This case study demonstrates how to build a machine learning model using data from the Collaborative Drug Discovery (CDD) Public vault and apply the model to score compounds in a private CDD vault. Specifically, it shows how to: 1. Select active compounds from AZ-ChEMBL data in the public vault to train a model. 2. Build a model using the selected active compounds. 3. Generate predictions for approved drugs in a private vault using the new model. 4. Export the model for use in other software or share it with collaborators. The goal is to illustrate how models can be developed in CDD and leveraged across projects and groups to help drug discovery

1. CASE STUDY #1
SEAN EKINS
COLLABORATIVE DRUG DISCOVERY, 1633
BAYSHORE HIGHWAY, SUITE 342, BURLINGAME, CA
94010, USA

2. MoDELS RESIDE IN PAPERS
NOT ACCESSIBLE…THIS IS
UNDESIRABLE
How do we share them?
How do we use Them?

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. 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. Using AZ-ChEMBL data for CDD Models
https://www.ebi.ac.uk/chembl/doc/inspect/CHEMBL3301361

6. The AZ data is also present in CDD Public

7. Select actives – you want low values to equal GOOD

8. Build Model

9. Model name

10. Model built

11. Model ROC PLOT

12. Predictions for some approved drugs in Vault with Model – select compo

13. Predictions for some approved drugs in Vault with Model –
select model from protocol section

14. Predictions for some approved drugs in Vault with Model – outp
You can rank molecules by these scores

15. You can create a private database of CDD Models in your CDD Vault

16. You can also export your CDD Model

17. Clark et al., JCIM 55: 1231-1245 (2015)
Exporting models from CDD

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. 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.
https://www.collaborativedrug.com/pages/co
ntact
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