MultiGML: Multimodal graph machine learning for prediction of adverse drug events

Krix, Sophia; DeLong, Lauren Nicole; Madan, Sumit; Domingo-Fernández, Daniel; Ahmad, Ashar; Gul, Sheraz; Zaliani, Andrea; Fröhlich, Holger

doi:10.1016/j.heliyon.2023.e19441

Cited by 4 publications

(1 citation statement)

References 89 publications

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“…These models demonstrate superior performance, independence from known drug–ADR interactions and broader predictive capabilities, expanding their potential applications in safeguarding patients and ensuring drug safety. On the other hand, the Graph Machine Learning neural network model MultiGML has also been developed to enhance ADR classification [ 172 ]. MultiGML significantly outperforms traditional classifiers in terms of performance, particularly in classifying ADRs into multiple categories.…”

Section: Applications Of Attention-based Models In Drug Discoverymentioning

confidence: 99%

Attention is all you need: utilizing attention in AI-enabled drug discovery

Zhang,

Liu,

Liu

et al. 2023

Briefings in Bioinformatics

123

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Recently, attention mechanism and derived models have gained significant traction in drug development due to their outstanding performance and interpretability in handling complex data structures. This review offers an in-depth exploration of the principles underlying attention-based models and their advantages in drug discovery. We further elaborate on their applications in various aspects of drug development, from molecular screening and target binding to property prediction and molecule generation. Finally, we discuss the current challenges faced in the application of attention mechanisms and Artificial Intelligence technologies, including data quality, model interpretability and computational resource constraints, along with future directions for research. Given the accelerating pace of technological advancement, we believe that attention-based models will have an increasingly prominent role in future drug discovery. We anticipate that these models will usher in revolutionary breakthroughs in the pharmaceutical domain, significantly accelerating the pace of drug development.

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Section: Applications Of Attention-based Models In Drug Discoverymentioning

confidence: 99%

Attention is all you need: utilizing attention in AI-enabled drug discovery

Zhang,

Liu,

Liu

et al. 2023

Briefings in Bioinformatics

123

View full text Add to dashboard Cite

show abstract

Pharmacological profiles of neglected tropical disease drugs

Greco,

Karki,

Gadiya

et al. 2024

Artificial Intelligence in the Life Sciences

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Graph Artificial Intelligence in Medicine

Johnson,

Li,

Noori

et al. 2024

Annual Review of Biomedical Data Science

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In clinical artificial intelligence (AI), graph representation learning, mainly through graph neural networks and graph transformer architectures, stands out for its capability to capture intricate relationships and structures within clinical datasets. With diverse data—from patient records to imaging—graph AI models process data holistically by viewing modalities and entities within them as nodes interconnected by their relationships. Graph AI facilitates model transfer across clinical tasks, enabling models to generalize across patient populations without additional parameters and with minimal to no retraining. However, the importance of human-centered design and model interpretability in clinical decision-making cannot be overstated. Since graph AI models capture information through localized neural transformations defined on relational datasets, they offer both an opportunity and a challenge in elucidating model rationale. Knowledge graphs can enhance interpretability by aligning model-driven insights with medical knowledge. Emerging graph AI models integrate diverse data modalities through pretraining, facilitate interactive feedback loops, and foster human–AI collaboration, paving the way toward clinically meaningful predictions.

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MultiGML: Multimodal graph machine learning for prediction of adverse drug events

Cited by 4 publications

References 89 publications

Attention is all you need: utilizing attention in AI-enabled drug discovery

Attention is all you need: utilizing attention in AI-enabled drug discovery

Pharmacological profiles of neglected tropical disease drugs

Graph Artificial Intelligence in Medicine

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