SMILES-Mamba: Chemical Mamba Foundation Models for Drug ADMET Prediction
<|reference_start|>SMILES-Mamba: Chemical Mamba Foundation Models for Drug ADMET Prediction: 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.<|reference_end|>
@article{xu2024smiles-mamba:,
    title={SMILES-Mamba: Chemical Mamba Foundation Models for Drug ADMET Prediction},
    author={Bohao Xu, Yingzhou Lu, Chenhao Li, Ling Yue, Xiao Wang, Nan Hao,
  Tianfan Fu, Jim Chen},
    journal={arXiv preprint arXiv:2408.05696},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.05696},
    primaryClass={cs.LG q-bio.QM}
}