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Neural multi-task learning in drug design
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 …
generalization of predictive models by leveraging shared information across multiple tasks …
A survey of drug-target interaction and affinity prediction methods via graph neural networks
Y Zhang, Y Hu, N Han, A Yang, X Liu, H Cai - Computers in Biology and …, 2023 - Elsevier
The tasks of drug-target interaction (DTI) and drug-target affinity (DTA) prediction play
important roles in the field of drug discovery. However, biological experiment-based …
important roles in the field of drug discovery. However, biological experiment-based …
A knowledge-guided pre-training framework for improving molecular representation learning
Learning effective molecular feature representation to facilitate molecular property prediction
is of great significance for drug discovery. Recently, there has been a surge of interest in pre …
is of great significance for drug discovery. Recently, there has been a surge of interest in pre …
[PDF][PDF] KGNN: Knowledge graph neural network for drug-drug interaction prediction.
Drug-drug interaction (DDI) prediction is a challenging problem in pharmacology and
clinical application, and effectively identifying potential D-DIs during clinical trials is critical …
clinical application, and effectively identifying potential D-DIs during clinical trials is critical …
Difficulty in chirality recognition for Transformer architectures learning chemical structures from string representations
Recent years have seen rapid development of descriptor generation based on
representation learning of extremely diverse molecules, especially those that apply natural …
representation learning of extremely diverse molecules, especially those that apply natural …
A novel molecular representation with BiGRU neural networks for learning atom
Molecular representations play critical roles in researching drug design and properties, and
effective methods are beneficial to assisting in the calculation of molecules and solving …
effective methods are beneficial to assisting in the calculation of molecules and solving …
Compound–protein interaction prediction by deep learning: databases, descriptors and models
The screening of compound–protein interactions (CPIs) is one of the most crucial steps in
finding hit and lead compounds. Deep learning (DL) methods for CPI prediction can address …
finding hit and lead compounds. Deep learning (DL) methods for CPI prediction can address …
IIFDTI: predicting drug–target interactions through interactive and independent features based on attention mechanism
Motivation Identifying drug–target interactions is a crucial step for drug discovery and
design. Traditional biochemical experiments are credible to accurately validate drug–target …
design. Traditional biochemical experiments are credible to accurately validate drug–target …
DeepGS: Deep representation learning of graphs and sequences for drug-target binding affinity prediction
Accurately predicting drug-target binding affinity (DTA) in silico is a key task in drug
discovery. Most of the conventional DTA prediction methods are simulation-based, which …
discovery. Most of the conventional DTA prediction methods are simulation-based, which …
A merged molecular representation learning for molecular properties prediction with a web-based service
H Kim, J Lee, S Ahn, JR Lee - Scientific Reports, 2021 - nature.com
Deep learning has brought a dramatic development in molecular property prediction that is
crucial in the field of drug discovery using various representations such as fingerprints …
crucial in the field of drug discovery using various representations such as fingerprints …