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Superbio.ai & ChemBERTa |

ChemBERTa: Skin

4
1
Drug Design
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Step 1: Upload your data

Train Data

Drag your file(s) or upload
  • Your file can be in the following formats:csv
    • Data should be in csv format with a single column named smiles. The molecule must be in smiles format
or
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Step 2: Set Parameters
Canonical SMILES
Step 3: Complete run profile

Predicts possible undesirable side effects on the skin and subcutaneous tissue of molecules (SMILES format). Our model was fine-tuned with the SIDER dataset and uses the ChemBERTa transformer. We follow the standard "System Organ Classes" nomenclature.

Example use case: Drug interactions and side effects

Technology: ChemBERTa Transformer of 77 million molecules.

Metrics: Test-AUC 0.73

Citation:
Ahmad, Walid, et al. "ChemBERTa-2: Towards chemical foundation models." arXiv preprint arXiv:2209.01712 (2022). SIDER-dataset: http://sideeffects.embl.de/
Released:
Dec-15-2022
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