Research on Potential Adverse Drug Reaction Forecasting Based on SAO Semantic Structure

With advances in medicine and biotechnology, the variety of available drugs has become more and more abundant. However, along with these innovations come complex adverse drug reactions (ADRs) as well. Extensive clinical trials are one of the best ways to reduce the incidence of drug reactions, but as the number of potential drug interactions grows, trials are becoming enormously time-consuming and costly. Hence, we set out to develop an alternative that could widely identify potential ADRs. Our solution is a 'drug-ADR' network built from semantic subject-action-object structures, combined with complex network analysis and link prediction methods to reveal likely adverse reactions. Some similarity calculating methods also be used to improve our prediction accuracy. Evaluations of the results against the medical literature show that the predictions produced can be used as a weather vane for clinical trials, helping to save R&D time and capital costs. In addition, the framework can be used to provide useful guidance for discovering new drug indications or to inform the development of new drugs.