Ligand-Based Structural Hypotheses for Virtual Screening
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Transcript of Ligand-Based Structural Hypotheses for Virtual Screening
Ligand-Based Structural Hypotheses for Virtual Screening
Ajay N. Jain
Uses the tool described in the pervious paper
Agenda• To investigate adequacy of the utility of a model
comprised by the overlap of known ligands for a given target in identifying novel ligands with high sensitivity and specificity– The target’s structure is not known– Justification: “Given a small number of potentially quite
flexible molecules of diverse chemical structures, one must generate a hypothesis consisting of a single pose for each input molecule such that the joint superposition of all molecules will lead to predictive models of biological activity”
Molecules Used:Chose 4 therapeutically interesting targets with
unknown three-dimensional structure, and identified ligands known to associate with those:
Positive Testing Set:
Control Test Sets
Estrogen Receptor Ligands
HSV-1 Thymidine kinase inhibitors
Control Test Sets Alignments
GPCR Models
GABAA Model
ROC Curves
TanimotoSurflex-Sim
TanimotoSurflex-Sim
TanimotoSurflex-Sim
TanimotoSurflex-Sim
Serotonin Model Muscarinic Model
Histamine Model GABAA Model
Examples of High Scoring Ligands
Selectivity of the Models
GABAA vs. GPCRGABAA vs. Random
Musc. vs. NonMusc. vs. Random
Hist. vs. NonHist. vs. Random
Serotonin Model Binding Affinity
Conclusions• “Offers a generally applicatble method for producing
ligand-based binding site hypotheses, which can be used directly for high-throughput virtual screening or to form the basis on which to construct more detailed models of molecular activity”
• “Performance in terms of screening utility is comparable to that of many structure-based molecular docking techniques, but the best docking methods are capable of better sensitivity and specificity”
Applicability• “where many existing ligands are known but where
they share side-effects or biological properties that limit their biological utility”
• “where a small number of ligands have been discovered for a target that has not been extensively probed and augmentation of the set is a primary goal of a medicinal chemistry effort”
Filler for Surflex Similarity Function