Machine learning-based prediction of drug approvals using molecular, physicochemical, clinical trial, and patent-related features

Background: Drug development productivity has been declining lately due to elevated costs and reduced discovery rates. Therefore, pharmaceutical companies have been seeking alternative ways to determine and evaluate drug candidates.Research design and methods: In this work, we proposed a new computational approach to directly predict the regulatory approval of drug candidates, and implemented it as a method called "DrugApp.' To accomplish this task, we employed multiple types of features including molecular and physicochem-ical properties of drug candidates, together with clinical trial and patent-related features, which are then processed by random forest classifiers to train our disease group-specific approval prediction models.Results: Our evaluations indicated DrugApp has a high and robust prediction performance. Within a use-case study, we showed our method can predict phase IV trial drugs that are later withdrawn from the market due to severe side effects. Finally, we used DrugApp models to forecast the approval of drug candidates that are currently in phases I/II/III of clinical trials.Conclusions: We hope that our study will aid the research community in terms of evaluating and improving the process of drug development. The datasets, source code, results, and pre-trained models of DrugApp are freely available at https://github.com/HUBioDataLab/DrugApp.