SMILES-Mamba: Chemical Mamba Foundation Models for Drug ADMET Prediction
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866915891241811968 |
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| author | Xu, Bohao Lu, Yingzhou Li, Chenhao Yue, Ling Wang, Xiao Fu, Tianfan Shen, Minjie Chen, Lulu |
| author_facet | Xu, Bohao Lu, Yingzhou Li, Chenhao Yue, Ling Wang, Xiao Fu, Tianfan Shen, Minjie Chen, Lulu |
| contents | In drug discovery, predicting the absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of small-molecule drugs is critical for ensuring safety and efficacy. However, the process of accurately predicting these properties is often resource-intensive and requires extensive experimental data. To address this challenge, we propose SMILES-Mamba, a two-stage model that leverages both unlabeled and labeled data through a combination of self-supervised pretraining and fine-tuning strategies. The model first pre-trains on a large corpus of unlabeled SMILES strings to capture the underlying chemical structure and relationships, before being fine-tuned on smaller, labeled datasets specific to ADMET tasks. Our results demonstrate that SMILES-Mamba exhibits competitive performance across 22 ADMET datasets, achieving the highest score in 14 tasks, highlighting the potential of self-supervised learning in improving molecular property prediction. This approach not only enhances prediction accuracy but also reduces the dependence on large, labeled datasets, offering a promising direction for future research in drug discovery. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_05696 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SMILES-Mamba: Chemical Mamba Foundation Models for Drug ADMET Prediction Xu, Bohao Lu, Yingzhou Li, Chenhao Yue, Ling Wang, Xiao Fu, Tianfan Shen, Minjie Chen, Lulu Machine Learning Quantitative Methods In drug discovery, predicting the absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of small-molecule drugs is critical for ensuring safety and efficacy. However, the process of accurately predicting these properties is often resource-intensive and requires extensive experimental data. To address this challenge, we propose SMILES-Mamba, a two-stage model that leverages both unlabeled and labeled data through a combination of self-supervised pretraining and fine-tuning strategies. The model first pre-trains on a large corpus of unlabeled SMILES strings to capture the underlying chemical structure and relationships, before being fine-tuned on smaller, labeled datasets specific to ADMET tasks. Our results demonstrate that SMILES-Mamba exhibits competitive performance across 22 ADMET datasets, achieving the highest score in 14 tasks, highlighting the potential of self-supervised learning in improving molecular property prediction. This approach not only enhances prediction accuracy but also reduces the dependence on large, labeled datasets, offering a promising direction for future research in drug discovery. |
| title | SMILES-Mamba: Chemical Mamba Foundation Models for Drug ADMET Prediction |
| topic | Machine Learning Quantitative Methods |
| url | https://arxiv.org/abs/2408.05696 |