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Journal of Pharmaceutical Innovation

, Volume 14, Issue 1, pp 28–34 | Cite as

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

  • Hui-Heng Lin
  • Defang Ouyang
  • Yuanjia HuEmail author
Original Article
  • 130 Downloads

Abstract

Purpose

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.

Methods

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.

Results

Our support vector machine-based model achieved a fairly good performance of 82.50% in precision and 88.89% in specificity.

Conclusions

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.

Keywords

University patents Pharmaceutical patents Technology transfer Patent licensing Machine learning prediction Support vector machine 

Notes

Funding

This study was funded by the grant MYRG2015-00145-ICMS-QRCM from the University of Macau.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

Research Involving Human Participants or Animals

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed Consent

Not applicable.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical SciencesUniversity of MacauMacauChina
  2. 2.The Research Center of National Drug Policy and EcosystemNanjingChina

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