Artificial intelligence-based decision support model for new drug development planning.

ABSTRACT: New drug development guarantees a very high return on success, but the success rate is extremely low. Pharmaceutical companies have attempted to use various strategies to increase the success rate of drug development, but this goal has been difficult to achieve. In this study, we developed a model that can guide effective decision-making at the planning stage of new drug development by leveraging machine learning. The Drug Development Recommendation (DDR) model, we present here, is a hybrid model for recommending and/or predicting drug groups suitable for development by individual pharmaceutical companies. It combines association rule learning, collaborative filtering, and content-based filtering approaches for enterprise-customized recommendations. In the case of content-based filtering a

SUBMITTER: Jung YL 

PROVIDER: S-EPMC8902892 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature