Computational Drug Discovery and Design

match that overlay on the corresponding oxygen atoms in

DKPES (Fig.2).

To measure the biological activity of the 56 DKPES inhibitor

candidates selected with the above screening and prioritization

criteria, we used an electro-olfactogram (EOG) [10]. The

measured EOG response, acting as the target variable in this data-

set, was the percentage reduction of the standard DKPES signal

when the sea lamprey nose was perfused with a known concentra-

tion (10
^6 M) of inhibitor candidate (computed as the average of

2–5 experimental replicates). Figure5 shows four of the 56 mole-

cules for illustrative purposes, two actives and two non-actives, with

the percent DKPES olfactory inhibition for each. In the context of

Fig. 3Summary of the virtual screening workflow to prioritize molecules for electro-olfactogram (EOG) assays.
The Screenlamp toolkit 10 was used to prepare the virtual screening
pipeline, including ROCS v3.2.0.4 (OpenEye Scientific Software, Santa Fe, NM;https://www.eyesopen.com/
rocs), OMEGA v2.4.6 (OpenEye Scientific Software, Santa Fe, NM;https://www.eyesopen.com/omega), and
QUACPAC/molcharge (OpenEye Scientific Software, Santa Fe, NM;https://docs.eyesopen.com/toolkits/python/
quacpactk/molchargetheory.html). The screening databases of small molecules, mostly commercially avail-
able, were the drug-like molecules in ZINC12 (http://zinc.docking.org)[20], steroid structures from Chemical
Abstracts Service Registry (CAS;https://www.cas.org), and steroid structures from the Cambridge Structural
Database (CSD;https://www.ccdc.cam.ac.uk/solutions/csd-system/components/csd/)[21]

312 Sebastian Raschka et al.