The DOI matches its Crossref record; 105 of the 105 references it cites were found in a registry or an index.
Deep learning methods for molecular representation and property prediction
Abstract
With advances in artificial intelligence (AI) methods, computer-aided drug design (CADD) has developed rapidly in recent years. Effective molecular representation and accurate property prediction are crucial tasks in CADD workflows. In this review, we summarize contemporary applications of deep learning (DL) methods for molecular representation and property prediction. We categorize DL methods according to the format of molecular data (1D, 2D, and 3D). In addition, we discuss some common DL models, such as ensemble learning and transfer learning, and analyze the interpretability methods for these models. We also highlight the challenges and opportunities of DL methods for molecular representation and property prediction.
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Cited by
A review of large language models and autonomous agents in chemistry
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Wang, Y., Wang, Z., Yu, X., Wang, X., Song, J., Yu, D.-J., & Ge, F. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae658 · Cited by 25
Metadata
- Authors
- Li, Z., Jiang, M., Wang, S., & Zhang, S.
- Venue
- Drug Discovery Today
- Publisher
- Elsevier BV
- Published
- 2022-12
- Volume
- 27
- Issue
- 12
- Pages
- 103373
- ISSN
- 1359-6446 (ISSN-L)
- OpenAlex
- W4297179162
- Cited by
- 279
- References
- 105
- FWCI
- 26.99
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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