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README.md

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