MOTIVATION: Adverse drug reactions (ADRs) are a central consideration during 
drug development. Here we present a machine learning classifier to prioritize 
ADRs for approved drugs and pre-clinical small-molecule compounds by combining 
chemical structure (CS) and gene expression (GE) features. The GE data is from 
the Library of Integrated Network-based Cellular Signatures (LINCS) L1000 
dataset that measured changes in GE before and after treatment of human cells 
with over 20 000 small-molecule compounds including most of the FDA-approved 
drugs. Using various benchmarking methods, we show that the integration of GE 
data with the CS of the drugs can significantly improve the predictability of 
ADRs. Moreover, transforming GE features to enrichment vectors of biological 
terms further improves the predictive capability of the classifiers. The most 
predictive biological-term features can assist in understanding the drug 
mechanisms of action. Finally, we applied the classifier to all  >20 000 
small-molecules profiled, and developed a web portal for browsing and searching 
predictive small-molecule/ADR connections.
AVAILABILITY AND IMPLEMENTATION: The interface for the adverse event predictions 
for the  >20 000 LINCS compounds is available at http://maayanlab.net/SEP-L1000/ 
CONTACT: avi.maayan@mssm.edu
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics 
online.