PocketDTA: A pocket-based multimodal deep learning model for drug-target affinity prediction.

Drug-target affinity prediction is a fundamental task in the field of drug discovery. Extracting and integrating structural information from proteins effectively is crucial to enhance the accuracy and generalization of prediction, which remains a substantial challenge. This paper proposes a pocket-based multimodal deep learning model named PocketDTA for drug-target affinity prediction, based on the principle of "structure determines function".

EMPDTA: An End-to-End Multimodal Representation Learning Framework with Pocket Online Detection for Drug-Target Affinity Prediction.

Accurately predicting drug-target interactions is a critical yet challenging task in drug discovery. Traditionally, pocket detection and drug-target affinity prediction have been treated as separate aspects of drug-target interaction, with few methods combining these tasks within a unified deep learning system to accelerate drug development. In this study, we propose EMPDTA, an end-to-end framework that integrates protein pocket prediction and drug-target affinity prediction to provide a comprehensive understanding of drug-target interactions.

DRTerHGAT: A drug repurposing method based on the ternary heterogeneous graph attention network.

Drug repurposing is an effective method to reduce the time and cost of drug development. Computational drug repurposing can quickly screen out the most likely associations from large biological databases to achieve effective drug repurposing. However, building a comprehensive model that integrates drugs, proteins, and diseases for drug repurposing remains challenging.

MSFF-MA-DDI: Multi-Source Feature Fusion with Multiple Attention blocks for predicting Drug-Drug Interaction events.

The interaction of multiple drugs could lead to severe events, which cause medical injuries and expenses. Accurate prediction of drug-drug interaction (DDI) events can help clinicians make effective decisions and establish appropriate therapy programs. However, there exist two issues worthy of further consideration.

Small molecule drug and biotech drug interaction prediction based on multi-modal representation learning.

Background: Drug-drug interactions (DDIs) occur when two or more drugs are taken simultaneously or successively. Early detection of adverse drug interactions can be essential in preventing medical errors and reducing healthcare costs. Many computational methods already predict interactions between small molecule drugs (SMDs).

TP-DDI: A Two-Pathway Deep Neural Network for Drug-Drug Interaction Prediction.

Adverse drug-drug interactions (DDIs) can severely damage the body. Thus, it is essential to accurately predict DDIs. DDIs are complex processes in which many factors can cause interactions.