10.1021/acs.jcim.5b00186.s002
Cen Gao
Cen
Gao
Nels Thorsteinson
Nels
Thorsteinson
Ian Watson
Ian
Watson
Jibo Wang
Jibo
Wang
Michal Vieth
Michal
Vieth
Knowledge-Based Strategy to Improve Ligand Pose Prediction
Accuracy for Lead Optimization
American Chemical Society
2015
prediction
drug discovery efforts
docking
ligand
program
accuracy
molecule
compound
SBDD
Ligand Pose Prediction Accuracy
target protein
MCS
2015-07-27 00:00:00
Dataset
https://acs.figshare.com/articles/dataset/Knowledge_Based_Strategy_to_Improve_Ligand_Pose_Prediction_Accuracy_for_Lead_Optimization/2146651
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.