ligand-based structural hypotheses for virtual screening

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Ligand-Based Structural Hypotheses for Virtual Screening Ajay N. Jain Uses the tool described in the pervious paper

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Ligand-Based Structural Hypotheses for Virtual Screening. Ajay N. Jain Uses the tool described in the pervious paper. Agenda. - PowerPoint PPT Presentation

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Page 1: 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

Page 2: Ligand-Based Structural Hypotheses for Virtual Screening

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”

Page 3: Ligand-Based Structural Hypotheses for Virtual Screening

Molecules Used:Chose 4 therapeutically interesting targets with

unknown three-dimensional structure, and identified ligands known to associate with those:

Page 4: Ligand-Based Structural Hypotheses for Virtual Screening

Positive Testing Set:

Page 5: Ligand-Based Structural Hypotheses for Virtual Screening

Control Test Sets

Estrogen Receptor Ligands

HSV-1 Thymidine kinase inhibitors

Page 6: Ligand-Based Structural Hypotheses for Virtual Screening

Control Test Sets Alignments

Page 7: Ligand-Based Structural Hypotheses for Virtual Screening

GPCR Models

Page 8: Ligand-Based Structural Hypotheses for Virtual Screening

GABAA Model

Page 9: Ligand-Based Structural Hypotheses for Virtual Screening

ROC Curves

TanimotoSurflex-Sim

TanimotoSurflex-Sim

TanimotoSurflex-Sim

TanimotoSurflex-Sim

Serotonin Model Muscarinic Model

Histamine Model GABAA Model

Page 10: Ligand-Based Structural Hypotheses for Virtual Screening

Examples of High Scoring Ligands

Page 11: Ligand-Based Structural Hypotheses for Virtual Screening

Selectivity of the Models

GABAA vs. GPCRGABAA vs. Random

Musc. vs. NonMusc. vs. Random

Hist. vs. NonHist. vs. Random

Page 12: Ligand-Based Structural Hypotheses for Virtual Screening

Serotonin Model Binding Affinity

Page 13: Ligand-Based Structural Hypotheses for Virtual Screening

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”

Page 14: Ligand-Based Structural Hypotheses for Virtual Screening

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”

Page 15: Ligand-Based Structural Hypotheses for Virtual Screening

Filler for Surflex Similarity Function