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README.md

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+---
+license: mit
+pipeline_tag: tabular-regression
+tags:
+- chemistry
+- microbiology
+- antibiotics
+library_name: duvida
+datasets:
+- scbirlab/thomas-2018-spark-wt
+---
+
+# Predictor of _Brucella abortus_ MICs 
+
+_Updated:_ Fri 28 Mar 18:11:12 GMT 2025
+
+Trained on the _Brucella abortus_, WT accumulator phenotype subset of the [human-curated SPARK dataset](https://doi.org/10.1021/acsinfecdis.8b00193) ( rows in total for _Brucella abortus_).
+
+## Model details
+
+This model was trained using [our Duvida framework](https://github.com/scbirlab/duvida), 
+as a result of hyperparameter searches and selecting the model that performs best on unseen test data 
+(from a scaffold split). 
+
+Duvida also saves the training data in this checkpoint to allows the calculation of uncertainty metrics 
+based on that training data.
+
+This model is the best regression model from a hyperparameter search, determined
+by Spearman's $\rho$ on a held-out test set not used in training or early stopping. 
+
+### Model architecture
+
+- **Regression**
+
+```json
+
+{
+    "dropout": 0.2,
+    "ensemble_size": 10,
+    "extra_featurizers": null,
+    "learning_rate": 0.0001,
+    "model_class": "ChempropModelBox",
+    "n_hidden": 5,
+    "n_units": 16,
+    "use_2d": true,
+    "use_fp": true
+}
+```
+
+### Model usage
+
+You can use this model with:
+
+```python
+from duvida.autoclasses import AutoModelBox
+modelbox = AutoModelBox.from_pretrained("hf://scbirlab/spark-dv-2503-babo")
+modelbox.predict(filename=..., inputs=[...], columns=[...])  # make predictions on your own data
+```
+
+## Training details
+
+- **Dataset:** [SPARK, WT accumulator, _Brucella abortus_ subset](https://huggingface.co/datasets/scbirlab/thomas-2018-spark-wt)
+- **Input column:** smiles
+- **Output column:** pmic
+- **Split type:** Murcko scaffold
+- **Split proportions:** 
+    - 70% training (6963 rows)
+    - 15% validation (for early stopping) (1491 rows)
+    - 15% test (for selecting hyperparameters) (1492 rows)
+
+Here is the training log:
+
+<img src="training-log.png" width=450>
+
+And these are the evaluation scores.
+
+Train (6963 rows):
+
+```json
+
+{
+    "Pearson r": 0.5503574788961993,
+    "RMSE": 0.053899530321359634,
+    "Spearman rho": 0.9346845985202912
+}
+```
+
+Validation (1491 rows):
+
+```json
+
+{
+    "Pearson r": 0.4224614855497885,
+    "RMSE": 0.06130664795637131,
+    "Spearman rho": 0.88412899537977
+}
+```
+
+
+Test (1492 rows):
+
+```json
+
+{
+    "Pearson r": 0.4829411208985374,
+    "RMSE": 0.06526102125644684,
+    "Spearman rho": 0.9303547327209174
+}
+```
+
+## Training data details
+
+The training data were collated by the authors of:
+
+> Joe Thomas, Marc Navre, Aileen Rubio, and Allan Coukell
+> Shared Platform for Antibiotic Research and Knowledge: A Collaborative Tool to SPARK Antibiotic Discovery
+> ACS Infectious Diseases 2018 4 (11), 1536-1539
+> DOI: 10.1021/acsinfecdis.8b00193
+
+We cleaned the original SPARK dataset to subset the most relevant columns, remove empty values, 
+give succint column titles, and split by species.
+
+This particular dataset retains only measurements on bacteria with wild-type accumulation phenotypes.
+
+### Dataset Sources
+
+- **Repository:** https://www.collaborativedrug.com/spark-data-downloads
+- **Paper:** https://doi.org/10.1021/acsinfecdis.8b00193
+
+### Data Collection and Processing
+
+Data were processed using [schemist](https://github.com/scbirlab/schemist), a tool for processing chemical datasets.
+
+The SMILES strings have been canonicalized, and split into training (70%), validation (15%), and test (15%) sets 
+by Murcko scaffold for each species with more than 1000 entries. Additional features like molecular weight and 
+topological polar surface area have also been calculated.
+
+### Who are the source data producers?
+
+Joe Thomas, Marc Navre, Aileen Rubio, and Allan Coukell

data-config.json

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+    "_in_key": "inputs",
+    "_input_cols": [
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+    ],
+    "_label_cols": [
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+    ],
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+    ]
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