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license: gpl-2.0 |
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# Model Card for FupBERT |
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A descriptor free approach to predicting fraction unbound in human plasma. |
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## Model Details |
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### Model Description |
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Chemical specific parameters are either measured _in vitro_ or estimated using quantitative |
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structure–activity relationship (QSAR) models. The existing body of QSAR work relies on extracting a |
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set of descriptors or fingerprints, subset selection, and training a machine learning model. In this work, |
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we used a state-of-the-art natural language processing model, Bidirectional Encoder Representations from Transformers |
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(BERT), that allowed us to circumvent the need for calculation of these chemical descriptors. In this approach, |
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simplified molecular-input line-entry system (SMILES) strings were embedded in a high dimensional space using a |
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two-stage training approach. The model was first pre-trained on a masked SMILES token task and then fine-tuned on |
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a QSAR prediction task. The pre-training task learned meaningful high dimensional embeddings based upon the relationships |
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between the chemical tokens in the SMILES strings derived from the "in-stock" portion of the ZINC 15 dataset – a |
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large dataset of commercially available chemicals. The fine-tuning task then perturbed the pre-trained embeddings |
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to facilitate prediction of a specific QSAR endpoint of interest. The power of this model stems from the ability |
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to reuse the pre-trained model for multiple different fine-tuning tasks, reducing the computational burden of developing |
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multiple models for different endpoints. We used our framework to develop a predictive model for fraction unbound |
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in human plasma (fup). This approach is flexible, requires minimum domain expertise, and can be generalized for |
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other parameters of interest for rapid and accurate estimation of absorption, distribution, metabolism, excretion, and toxicity (ADMET). |
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- **Developed by:** Michael Riedl, Sayak Mukherjee, and Mitch Gauthier |
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- **Model type:** BERT |
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### Model Sources |
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<!-- Provide the basic links for the model. --> |
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- **Paper:** Riedl, Michael, Sayak Mukherjee, and Mitch Gauthier. "Descriptor-Free Deep Learning QSAR Model for the Fraction Unbound in Human Plasma." Molecular Pharmaceutics (2023). |
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- **Demo:** https://huggingface.co/spaces/battelle/FupBERT_Space |
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## Citation |
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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**BibTeX:** |
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``` |
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@article{riedl2023descriptor, |
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title={Descriptor-Free Deep Learning QSAR Model for the Fraction Unbound in Human Plasma}, |
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author={Riedl, Michael and Mukherjee, Sayak and Gauthier, Mitch}, |
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journal={Molecular Pharmaceutics}, |
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publisher={ACS Publications} |
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} |
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``` |
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## Model Card Contact |
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riedl@battelle.org |
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