Application of Bioactivity Profile Based Fingerprints for Building Machine Learning Models

15 August 2018, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


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.


Machine learning
Deep Neural Network
hit identification
Virtual Screening
High-throughput Screening Fingerprint


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