Intelligent Classifier: a Tool to Impel Drug Technology Transfer from Academia to Industry

Abstract

Pharmaceutical technology transfer is one of the components of pharmaceutical innovation. Currently, a gap exists in pharmaceutical technology transfer from academia to industry. This study aims to develop an objective model to identify valuable pharmaceutical technologies for transferring in order to drive pharmaceutical innovation. We created a support vector machine classifier model using the data of pharmaceutical patents held by universities to predict the licensing outcomes of those patents. We collected data on 369 United States (US) pharmaceutical patents, using 142 licensed patents as the positive samples and 227 unlicensed patents as the negative samples. We also collected the licensing data of the patents, and the distinguished patent features were selected for model training and generation. Upon optimization, the machine learning model was evaluated using different scoring methods. Our support vector machine-based model achieved a fairly good performance of 82.50% in precision and 88.89% in specificity. To the best of our knowledge, our study is the first to apply the machine learning approach to predict the licensing outcomes for pharmaceutical patent valuation and technology transfer. Our work is a good alternative to the current patent valuation methods available in the market, and it could be further developed for practical use in real business contexts.