Multi-task learning (MTL) is a machine learning paradigm that aims to enhance the
generalization of predictive models by leveraging shared information across multiple tasks …

Artificial intelligence (AI) in the form of deep learning has promise for drug discovery and
chemical biology, for example, to predict protein structure and molecular bioactivity, plan …

Predicting drug–target interaction is key for drug discovery. Recent deep learning-based
methods show promising performance, but two challenges remain: how to explicitly model …

The rational design of PROTACs is difficult due to their obscure structure-activity
relationship. This study introduces a deep neural network model-DeepPROTACs to help …

Motivation Identifying drug–target interactions (DTIs) is a crucial step in drug repurposing
and drug discovery. Accurately identifying DTIs in silico can significantly shorten …

Predicting protein–ligand binding affinities (PLAs) is a core problem in drug discovery.
Recent advances have shown great potential in applying machine learning (ML) for PLA …

The prediction of drug-target affinity (DTA) plays an increasingly important role in drug
discovery. Nowadays, lots of prediction methods focus on feature encoding of drugs and …

Drug-target interaction (DTI) prediction plays a crucial role in drug repositioning and virtual
drug screening. Most DTI prediction methods cast the problem as a binary classification task …

Predicting protein–ligand binding affinity is a central issue in drug design. Various deep
learning models have been published in recent years, where many of them rely on 3D …

Exiting computational models for drug–target binding affinity prediction have much room for
improvement in prediction accuracy, robustness and generalization ability. Most deep …