Intelligent Classifier: a Tool to Impel Drug Technology Transfer from Academia to Industry
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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.
KeywordsUniversity patents Pharmaceutical patents Technology transfer Patent licensing Machine learning prediction Support vector machine
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.
- 11.Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: machine learning in Python. J Mach Learn Res. 2011;12:2825–30.Google Scholar
- 13.Cervantes M. Academic patenting: how universities and public research organizations are using their intellectual property to boost research and spur innovative start-ups. WIPO Small And Medium-sized Enterprises Documents 2003. http://www.wipo.int/sme/en/documents/academic_patenting.html. Accessed 1 April 2018.
- 18.Smith DKW. A new methodology for citation dependent patent evaluations. Carleton University. 2014. (Electronic, M.Sc. thesis) http://curve.carleton.ca/system/files/theses/31557.pdf. Accessed 1 Oct 2017.
- 20.Pai PF, Hong WC, Change PT, Chen CT. The application of support vector machines to forecast tourist arrivals in Barbados: an empirical study. Int J Manag. 2006;23(2):375.Google Scholar
- 21.Tseng CY, Chen MS. Incremental SVM model for spam detection on dynamic email social networks. IEEE CSE Int Conf. 2009;4:128–35.Google Scholar
- 24.Powers DM. Evaluation: from precision, recall and F-measure to ROC, 2011 informedness, markedness and correlation. J Mach Learn Tech. 2011;2(1):37–63.Google Scholar