Theoretical and Computational Chemistry

Application of Bioactivity Profile Based Fingerprints for Building Machine Learning Models

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.

Version notes

pre-revised

Content

Thumbnail image of HTSFP_manusAndSuppInfo_NSturm.pdf