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open access articleDrugs have become an essential part of our lives due to their ability to improve people’s
health and quality of life. However, for many diseases, approved drugs are not yet available
or existing drugs have undesirable side effects, making the pharmaceutical industry strive to
discover new drugs and active compounds. The development of drugs is an expensive
process, which typically starts with the detection of candidate molecules (screening) for an
identified protein target. To this end, the use of high-performance screening techniques has
become a critical issue in order to palliate the high costs. Therefore, the popularity of
computer-based screening (often called virtual screening or in-silico screening) has rapidly
increased during the last decade. A wide variety of Machine Learning (ML) techniques has
been used in conjunction with chemical structure and physicochemical properties for
screening purposes including (i) simple classifiers, (ii) ensemble methods, and more recently
(iii) Multiple Classifier Systems (MCS). In this work, we apply an MCS for virtual screening
(D2-MCS) using circular fingerprints. We applied our technique to a dataset of cannabinoid
CB2 ligands obtained from the ChEMBL database. The HTS collection of Enamine
(1.834.362 compounds), was virtually screened to identify 48.432 potential active molecules
using D2-MCS. This list was subsequently clustered based on circular fingerprints and from
each cluster, the most active compound was maintained. From these, the top 60 were kept,
and 21 novel compounds were purchased. Experimental validation confirmed six highly
active hits (>50% displacement at 10 μM and subsequent Ki determination) and an
additional five medium active hits (>25% displacement at 10 μM). D2-MCS hence provided a
hit rate of 29% for highly active compounds and an overall hit rate of 52%
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