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CDD models case study #3


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A Case study for CDD Models using a kinase dataset in a secure CDD Vault

Published in: Science
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CDD models case study #3

  2. 2. MoDELS RESIDE IN PAPERS NOT ACCESSIBLE…THIS IS UNDESIRABLE How do we share them? How do we use Them?
  3. 3. What if we could build Machine Learning Models in the CDD Vault We could then use them to score public or private libraries in the Vault We can leverage models from other companies or groups to help internal projects We can export models to use in other software We can develop our own private database of models Deliverable: This Case Study walks you through building a model with a dataset in CDD Public and generating predictions in a CDD Vault
  4. 4. Open Extended Connectivity Fingerprints ECFP_6 FCFP_6 • Collected, deduplicated, hashed • Sparse integers • Invented for Pipeline Pilot: public method, proprietary details • Often used with Bayesian models: many published papers • Built a new implementation: open source, Java, CDK – stable: fingerprints don't change with each new toolkit release – well defined: easy to document precise steps – easy to port: already migrated to iOS (Objective-C) for TB Mobile app • Provides core basis feature for CDD open source model service Clark et al., J Cheminform 6:38 2014
  5. 5. Using Kinase data for CDD Models
  6. 6. The Kinase data is present in CDD Public
  7. 7. Select actives – >80% inhibition for EPHB1 = active
  8. 8. Build Model
  9. 9. Model name
  10. 10. Model name
  11. 11. Model ROC PLOT
  12. 12. Predictions for some approved drugs in Vault with Model – select compounds
  13. 13. Predictions for some approved drugs in Vault with Model – select model from protocol section
  14. 14. Predictions for some approved drugs in Vault with Model – output You can rank molecules by these scores
  15. 15. You can create a private database of CDD Models in your CDD Vault
  16. 16. You can also export your CDD Model Search under protocols tab
  17. 17. Clark et al., JCIM 55: 1231-1245 (2015) Exporting models from CDD
  18. 18. Clark et al., JCIM 55: 1231-1245 (2015)9R44TR000942-02 You can import your model in a mobile app like MMDS for private use of the model or sharing with a collaborator
  19. 19. Find out more about Clark AM, Dole K, Coulon-Spektor A, McNutt A, Grass G, Freundlich JS, Reynolds RC and Ekins S, Open Source Bayesian Models: 1. Application to ADME/Tox and Drug Discovery Datasets, J Chem Inf Model, 55(6):1231-45, 2015 Clark AM, and Ekins S Open Source Bayesian Models: 2. Mining a “Big Dataset” to Create and Validate Models with ChEMBL, J Chem Inf Model, 55(6):1246-60, 2015. Ekins S, Clark, AM and Wright SH, Making transporter models for drug-drug interaction prediction mobile, Drug Metab Dispos, 43:1642-5, 2015 Clark AM, Dole K and Ekins S, Open Source Bayesian Models: 3. Composite Models for prediction of binned responses, 56: 275-85, 2016. Perryman AL, Stratton TP, Ekins S and Freundlich, Predicting mouse liver microsomal stability with "pruned' machine learning models and public data, Pharm Res, 33: 433-49, 2016. ntact Sales: (650) 242-5259