GFLearn: Generalized Feature Learning for Drug-Target Binding Affinity Prediction

Predicting drug-target binding affinity is critical for drug discovery, as it helps identify promising drug candidates and predict their effectiveness. Recent advancements in deep learning have made significant progress in tackling this task. However, existing methods heavily rely on training data, and their performance is often limited when predicting binding affinities for new drugs and targets. To address this challenge, we propose a novel Generalized Feature Learning (GFLearn) model for drug-target binding affinity prediction. By integrating Graph Neural Networks (GNNs) with a self-supervised invariant feature learning module, our GFLearn model can extract robust and highly generalizable features from both drugs and targets, significantly enhancing prediction performance. This innovation enables the model to effectively predict binding affinities for previously unseen drugs or targets, while also mitigates the common issue of prediction performance degrading due to shifts in data distribution. Extensive experiments were conducted on two diverse datasets across three challenging scenarios: new drugs, new targets, and combinations of both. Comparisons with state-of-the-art methods demonstrated that our GFLearn model consistently outperformed others, showcasing its robustness across various prediction tasks. Additionally, cross-dataset evaluations and noise perturbation experiments further validated the model's generalizability across different data distributions. Case studies on two drug-target pairs, Canertinib-PIK3C2G and MLN8054-FLT1, provided further evidence of GFLearn's ability to make accurate binding affinity predictions, offering valuable insights for drug screening and repurposing efforts.