Graph neural networks
(GNNs) constitute a class of deep learning
methods for graph data. They have wide applications in chemistry and
biology, such as molecular property prediction, reaction prediction,
and drug–target interaction prediction. Despite the interest,
GNN-based modeling is challenging as it requires graph data preprocessing
and modeling in addition to programming and deep learning. Here, we
present Deep Graph Library (DGL)-LifeSci, an open-source package for
deep learning on graphs in life science. Deep Graph Library (DGL)-LifeSci
is a python toolkit based on RDKit, PyTorch, and Deep Graph Library
(DGL). DGL-LifeSci allows GNN-based modeling on custom datasets for
molecular property prediction, reaction prediction, and molecule generation.
With its command-line interfaces, users can perform modeling without
any background in programming and deep learning. We test the command-line
interfaces using standard benchmarks MoleculeNet, USPTO, and ZINC.
Compared with previous implementations, DGL-LifeSci achieves a speed
up by up to 6×. For modeling flexibility, DGL-LifeSci provides
well-optimized modules for various stages of the modeling pipeline.
In addition, DGL-LifeSci provides pretrained models for reproducing
the test experiment results and applying models without training.
The code is distributed under an Apache-2.0 License and is freely
accessible at .
The Janus kinase (JAK) family plays a pivotal role in most cytokine-mediated inflammatory and autoimmune responses via JAK/STAT signaling, and administration of JAK inhibitors is a promising therapeutic strategy for several diseases including COVID-19. However, to screen and design selective JAK inhibitors is a daunting task due to the extremely high homology among four JAK isoforms. In this study, we aimed to simultaneously predict pIC50 values of compounds for all JAK subtypes by constructing an interpretable GNN multitask regression model. The final model performance was positive, with R2 values of 0.96, 0.79 and 0.78 on the training, validation and test sets, respectively. Meanwhile, we calculated and visualized atom weights, followed by the rank sum tests and local mean comparisons to obtain key atoms and substructures that could be fine-tuned to design selective JAK inhibitors. Several successful case studies have demonstrated that our approach is feasible and our model could learn the interactions between proteins and small molecules well, which could provide practitioners with a novel way to discover and design JAK inhibitors with selectivity.
Graphical Abstract
In recent years, drug-induced nephrotoxicity has been one of the main reasons for the failure of drug development. Early prediction of the nephrotoxicity for drug candidates is critical to the success of clinical trials. Therefore, it is very important to construct an effective model that can predict the potential nephrotoxicity of compounds. Machine learning methods have been widely used to predict the physico-
Drug‐induced liver injury (DILI) is one of the major causes of drug withdrawals, acute liver injury and blackbox warnings. Clinical diagnosis of DILI is a huge challenge due to the complex pathogenesis and lack of specific biomarkers. In recent years, machine learning methods have been used for DILI risk assessment, but the model generalization does not perform satisfactorily. In this study, we constructed a large DILI data set and proposed an integration strategy based on hybrid representations for DILI prediction (HR‐DILI). Benefited from feature integration, the hybrid graph neural network models outperformed single representation‐based models, among which hybrid‐GraphSAGE showed balanced performance in cross‐validation with AUC (area under the curve) as 0.804±0.019. In the external validation set, HR‐DILI improved the AUC by 6.4 %‐35.9 % compared to the base model with a single representation. Compared with published DILI prediction models, HR‐DILI had better and balanced performance. The performance of local models for natural products and synthetic compounds were also explored. Furthermore, eight key descriptors and six structural alerts associated with DILI were analyzed to increase the interpretability of the models. The improved performance of HR‐DILI indicated that it would provide reliable guidance for DILI risk assessment.
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