Report 4

• Worked on the dataset
  • Discovered a minor bug, involving the get_pubchem_cid_and_smiles_using_name function, that could introduce some errors in the dataset
  • Only affected the entries associated with the Gao et al. datasets
  • Some drug names can have multiple SMILES associated with them and the bug caused the function to only retrieve the first SMILES available
  • To remove uncertainty from my dataset, drugs with multiple SMILES were decided to be removed
  • Instead of just fixing the mistake and removing the affected entries I decided to repopulate the whole dataset which was easy given my functions, but it took a couple of hours
  • Retrieved indicators from SIDER database
  • Produced one hot encodings for both side effects and indicators
• Performed Automated Google Searches
  • Performing specific drug-targeted google searches was proven to be ineffective. Would get irrelevant results
  • Performing different general queries to gather as much data as possible was proven to be ineffective as well. Too noisy
  • Decided to perform site-targeted queries to gather as much reliable data as possible without the need of manual verification
  • For each query roughly 100 URLs were retrieved and regular expressions were used to retrieved matches
  • Google seemed to discovered a bot was being used after roughly 100 URLs were retrieved
• Question/Topics to discuss
  • Next steps. ML tutorials and material?