Summary of Smiles-mamba: Chemical Mamba Foundation Models For Drug Admet Prediction, by Bohao Xu et al.
SMILES-Mamba: Chemical Mamba Foundation Models for Drug ADMET Prediction
by Bohao Xu, Yingzhou Lu, Chenhao Li, Ling Yue, Xiao Wang, Nan Hao, Tianfan Fu, Jim Chen
First submitted to arxiv on: 11 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Quantitative Methods (q-bio.QM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A machine learning-based framework called SMILES-Mamba is proposed to predict the ADMET properties of small-molecule drugs with improved accuracy and reduced reliance on labeled data. The two-stage model leverages self-supervised pretraining and fine-tuning strategies, first processing a large corpus of unlabeled SMILES strings to capture chemical structure and relationships, then fine-tuning on smaller labeled datasets for specific ADMET tasks. Experimental results show competitive performance across 22 ADMET datasets, achieving the highest score in 14 tasks, highlighting the potential of self-supervised learning in molecular property prediction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SMILES-Mamba is a new way to predict how well drugs work and are safe. Right now, it takes a lot of time and data to figure out these things. This new model uses a special kind of training that doesn’t need as much labeled data. It first looks at many drug molecules to learn about their structure and relationships, then adjusts its predictions based on smaller amounts of labeled data. The results are very promising – it did better than other methods in 14 out of 22 tests. |
Keywords
» Artificial intelligence » Fine tuning » Machine learning » Pretraining » Self supervised
