An inductive graph neural network model for compound–protein interaction prediction based on a homogeneous graph

Wan, Xinyue; Wu, Xiaolong; Wang, Dingyan; Tan, Xin; Liu, Xiaohong; Fu, Zunyun; Jiang, Hualiang; Zheng, Mingyue; Li, Xutong

doi:10.1093/bib/bbac073

Cited by 12 publications

(9 citation statements)

References 60 publications

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“…However, the applicability of existing proteochemometric methods is still limited to relatively well-studied regions of the protein space, enriched with ligandable proteins for which a substantial amount of experimental data is available. In practice, the performance of proteochemometric methods may vary depending on protein families, with a bias toward protein families that are already considered to be ligandable . Extrapolation to genuinely novel targets, belonging to unliganded regions of the sequence space, is a major challenge for current supervised AI/ML methods.…”

Section: Empowering Ligand Discovery With Proteomics and Ai/mlmentioning

confidence: 99%

“…In practice, the performance of proteochemometric methods may vary depending on protein families, with a bias toward protein families that are already considered to be ligandable. 120 Extrapolation to genuinely novel targets, belonging to unliganded regions of the sequence space, is a major challenge for current supervised AI/ML methods. In this line, passing chemoproteomics data through these models may increase their domain of applicability, 121 since these high-throughput assays are likely to introduce new evidence for unseen targets and target families.…”

Section: Empowering Ligand Discovery Withmentioning

confidence: 99%

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Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence

2023

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Only around 20% of the human proteome is considered to be druggable with small-molecule antagonists. This leaves some of the most compelling therapeutic targets outside the reach of ligand discovery. The concept of targeted protein degradation (TPD) promises to overcome some of these limitations. In brief, TPD is dependent on small molecules that induce the proximity between a protein of interest (POI) and an E3 ubiquitin ligase, causing ubiquitination and degradation of the POI. In this perspective, we want to reflect on current challenges in the field, and discuss how advances in multiomics profiling, artificial intelligence, and machine learning (AI/ML) will be vital in overcoming them. The presented roadmap is discussed in the context of small-molecule degraders but is equally applicable for other emerging proximity-inducing modalities.

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Section: Empowering Ligand Discovery With Proteomics and Ai/mlmentioning

confidence: 99%

Section: Empowering Ligand Discovery Withmentioning

confidence: 99%

Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence

2023

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“…Recent work proposes a new computational approach called CPI-IGAE for predicting compound−protein interactions (CPIs) in drug development. 243 The approach transforms a compound−protein heterogeneous graph into a homogeneous graph and uses an Inductive Graph AggrEgator (IGAE)-based framework to learn low-dimensional representations of compounds and proteins in an end-to-end manner. 243 The results show that CPI-IGAE outperforms some state-of-theart methods, and the model architecture and feature extraction process are advantageous, as validated by an ablation study and visualization of embeddings.…”

Section: Gnnmentioning

confidence: 99%

“…243 The approach transforms a compound−protein heterogeneous graph into a homogeneous graph and uses an Inductive Graph AggrEgator (IGAE)-based framework to learn low-dimensional representations of compounds and proteins in an end-to-end manner. 243 The results show that CPI-IGAE outperforms some state-of-theart methods, and the model architecture and feature extraction process are advantageous, as validated by an ablation study and visualization of embeddings. Some of the top-ranked CPIs predicted by CPI-IGAE have been validated by recent literature reviews.…”

Section: Gnnmentioning

confidence: 99%

“…Recent work proposes a new computational approach called CPI-IGAE for predicting compound–protein interactions (CPIs) in drug development . The approach transforms a compound–protein heterogeneous graph into a homogeneous graph and uses an Inductive Graph AggrEgator (IGAE)-based framework to learn low-dimensional representations of compounds and proteins in an end-to-end manner .…”

Section: Deep Learning In Predictive Drug Toxicity Studiesmentioning

confidence: 99%

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A Review on the Recent Applications of Deep Learning in Predictive Drug Toxicological Studies

Sinha¹,

Ghosh²,

Sil

2023

Chem. Res. Toxicol.

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Drug toxicity prediction is an important step in ensuring patient safety during drug design studies. While traditional preclinical studies have historically relied on animal models to evaluate toxicity, recent advances in deep-learning approaches have shown great promise in advancing drug safety science and reducing animal use in preclinical studies. However, deep-learning-based approaches also face challenges in handling large biological data sets, model interpretability, and regulatory acceptance. In this review, we provide an overview of recent developments in deep-learning-based approaches for predicting drug toxicity, highlighting their potential advantages over traditional methods and the need to address their limitations. Deep-learning models have demonstrated excellent performance in predicting toxicity outcomes from various data sources such as chemical structures, genomic data, and high-throughput screening assays. The potential of deep learning for automated feature engineering is also discussed. This review emphasizes the need to address ethical concerns related to the use of deep learning in drug toxicity studies, including the reduction of animal use and ensuring regulatory acceptance. Furthermore, emerging applications of deep learning in drug toxicity prediction, such as predicting drug–drug interactions and toxicity in rare subpopulations, are highlighted. The integration of deep-learning-based approaches with traditional methods is discussed as a way to develop more reliable and efficient predictive models for drug safety assessment, paving the way for safer and more effective drug discovery and development. Overall, this review highlights the critical role of deep learning in predictive toxicology and drug safety evaluation, emphasizing the need for continued research and development in this rapidly evolving field. By addressing the limitations of traditional methods, leveraging the potential of deep learning for automated feature engineering, and addressing ethical concerns, deep-learning-based approaches have the potential to revolutionize drug toxicity prediction and improve patient safety in drug discovery and development.

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Machine learning for predicting protein properties: A comprehensive review

Wang,

Zhang,

Zhan

et al. 2024

Neurocomputing

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An inductive graph neural network model for compound–protein interaction prediction based on a homogeneous graph

Cited by 12 publications

References 60 publications

Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence

Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence

A Review on the Recent Applications of Deep Learning in Predictive Drug Toxicological Studies

Machine learning for predicting protein properties: A comprehensive review

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