Canertinib Dihydrochloride

Drug-Target Affinity (DTA) prediction is a cornerstone of drug discovery and development, providing critical insights into the intricate interactions between candidate drugs and their biological targets. Despite its importance, existing methodologies often face significant limitations in capturing comprehensive global features from molecular graphs, which are essential for accurately characterizing drug properties. Furthermore, protein feature extraction is predominantly restricted to 1D amino acid sequences, which fail to adequately represent the spatial structures and complex functional regions of proteins. These shortcomings impede the development of models capable of fully elucidating the mechanisms underlying drug-target interactions. To overcome these challenges, we propose a multimodal, multiscale model based on Sequence and Graph Modalities for Drug-Target Affinity (MMSG-DTA) Prediction. The model combines graph neural networks with Transformers to effectively capture both local node-level features and global structural features of molecular graphs. Additionally, a graph-based modality is employed to improve the extraction of protein features from amino acid sequences. To further enhance the model's performance, an attention-based feature fusion module is incorporated to integrate diverse feature types, thereby strengthening its representation capacity and robustness. We evaluated MMSG-DTA on three public benchmark data sets─Davis, KIBA, and Metz─and the experimental results demonstrate that the proposed model outperforms several state-of-the-art methods in DTA prediction. These findings highlight the effectiveness of MMSG-DTA in advancing the accuracy and robustness of drug-target interaction modeling.

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.