MGRL-DDI: Multiview Graph Representation Learning for Accurate Drug-Drug Interaction Prediction.

Drug-drug interactions (DDIs) present a significant challenge in clinical practice, as they may lead to adverse reactions, diminished therapeutic efficacy, and serious risks to patient safety. However, most existing methods depend on single-view representations of drug molecules or substructures, which limits their capacity to capture the diverse and complex nature of drug properties. To overcome this limitation, we propose MGRL-DDI, a multiview graph representation learning framework that comprehensively models drug structures from three complementary perspectives: Three-dimensional (3D) molecular graphs, motif graphs, and molecular graphs.

Deep Drug-Target Binding Affinity Prediction Base on Multiple Feature Extraction and Fusion.

Accurate drug-target binding affinity (DTA) prediction is crucial in drug discovery. Recently, deep learning methods for DTA prediction have made significant progress. However, there are still two challenges: (1) recent models always ignore the correlations in drug and target data in the drug/target representation process and (2) the interaction learning of drug-target pairs always is by simple concatenation, which is insufficient to explore their fusion.

Deep arrhythmia classification based on SENet and lightweight context transform.

Arrhythmia is one of the common cardiovascular diseases. Nowadays, many methods identify arrhythmias from electrocardiograms (ECGs) by computer-aided systems. However, computer-aided systems could not identify arrhythmias effectively due to various the morphological change of abnormal ECG data.

Multi-scaled self-attention for drug-target interaction prediction based on multi-granularity representation.

Background: Drug-target interaction (DTI) prediction plays a crucial role in drug discovery. Although the advanced deep learning has shown promising results in predicting DTIs, it still needs improvements in two aspects: (1) encoding method, in which the existing encoding method, character encoding, overlooks chemical textual information of atoms with multiple characters and chemical functional groups; as well as (2) the architecture of deep model, which should focus on multiple chemical patterns in drug and target representations.

Results: In this paper, we propose a multi-granularity multi-scaled self-attention (SAN) model by alleviating the above problems.

Deep drug-target binding affinity prediction with multiple attention blocks.

Drug-target interaction (DTI) prediction has drawn increasing interest due to its substantial position in the drug discovery process. Many studies have introduced computational models to treat DTI prediction as a regression task, which directly predict the binding affinity of drug-target pairs. However, existing studies (i) ignore the essential correlations between atoms when encoding drug compounds and (ii) model the interaction of drug-target pairs simply by concatenation.