMITTER: Li J 

PROVIDER: S-EPMC12496183 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature