Knowledge-Based Strategy to Improve Ligand Pose Prediction Accuracy for Lead Optimization

Accurately predicting how a small
molecule binds to its target
protein is an essential requirement for structure-based drug design
(SBDD) efforts. In structurally enabled medicinal chemistry programs,
binding pose prediction is often applied to ligands after a related
compound’s crystal structure bound to the target protein has
been solved. In this article, we present an automated pose prediction
protocol that makes extensive use of existing X-ray ligand information.
It uses spatial restraints during docking based on maximum common
substructure (MCS) overlap between candidate molecule and existing
X-ray coordinates of the related compound. For a validation data set
of 8784 docking runs, our protocol’s pose prediction accuracy
(80–82%) is almost two times higher than that of one unbiased
docking method software (43%). To demonstrate the utility of this
protocol in a project setting, we show its application in a chronological
manner for a number of internal drug discovery efforts. The accuracy
and applicability of this algorithm (>70% of cases) to medicinal
chemistry
efforts make this the approach of choice for pose prediction in lead
optimization programs.