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Abstract
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.
