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HTSFP_manusAndSuppInfo_NSturm.pdf (2.09 MB)
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Application of Bioactivity Profile Based Fingerprints for Building Machine Learning Models

preprint
submitted on 15.08.2018 and posted on 15.08.2018 by Noé Sturm, Jiangming Sun, Yves Vandriessche, Andreas Mayr, Günter Klambauer, Lars-Anders Carlson, Ola Engkvist, Hongming Chen
This article describes an application of high-throughput fingerprints (HTSFP) built upon industrial data accumulated over the years.
The fingerprint was used to build machine learning models (multi-task deep learning + SVM) for compound activity predictions towards a panel of 131 targets.
Quality of the predictions and the scaffold hopping potential of the HTSFP were systematically compared to traditional structural descriptors ECFP.

Funding

European Commission, Horizon 2020, Grant Agreement no. 671555

History

Email Address of Submitting Author

noejoseph.sturm@astrazeneca.com

Institution

AstraZeneca

Country

Sweden

ORCID For Submitting Author

0000-0002-9775-7872

Declaration of Conflict of Interest

no conflict of interest

Version Notes

pre-revised

Exports