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.