Advanced deep learning methods for molecular property prediction

Chao Pang, Henry H.Y. Tong, Leyi Wei

Research output: Contribution to journalReview articlepeer-review

Abstract

The prediction of molecular properties is a crucial task in the field of drug discovery. Computational methods that can accurately predict molecular properties can significantly accelerate the drug discovery process and reduce the cost of drug discovery. In recent years, iterative updates in computing hardware and the rise of deep learning have created a new and effective path for molecular property prediction. Deep learning methods can leverage the vast amount of data accumulated over the years in drug discovery and do not require complex feature engineering. In this review, we summarize molecular representations and commonly used datasets in molecular property prediction models and present advanced deep learning methods for molecular property prediction, including state-of-the-art deep learning networks such as graph neural networks and Transformer-based models, as well as state-of-the-art deep learning strategies such as 3D pre-train, contrastive learning, multi-task learning, transfer learning, and meta-learning. We also point out some critical issues such as lack of datasets, low information utilization, and lack of specificity for diseases.

Original languageEnglish
Pages (from-to)395-404
Number of pages10
JournalQuantitative Biology
Volume11
Issue number4
DOIs
Publication statusPublished - Dec 2023

Keywords

  • dataset
  • deep learning
  • molecular property prediction
  • molecular representations

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