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