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PharmaMiner : Geometric Mining of Pharmacophores

PharmaMiner : Geometric Mining of Pharmacophores. PharmaMiner. Analysis of 3D arrangements of pharmacophoric features in compound collections Flexible definition of features of interest (donor, acceptor, hydrophobic core, etc.) Classification and clustering of arrangements .

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PharmaMiner : Geometric Mining of Pharmacophores

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  1. PharmaMiner: Geometric Mining of Pharmacophores

  2. PharmaMiner Analysis of 3D arrangements of pharmacophoric features in compound collections Flexible definition of features of interest (donor, acceptor, hydrophobic core, etc.) Classification and clustering of arrangements

  3. Applications of PharmaMiner • What makes compounds active against a target? • Develop pharmacophore models based on results • What features impart specific biological activity (e.g., BBB permeability)? • Screen compounds for specific arrangements • Query for combination of properties and arrangements • Diversity analysis in 3D space • Scaffold hopping

  4. Key Benefits • Only automatic tool that analyzes the 3D space of pharmacophores • Identification of activity against a target based on a set of actives and inactives (clustering) • Unique exploration of the pharmacophore space through biological activity (classification) • Unique querying for proximity and distance from clusters • Specification of toxicity, absorption, side-effects

  5. Validation Studies Activity prediction of cancer datasets

  6. Pharmacophoric Features: Cancer Datasets • Cations • Anions • Hydrogen Bond Donors • Hydrogen Bond Acceptors • Hydrophobic Centers • Aromatic Rings

  7. Results: Cancer Datasets • Many clusters show significant behavior • Examples of some positive clusters

  8. Results: Cancer Datasets • Clusters involving anions show strong negative significance:  • However, typically Acceptor-Acceptor-Anion clusters also contained a positive cluster indicating a certain structure that occurs frequently in active molecules

  9. Prediction Pipeline Extract all pharmacophoric triangles K-medoid clustering Significant cluster centers Molecular DB Triangles Classification Model Feature Vector Vector representation of molecules Result SVM with MinMax kernel

  10. Complementary to GraphSig • Pharmacophoric atoms vs. atom types • E.g., N-O-N and N-O-O get clustered together due to similar pharmacophoric type • Graph based approach allows more flexibility and computational efficiency • 3D analysis has better accuracy • Possible to combine the two techniques

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