This repository contains source code for paper "Towards early detection of adverse drugreactions: combining pre-clinical drug structuresand post-market safety reports" (accepted by BMC Medical Informatics andDecision Making).

Please kindly cite the paper if you use the code, datasets or any results in this repo or in the paper:

Liu, R., Zhang, P. Towards early detection of adverse drug reactions: combining pre-clinical 

drug structures and post-market safety reports. BMC Med Inform Decis Mak 19, 279 (2019) doi:10.1186/s12911-019-0999-1

In this paper, we propose a label propagation framework to enhance drug safety signals by combining pre-clinical drug chemical structures with post-marketing safety reports from FDA Adverse Event Reporting System (FAERS).

We apply the label propagation framework to four popular signal detection algorithms (PRR, ROR,MGPS, BCPNN) and find that our proposed framework generates more accurate drug safety signals than the corresponding baselines.

Fig. 1: The overall framework for label propagation based signal detection algorithms. It consists of three main steps: computing original drug safety signals from FAERS reports, constructing a drug-drug similarity network from pre-clinical drug structures, and generating enhanced drug safety signals through a label propagation process.

Datasets used in the paper:

• FAERS: a database that contains information on adverse event and medication error reports submitted to FDA. We use a curated and standardized version of FAERS data from 2004 to 2014 (Banda, Juan M. et al., 2017) [paper&data].
• PubChem: a public repository for information on chemical substances and their biological activities. The PubChem Compound database provides unique chemical structure information of drugs.
• SIDER: a database that contains information on marketed medicines and their recorded adverse drug reactions.

python run.py --input SignalScoresSource --method PRR05 --year all --eval_metrics all --split True

• --input, input original signal scores files.
• --method, signal detection algorithm (i.e., PRR, ROR, MGPS, BCPNN).
• --year, years of data used for model (i.e., all years data from 2004 to 2014 or data arranged by ending years).
• --eval_metrics, evaluation metrics (i.e., AUC, AUPR, Precision, Recall, etc.)
• --split, whether to split entire dataset into validation set and testing set.
• --output, output file.