Coloring molecules with explainable artificial intelligence for preclinical relevance assessment
J Jiménez-Luna, M Skalic, N Weskamp… - Journal of Chemical …, 2021 - ACS Publications
Graph neural networks are able to solve certain drug discovery tasks such as molecular
property prediction and de novo molecule generation. However, these models are …
property prediction and de novo molecule generation. However, these models are …
GraphDTA: predicting drug–target binding affinity with graph neural networks
The development of new drugs is costly, time consuming and often accompanied with safety
issues. Drug repurposing can avoid the expensive and lengthy process of drug development …
issues. Drug repurposing can avoid the expensive and lengthy process of drug development …
Quantitative evaluation of explainable graph neural networks for molecular property prediction
Graph neural networks (GNNs) have received increasing attention because of their
expressive power on topological data, but they are still criticized for their lack of …
expressive power on topological data, but they are still criticized for their lack of …
HiGNN: A hierarchical informative graph neural network for molecular property prediction equipped with feature-wise attention
Elucidating and accurately predicting the druggability and bioactivities of molecules plays a
pivotal role in drug design and discovery and remains an open challenge. Recently, graph …
pivotal role in drug design and discovery and remains an open challenge. Recently, graph …
Interpretable chirality-aware graph neural network for quantitative structure activity relationship modeling in drug discovery
In computer-aided drug discovery, quantitative structure activity relation models are trained
to predict biological activity from chemical structure. Despite the recent success of applying …
to predict biological activity from chemical structure. Despite the recent success of applying …
Pharmacophoric-constrained heterogeneous graph transformer model for molecular property prediction
Informative representation of molecules is a crucial prerequisite in AI-driven drug design and
discovery. Pharmacophore information including functional groups and chemical reactions …
discovery. Pharmacophore information including functional groups and chemical reactions …
Prediction of pharmacological activities from chemical structures with graph convolutional neural networks
M Sakai, K Nagayasu, N Shibui, C Andoh… - Scientific reports, 2021 - nature.com
Many therapeutic drugs are compounds that can be represented by simple chemical
structures, which contain important determinants of affinity at the site of action. Recently …
structures, which contain important determinants of affinity at the site of action. Recently …
CasANGCL: pre-training and fine-tuning model based on cascaded attention network and graph contrastive learning for molecular property prediction
Motivation Molecular property prediction is a significant requirement in AI-driven drug design
and discovery, aiming to predict the molecular property information (eg toxicity) based on the …
and discovery, aiming to predict the molecular property information (eg toxicity) based on the …
Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models
Graph neural networks (GNN) has been considered as an attractive modelling method for
molecular property prediction, and numerous studies have shown that GNN could yield …
molecular property prediction, and numerous studies have shown that GNN could yield …
Comprehensive evaluation of deep and graph learning on drug–drug interactions prediction
Recent advances and achievements of artificial intelligence (AI) as well as deep and graph
learning models have established their usefulness in biomedical applications, especially in …
learning models have established their usefulness in biomedical applications, especially in …