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J Chem Inf Model. 2019 Mar 25;59(3):1221-1229. doi: 10.1021/acs.jcim.8b00640. Epub 2018 Nov 8.

Drug Discovery Maps, a Machine Learning Model That Visualizes and Predicts Kinome-Inhibitor Interaction Landscapes.

Author information

1
Molecular Physiology, Leiden Institute of Chemistry , Leiden University , 2333 CC Leiden , The Netherlands.
2
Department of Cell and Chemical Biology , Leiden University Medical Centre , 2333 ZC Leiden , The Netherlands.
3
Drug and Target Discovery , Leiden Academic Centre for Drug Research, Leiden University , 2333 CC Leiden , The Netherlands.
4
Pivot Park Screening Centre , 5349 AB Oss , The Netherlands.

Abstract

The interpretation of high-dimensional structure-activity data sets in drug discovery to predict ligand-protein interaction landscapes is a challenging task. Here we present Drug Discovery Maps (DDM), a machine learning model that maps the activity profile of compounds across an entire protein family, as illustrated here for the kinase family. DDM is based on the t-distributed stochastic neighbor embedding (t-SNE) algorithm to generate a visualization of molecular and biological similarity. DDM maps chemical and target space and predicts the activities of novel kinase inhibitors across the kinome. The model was validated using independent data sets and in a prospective experimental setting, where DDM predicted new inhibitors for FMS-like tyrosine kinase 3 (FLT3), a therapeutic target for the treatment of acute myeloid leukemia. Compounds were resynthesized, yielding highly potent, cellularly active FLT3 inhibitors. Biochemical assays confirmed most of the predicted off-targets. DDM is further unique in that it is completely open-source and available as a ready-to-use executable to facilitate broad and easy adoption.

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