Graph Neural Networks for COVID-19 Drug Discovery

Cheung, Mark; Moura, J.M.F.

doi:10.1109/bigdata50022.2020.9378164

Cited by 20 publications

(8 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Currently, several groups are exploiting the potential of Machine Learning (ML) in drug discovery (Li et al, 2017;Stephenson et al, 2019;Zhou et al, 2021). The models applied range from the use of fingerprints and molecular descriptors such as BCUTs to the use of connectivity graphs in graph neural networks (Cheung and Moura, 2020).…”

Section: Introductionmentioning

confidence: 99%

A machine learning-based virtual screening for natural compounds capable of inhibiting the HIV-1 integrase

Machado

Krempser²,

Guimarães³

2022

Front. Drug. Discov.

View full text Add to dashboard Cite

HIV-1 integrase is an essential enzyme for the HIV-1 replication cycle, and currently, integrase inhibitors are in the first line of treatment in many guidelines. Despite the discovery of new inhibitors, including a new class of molecules with different mechanisms of action, resistance is still a relevant problem, and adding new options to the therapeutic arsenal to fight viral resistance is a Sisyphean task. Because of the difficulty and cost of in vitro screenings, machine learning-driven ligand-based virtual screenings are an alternative that can not only cut costs but also use valuable information about active compounds with yet unknown mechanisms of action. In this work, we describe a thorough model exploration and hyperparameter tuning procedure in a dataset with class imbalance and show several models capable of distinguishing between compounds that are active or inactive against the HIV-1 integrase. The best of the models was then used to screen the natural product atlas for active compounds, resulting in a myriad of molecules that share features with known integrase inhibitors. Here we also explore the strengths and shortcomings of our models and discuss the use of the applicability domain to guide in vitro screenings and differentiate between the “predictable” and “unknown” regions of the chemical space.

show abstract

Section: Introductionmentioning

confidence: 99%

A machine learning-based virtual screening for natural compounds capable of inhibiting the HIV-1 integrase

Machado

Krempser²,

Guimarães³

2022

Front. Drug. Discov.

View full text Add to dashboard Cite

show abstract

“…We also selected the popular deep learning methods for comparison, including CNN [13] , DeepLSTM, CNN-AbiLSTM [14] , MPNN [15] , and transformer. With the same data set, we conducted pairwise experiments.…”

Section: Comparison With Deep Learning Methodsmentioning

confidence: 99%

Drug-target interaction prediction using high order nonlinear features via neural factorization machines

Zhu

2022

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

Medication design and repositioning are sped up by the prediction of drug-target interactions (DTIs). Two main kinds of prediction methods are commonly used, which are based on chemical structure feature extraction and deep learning methods. However, on the one hand, the DTI prediction approaches based on chemical structural feature extraction may not wholly explore the possible network characteristics in the data. On the other hand, many deep learning methods call for numerous layers of neural networks to be layered to learn higher-order feature interaction information. To sum up, the existing computation methods often have the limitations of gradient disappearance and overfitting. This study presents a novel method (JRD-NFM) by calculating Jaccard similarities, getting an eigenvector through Restarted random walk (RWR), and generating low-dimensional feature vectors by Disposition Component Analysis (DCA). Besides decoding the topological features and similarity information of target and drug node, it can also get the context information of a single network. Considering the advantages of Neural Factorization Machines (NFM) in extracting high-order nonlinear features and processing sparse data, this study use NFM to classifier the collection data to integrate drug and target biochemical structure information. The experimental results demonstrate that JRD-NFM can outperform widely used deep learning methods and conventional chemical structure approaches. It could provide fresh ideas for combining molecular structure and heterogeneous network data to predict DTIs.

show abstract

“…A review on GNNs for drug design can be found in Xiong et al 169 . In recent years, one application focus were Covid 19 related challenges, where GNNs were used for e.g., finding new drug candidates 170 or detecting infections in medical images 171,172 . Similar methods are also applicable to other challenges in drug design and medicine.…”

Section: Applicationsmentioning

confidence: 99%

Graph neural networks for materials science and chemistry

et al. 2022

View full text Add to dashboard Cite

Machine learning plays an increasingly important role in many areas of chemistry and materials science, being used to predict materials properties, accelerate simulations, design new structures, and predict synthesis routes of new materials. Graph neural networks (GNNs) are one of the fastest growing classes of machine learning models. They are of particular relevance for chemistry and materials science, as they directly work on a graph or structural representation of molecules and materials and therefore have full access to all relevant information required to characterize materials. In this Review, we provide an overview of the basic principles of GNNs, widely used datasets, and state-of-the-art architectures, followed by a discussion of a wide range of recent applications of GNNs in chemistry and materials science, and concluding with a road-map for the further development and application of GNNs.

show abstract

Graph Neural Networks for COVID-19 Drug Discovery

Cited by 20 publications

References 8 publications

A machine learning-based virtual screening for natural compounds capable of inhibiting the HIV-1 integrase

A machine learning-based virtual screening for natural compounds capable of inhibiting the HIV-1 integrase

Drug-target interaction prediction using high order nonlinear features via neural factorization machines

Graph neural networks for materials science and chemistry

Contact Info

Product

Resources

About