1 research outputs found
Generalized Workflow for Generating Highly Predictive in Silico Off‑Target Activity Models
Chemical structure data and corresponding
measured bioactivities
of compounds are nowadays easily available from public and commercial
databases. However, these databases contain heterogeneous data from
different laboratories determined under different protocols and, in
addition, sometimes even erroneous entries. In this study, we evaluated
the use of data from bioactivity databases for the generation of high
quality in silico models for off-target mediated toxicity as a decision
support in early drug discovery and crop-protection research. We chose
human acetylcholinesterase (hAChE) inhibition as an exemplary end
point for our case study. A standardized and thorough quality management
routine for input data consisting of more than 2,200 chemical entities
from bioactivity databases was established. This procedure finally
enables the development of predictive QSAR models based on heterogeneous
in vitro data from multiple laboratories. An extended applicability
domain approach was used, and regression results were refined by an
error estimation routine. Subsequent classification augmented by special
consideration of borderline candidates leads to high accuracies in
external validation achieving correct predictive classification of
96%. The standardized process described herein is implemented as a
(semi)automated workflow and thus easily transferable to other off-targets
and assay readouts