Multi-channel learning for integrating structural hierarchies into context-dependent molecular representation
Abstract Reliable molecular property prediction is essential for various scientific endeavors and industrial applications, such as drug discovery. However, the data scarcity, combined with the highly non-linear causal relationships between physicochemical and biological properties and conventional m...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-55082-4 |
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| _version_ | 1850098999080517632 |
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| author | Yue Wan Jialu Wu Tingjun Hou Chang-Yu Hsieh Xiaowei Jia |
| author_facet | Yue Wan Jialu Wu Tingjun Hou Chang-Yu Hsieh Xiaowei Jia |
| author_sort | Yue Wan |
| collection | DOAJ |
| description | Abstract Reliable molecular property prediction is essential for various scientific endeavors and industrial applications, such as drug discovery. However, the data scarcity, combined with the highly non-linear causal relationships between physicochemical and biological properties and conventional molecular featurization schemes, complicates the development of robust molecular machine learning models. Self-supervised learning (SSL) has emerged as a popular solution, utilizing large-scale, unannotated molecular data to learn a foundational representation of chemical space that might be advantageous for downstream tasks. Yet, existing molecular SSL methods largely overlook chemical knowledge, including molecular structure similarity, scaffold composition, and the context-dependent aspects of molecular properties when operating over the chemical space. They also struggle to learn the subtle variations in structure-activity relationship. This paper introduces a multi-channel pre-training framework that learns robust and generalizable chemical knowledge. It leverages the structural hierarchy within the molecule, embeds them through distinct pre-training tasks across channels, and aggregates channel information in a task-specific manner during fine-tuning. Our approach demonstrates competitive performance across various molecular property benchmarks and offers strong advantages in particularly challenging yet ubiquitous scenarios like activity cliffs. |
| format | Article |
| id | doaj-art-bda65690da1c4fabac046be4f4649a39 |
| institution | DOAJ |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-bda65690da1c4fabac046be4f4649a392025-08-20T02:40:35ZengNature PortfolioNature Communications2041-17232025-01-0116111310.1038/s41467-024-55082-4Multi-channel learning for integrating structural hierarchies into context-dependent molecular representationYue Wan0Jialu Wu1Tingjun Hou2Chang-Yu Hsieh3Xiaowei Jia4University of Pittsburgh, Department of Computer ScienceInnovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang UniversityInnovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang UniversityInnovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang UniversityUniversity of Pittsburgh, Department of Computer ScienceAbstract Reliable molecular property prediction is essential for various scientific endeavors and industrial applications, such as drug discovery. However, the data scarcity, combined with the highly non-linear causal relationships between physicochemical and biological properties and conventional molecular featurization schemes, complicates the development of robust molecular machine learning models. Self-supervised learning (SSL) has emerged as a popular solution, utilizing large-scale, unannotated molecular data to learn a foundational representation of chemical space that might be advantageous for downstream tasks. Yet, existing molecular SSL methods largely overlook chemical knowledge, including molecular structure similarity, scaffold composition, and the context-dependent aspects of molecular properties when operating over the chemical space. They also struggle to learn the subtle variations in structure-activity relationship. This paper introduces a multi-channel pre-training framework that learns robust and generalizable chemical knowledge. It leverages the structural hierarchy within the molecule, embeds them through distinct pre-training tasks across channels, and aggregates channel information in a task-specific manner during fine-tuning. Our approach demonstrates competitive performance across various molecular property benchmarks and offers strong advantages in particularly challenging yet ubiquitous scenarios like activity cliffs.https://doi.org/10.1038/s41467-024-55082-4 |
| spellingShingle | Yue Wan Jialu Wu Tingjun Hou Chang-Yu Hsieh Xiaowei Jia Multi-channel learning for integrating structural hierarchies into context-dependent molecular representation Nature Communications |
| title | Multi-channel learning for integrating structural hierarchies into context-dependent molecular representation |
| title_full | Multi-channel learning for integrating structural hierarchies into context-dependent molecular representation |
| title_fullStr | Multi-channel learning for integrating structural hierarchies into context-dependent molecular representation |
| title_full_unstemmed | Multi-channel learning for integrating structural hierarchies into context-dependent molecular representation |
| title_short | Multi-channel learning for integrating structural hierarchies into context-dependent molecular representation |
| title_sort | multi channel learning for integrating structural hierarchies into context dependent molecular representation |
| url | https://doi.org/10.1038/s41467-024-55082-4 |
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