General Purpose 2D and 3D Similarity Approach to Identify hERG Blockers

Screening compounds for human ether-à-go-go-related
gene
(hERG) channel inhibition is an important component of early stage
drug development and assessment. In this study, we developed a high-confidence
(p-value < 0.01) hERG prediction model based on a combined two-dimensional
(2D) and three-dimensional (3D) modeling approach. We developed a
3D similarity conformation approach (SCA) based on examining a limited
fixed number of pairwise 3D similarity scores between a query molecule
and a set of known hERG blockers. By combining 3D SCA with 2D similarity
ensemble approach (SEA) methods, we achieved a maximum sensitivity
in hERG inhibition prediction with an accuracy not achieved by either
method separately. The combined model achieved 69% sensitivity and
95% specificity on an independent external data set. Further validation
showed that the model correctly picked up documented hERG inhibition
or interactions among the Food and Drug Administration- approved drugs
with the highest similarity scoreswith 18 of 20 correctly
identified. The combination of ascertaining 2D and 3D similarity of
compounds allowed us to synergistically use 2D fingerprint matching
with 3D shape and chemical complementarity matching.