Error code:   JWTInvalidSignature
Exception:    InvalidSignatureError
Message:      Signature verification failed
Traceback:    Traceback (most recent call last):
                File "/src/libs/libapi/src/libapi/jwt_token.py", line 286, in validate_jwt
                  decoded = jwt.decode(
                      jwt=token,
                  ...<2 lines>...
                      options=options,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 368, in decode
                  decoded = self.decode_complete(
                      jwt,
                  ...<8 lines>...
                      leeway=leeway,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 265, in decode_complete
                  decoded = self._jws.decode_complete(
                      jwt,
                  ...<3 lines>...
                      detached_payload=detached_payload,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 270, in decode_complete
                  self._verify_signature(
                  ~~~~~~~~~~~~~~~~~~~~~~^
                      signing_input,
                      ^^^^^^^^^^^^^^
                  ...<4 lines>...
                      options=merged_options,
                      ^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 417, in _verify_signature
                  raise InvalidSignatureError("Signature verification failed")
              jwt.exceptions.InvalidSignatureError: Signature verification failed

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Drug-Likeness Prediction Dataset (Based on DBPP-Predictor Data)

This dataset was created as part of a final project on drug-likeness prediction, based on the data from the DBPP-Predictor paper:

Gu, Y., Wang, Y., Zhu, K. et al. DBPP-Predictor: a novel strategy for prediction of chemical drug-likeness based on property profiles. J Cheminform 16, 4 (2024). https://doi.org/10.1186/s13321-024-00800-9

It includes curated molecular data, preprocessed RDKit descriptors, and training/test splits suitable for training classification models to distinguish drug-like from non-drug-like molecules.

Project Background

Drug-likeness refers to the potential of a small molecule to become a drug. Traditional rule-based approaches (e.g., Lipinski's Rule of Five) often fail to generalize across complex compounds. This project uses RDKit descriptors and AutoML (H2O) to construct a highly interpretable, generalizable classification model.

Dataset Description

The dataset is derived from the DBPP GitHub repository (https://github.com/yxgu2353/DBPP-Predictor.git). It includes:

5,147 drug-like molecules (FDA and globally approved drugs)
10,000 non-drug-like molecules sampled from ZINC

All molecules were standardized using MolVS(clean_data.py), the datasets were split by sklearn(split_dataset.py), and RDKit descriptors (216 features) were computed (Rdkit_descriptor.py).

Files:

'train_rdkit_descriptors.parquet'
'test_rdkit_descriptors.parquet'

Each file contains:

'label': 0 for non-drug, 1 for drug
'Standardized_SMILES'
216 numerical RDKit descriptors

Model Development

All models were trained using H2O AutoML with 10-fold cross-validation(model_constrcution.py). The top 3 models were also evaluated using an independent test set, and SHAP analysis was used to interpret the top structural features contributing to drug-likeness(model_analysis.py).

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