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Recent Advances in Toxicity Prediction: Applications of Deep Graph Learning.

Yuwei Miao1, Hehuan Ma1, Junzhou Huang1

  • 1Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, Texas 76019, United States.

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Deep graph learning models offer efficient and accurate drug toxicity prediction, accelerating drug development. These advanced methods provide better insights, improving model interpretation and generalization for safer drug discovery.

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Area of Science:

  • Computational chemistry
  • Pharmacology
  • Artificial intelligence in drug discovery

Background:

  • Drug development is costly and time-consuming, necessitating accurate toxicity prediction for safety and efficacy.
  • Deep graph learning (DGL) offers computational power and cost-efficiency for predicting drug toxicity.
  • Existing methods require comprehensive understanding of DGL components and applications in toxicology.

Purpose of the Study:

  • To bridge fundamental knowledge with advanced deep graph learning methods for drug toxicity prediction.
  • To provide a comprehensive overview of DGL components, including molecular descriptors, representations, metrics, validation, and datasets.
  • To review representative DGL studies and methods, focusing on GNN architectures and graph pretrained models.

Main Methods:

  • Summarizing essential components of DGL models for toxicity prediction.
  • Analyzing various graph-based molecular representations.
  • Introducing representative studies from the perspective of GNN architectures and graph pretrained models.

Main Results:

  • Deep graph models demonstrate superior accuracy and efficiency compared to other approaches.
  • DGL models offer more intuitive insights, enhancing model interpretation and generalization.
  • Graph pretrained models show promise in extracting features from large unlabeled molecular data for improved toxicity prediction.

Conclusions:

  • Deep graph learning is a powerful and efficient tool for advancing drug toxicity prediction.
  • Graph pretrained models are emerging as key enablers for enhancing downstream toxicity prediction tasks.
  • This review serves as a guide for researchers entering the field of DGL for drug toxicity prediction.