AdaMR: Adaptable Molecular Representation for Unified Pre-training Strategy
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866911856431464448 |
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| author | Ding, Yan Cheng, Hao Ye, Ziliang Feng, Ruyi Tian, Wei Xie, Peng Zhang, Juan Gu, Zhongze |
| author_facet | Ding, Yan Cheng, Hao Ye, Ziliang Feng, Ruyi Tian, Wei Xie, Peng Zhang, Juan Gu, Zhongze |
| contents | We propose Adjustable Molecular Representation (AdaMR), a new large-scale uniform pre-training strategy for small-molecule drugs, as a novel unified pre-training strategy. AdaMR utilizes a granularity-adjustable molecular encoding strategy, which is accomplished through a pre-training job termed molecular canonicalization, setting it apart from recent large-scale molecular models. This adaptability in granularity enriches the model's learning capability at multiple levels and improves its performance in multi-task scenarios. Specifically, the substructure-level molecular representation preserves information about specific atom groups or arrangements, influencing chemical properties and functionalities. This proves advantageous for tasks such as property prediction. Simultaneously, the atomic-level representation, combined with generative molecular canonicalization pre-training tasks, enhances validity, novelty, and uniqueness in generative tasks. All of these features work together to give AdaMR outstanding performance on a range of downstream tasks. We fine-tuned our proposed pre-trained model on six molecular property prediction tasks (MoleculeNet datasets) and two generative tasks (ZINC250K datasets), achieving state-of-the-art (SOTA) results on five out of eight tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_06166 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | AdaMR: Adaptable Molecular Representation for Unified Pre-training Strategy Ding, Yan Cheng, Hao Ye, Ziliang Feng, Ruyi Tian, Wei Xie, Peng Zhang, Juan Gu, Zhongze Biomolecules Artificial Intelligence Machine Learning We propose Adjustable Molecular Representation (AdaMR), a new large-scale uniform pre-training strategy for small-molecule drugs, as a novel unified pre-training strategy. AdaMR utilizes a granularity-adjustable molecular encoding strategy, which is accomplished through a pre-training job termed molecular canonicalization, setting it apart from recent large-scale molecular models. This adaptability in granularity enriches the model's learning capability at multiple levels and improves its performance in multi-task scenarios. Specifically, the substructure-level molecular representation preserves information about specific atom groups or arrangements, influencing chemical properties and functionalities. This proves advantageous for tasks such as property prediction. Simultaneously, the atomic-level representation, combined with generative molecular canonicalization pre-training tasks, enhances validity, novelty, and uniqueness in generative tasks. All of these features work together to give AdaMR outstanding performance on a range of downstream tasks. We fine-tuned our proposed pre-trained model on six molecular property prediction tasks (MoleculeNet datasets) and two generative tasks (ZINC250K datasets), achieving state-of-the-art (SOTA) results on five out of eight tasks. |
| title | AdaMR: Adaptable Molecular Representation for Unified Pre-training Strategy |
| topic | Biomolecules Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2401.06166 |